Agricultural machine, systems, and methods

Abstract

To provide methods, systems and agricultural machines that can be used to promote automation and unmanned pruning of fruit trees while maintaining fruit yield and quality.SOLUTION: A method, which is performed using one or more computing devices, includes receiving sensor data obtained by one or more sensors for one or more branches of a fruit tree, and determining, based on the sensor data, whether each of the one or more branches is to be removed or to be left.SELECTED DRAWING: Figure 4C

Description

Technical Field

[0001] The present disclosure relates to agricultural machinery, systems, methods, a system for generating cutting point data of fruit tree branches, and a method for generating cutting point data of fruit tree branches.

Background Art

[0002] As next-generation agriculture, research and development of smart agriculture utilizing ICT (Information and Communication Technology) and IoT (Internet of Things) is underway. Research and development towards automation and unmanned operation of work vehicles such as tractors used in fields is also underway. For example, work vehicles that travel with automatic steering using a positioning system such as GNSS (Global Navigation Satellite System) capable of precise positioning have been put into practical use.

[0003] Patent Document 1 describes a work vehicle that can autonomously move between multiple rows of trees in an orchard such as a vineyard.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] There are also demands for automation and unmanned operation in the pruning work of fruit trees in orchards such as vineyards. Pruning is an operation of cutting off a part of the branches of a fruit tree as unnecessary branches in order to shape the fruit tree. The pruning work can be carried out during both the growing period and the dormant period, but in this specification, it mainly refers to the work carried out during the dormant period (for example, winter) after the harvest of the fruits of that year and before the growth of the fruit tree for the next year begins. Since the yield and quality of the next season are determined by which branches are cut off and which branches are left, the pruning work carried out during the dormant period is regarded as one of the important works in fruit tree cultivation. In the pruning work, for each individual fruit tree having different shapes, it is necessary to comprehensively judge the health state, sunlight exposure condition, ventilation condition, etc. of the fruit tree and perform optimal pruning for each fruit tree based on experience and sense. It is not easy to automate the pruning work involving such judgment.

[0006] An object of the present disclosure is to provide a method for generating cutting point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut, a system for generating the cutting point data, and an agricultural machine, which can solve such problems.

Means for Solving the Problems

[0007] A preferred embodiment of the present invention relates to a method for generating cutting point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut, a system for generating cutting point data of a branch of a fruit tree, and an agricultural machine.

[0008] The present disclosure provides the solution means described in the following items.

[0009] [Item a1] A method for generating cutting point data including information indicating the three-dimensional position of the point at which a branch (cane) of a fruit tree should be cut according to a preferred embodiment of the present invention is a method for generating cutting point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut using one or more computing devices, and Sensor data acquired by one or more sensors, based on the sensor data of a plurality of branches of the fruit tree, grouping the plurality of branches into a plurality of groups, Based on the sensor data, determining for each of the one or more branches grouped into the same group whether it is a branch to be removed or a branch to be retained, Generating the cutting point data for each of the branches determined to be branches to be removed A method comprising:

[0010] [Item a2] According to a preferred embodiment of the present invention, Determining whether each of the one or more branches is a branch to be removed or a branch to be retained comprises: Based on the sensor data, obtaining a measured value regarding one or more attributes of each of the one or more branches, Based on the measured value, determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained The method according to item a1, comprising:

[0011] [Item a3] According to a preferred embodiment of the present invention, The one or more attributes include at least one of the color of the branch, the direction in which the branch extends, the thickness of the branch, the height of the base of the branch, the size of the buds on the branch, the direction in which the buds on the branch face, the length of the branch, and the length of the internodes of the branch. The method according to item a2.

[0012] [Item a4] According to a preferred embodiment of the present invention, The method according to any one of items a1 to a3, further comprising inputting the generated cutting point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree.

[0013] [Item a5] According to a preferred embodiment of the present invention, The grouping comprises: The method according to any one of items a1 to a4, including grouping the plurality of branches into the plurality of groups based on the position of the roots of the plurality of branches based on the sensor data.

[0014] [Item a6] According to a preferred embodiment of the present invention, The grouping includes The method according to item a5, including grouping the branches growing from within a predetermined range of the plurality of branches into the same group based on the sensor data.

[0015] [Item a7] According to a preferred embodiment of the present invention, The grouping includes The method according to item a5, including grouping the branches growing from the same short spur among the plurality of branches into the same group based on the sensor data.

[0016] [Item a8] According to a preferred embodiment of the present invention, Determining whether each of the one or more branches is to be a branch to be removed or a branch to be left includes Determining based on the sensor data whether any of the one or more branches includes a branch other than a candidate that should not be selected as a branch to be left, When a branch other than the candidate is included, determining a branch selected from among the branches obtained by removing the branch other than the candidate from the one or more branches as the branch to be left, The method according to any one of items a1 to a7, including.

[0017] [Item a9] According to a preferred embodiment of the present invention, The method according to item a8, further including notifying the user that there is a branch other than the candidate when there is a branch other than the candidate.

[0018] [Item a10] According to a preferred embodiment of the present invention, further comprising obtaining information on the number of buds to be left on each of the branches to be left, The method according to any one of items a1 to a9, further comprising generating the cutting point data for each of the branches determined to be the branches to be left based on the number of buds to be left.

[0019] [Item a11] According to a preferred embodiment of the present invention, obtaining the information on the number of buds to be left The method according to item a10, comprising obtaining information on the number of buds to be left based on a user input.

[0020] [Item a12] According to a preferred embodiment of the present invention, generating the cutting point data for each of the branches determined to be the branches to be left The method according to item a10 or a11, comprising generating the cutting point data such that each of the branches determined to be the branches to be left has one or more buds after being cut.

[0021] [Item a13] According to a preferred embodiment of the present invention, the one or more branches are two or more branches, determining whether each of the one or more branches is to be a branch to be removed or a branch to be left The method according to any one of items a1 to a12, comprising determining, among the two or more branches, a branch other than the branch determined to be the branch to be left as the branch to be removed.

[0022] [Item a14] According to a preferred embodiment of the present invention, the sensor data the images of the plurality of branches obtained by the imaging device, and the estimated depths of the plurality of branches obtained based on the images The method according to any one of items a1 to a13, including

[0023] [Item a15] According to a preferred embodiment of the present invention, The sensor data is The plurality of branch images acquired by the imaging device, and The point cloud data obtained by sensing the plurality of branches by the LiDAR sensor The method according to any one of items a1 to a13, including

[0024] [Item a16] A system for generating cutting point data including information indicating the three-dimensional position of the points to be cut on the branches of a fruit tree according to a preferred embodiment of the present invention One or more sensors for acquiring sensor data of a plurality of branches of the fruit tree, A data processing device for generating the cutting point data of the branches of the fruit tree based on the sensor data Comprising The data processing device Group the plurality of branches into a plurality of groups based on the sensor data, Based on the sensor data, determine for each of the one or more branches grouped into the same group among the plurality of groups whether it is a branch to be removed or a branch to be left, A system for generating the cutting point data for each of the branches determined to be branches to be removed.

[0025] [Item a17] A system for generating cutting point data including information indicating the three-dimensional position of the points to be cut on the branches of a fruit tree according to a preferred embodiment of the present invention One or more sensors for acquiring sensor data of a plurality of branches of the fruit tree, Means for performing the steps of the method according to any one of items a1 to a15 Having a cutting point data generation system.

[0026] [Item a18] According to a preferred embodiment of the present invention, further comprising a cutter for cutting the branches of the fruit tree and a control device for controlling the three-dimensional position of the cutter, the data processing device inputs the generated cutting point data into the control device, the control device controls the three-dimensional position of the cutter based on the cutting point data, the system according to item a16 or a17.

[0027] [Item a19] According to a preferred embodiment of the present invention, an agricultural machine having the system according to item a18.

[0028] [Item a20] According to a preferred embodiment of the present invention, further comprising an arm for supporting the cutter, a support for supporting the arm, and a drive device for moving the support, the control device controls the three-dimensional position of the cutter by controlling the operation of the arm, the agricultural machine according to item a19.

[0029] [Item b1] A method for generating cutting point data including information indicating the three-dimensional position of a point at which a branch of a fruit tree is to be cut according to a preferred embodiment of the present invention is a method for generating cutting point data including information indicating the three-dimensional position of a point at which a branch of a fruit tree is to be cut using one or more computing devices, sensor data acquired by one or more sensors, obtaining measurement values regarding two or more attributes for each of the one or more branches based on the sensor data of the one or more branches of the fruit tree, obtaining information on the priority of the two or more attributes, determining, based on the measurement values and the priority, whether each of the one or more branches is to be a branch to be removed or a branch to be left, generating the cutting point data for each of the branches determined to be branches to be removed A method including

[0030] [Item b2] According to a preferred embodiment of the present invention, The method according to item b1, further comprising inputting the generated cut point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree.

[0031] [Item b3] According to a preferred embodiment of the present invention, Determining whether each of the one or more branches is to be a branch to be removed or a branch to be left includes: For each of the one or more branches, determining a factor score based on the measured values for each of the two or more attributes; For each of the one or more branches, calculating a total score based on the factor score for each of the two or more attributes and the priority of the attribute; Based on the total score, determining whether each of the one or more branches is to be a branch to be removed or a branch to be left The method according to item b1 or b2, including

[0032] [Item b4] According to a preferred embodiment of the present invention, Calculating the total score includes: The method according to item b3, including calculating the total score based on values obtained by correcting the factor scores for each of the two or more attributes for each of the one or more branches according to the priority of the attribute.

[0033] [Item b5] According to a preferred embodiment of the present invention, Calculating the total score includes: The method according to item b3 or b4, including calculating the total score by adding up the values obtained by multiplying the factor scores for each of the above-mentioned two or more attributes by the priority weights corresponding to the priorities of the respective attributes for each of the above-mentioned branches with one or more branches.

[0034] [Item b6] According to a preferred embodiment of the present invention, the above-mentioned one or more branches are two or more branches, determining whether each of the above-mentioned one or more branches is a branch to be removed or a branch to be retained includes determining, as the branch to be retained, the branch among the above-mentioned two or more branches with the highest total score, according to the method described in any one of items b3 to b5.

[0035] [Item b7] According to a preferred embodiment of the present invention, the above-mentioned one or more branches are two or more branches, determining whether each of the above-mentioned one or more branches is a branch to be removed or a branch to be retained when there are multiple branches having the highest total score among the above-mentioned two or more branches, includes determining, as the branch to be retained, the branch among them with the highest factor score regarding the attribute with the highest priority, according to the method described in any one of items b3 to b6.

[0036] [Item b8] According to a preferred embodiment of the present invention, the above-mentioned one or more branches are two or more branches, determining whether each of the above-mentioned one or more branches is a branch to be removed or a branch to be retained includes determining, as the branches to be retained, the branch with the highest total score and the second-highest branch among the above-mentioned two or more branches, according to the method described in any one of items b3 to b6.

[0037] [Item b9] According to a preferred embodiment of the present invention, The above-mentioned one or more branches are two or more branches, Determining the factor score includes determining the factor score such that the two or more branches have different factor scores for each of the two or more attributes, according to any one of items b3 to b8.

[0038] [Item b10] According to a preferred embodiment of the present invention, Determining whether each of the one or more branches is to be a branch to be removed or a branch to be left When all of the total scores of the one or more branches are lower than a predetermined value, the method according to any one of items b3 to b9, including making the branch to be left be one branch.

[0039] [Item b11] According to a preferred embodiment of the present invention, further includes obtaining information on the number of buds to be left on each of the branches to be left, When all of the total scores of the one or more branches are lower than a predetermined value, further includes generating the cutting point data of the branch determined to be the branch to be left such that the number of buds remaining on the branch determined to be the branch to be left is less than the obtained number of buds to be left, according to the method described in item b10.

[0040] [Item b12] According to a preferred embodiment of the present invention, Obtaining the priority information includes obtaining the priority information based on a user input, according to any one of items b1 to b11.

[0041] [Item b13] According to a preferred embodiment of the present invention, further includes grouping the plurality of branches into a plurality of groups based on sensor data of the plurality of branches of the fruit tree, For one or more branches grouped into the same group among the plurality of groups, obtaining the measurement value, determining whether to be the branch to be removed or the branch to be left, and generating the cut point data are executed, the method according to any one of items b1 to b12.

[0042] [Item b14] According to a preferred embodiment of the present invention, further includes obtaining information on the number of buds to be left for each of the branches to be left, further includes generating the cut point data for each of the branches determined to be the branches to be left based on the number of buds to be left, the method according to any one of items b1 to b13.

[0043] [Item b15] According to a preferred embodiment of the present invention, generating the cut point data for each of the branches determined to be the branches to be left is generating the cut point data such that each of the branches determined to be the branches to be left has one or more buds after being cut, the method according to item b14.

[0044] [Item b16] According to a preferred embodiment of the present invention, the two or more attributes include at least one of the color of the branch, the direction in which the branch extends, the thickness of the branch, the height of the base of the branch, the size of the buds of the branch, the direction in which the buds of the branch face, the length of the branch, and the length of the internodes of the branch, the method according to any one of items b1 to b15.

[0045] [Item b17] According to a preferred embodiment of the present invention, the one or more branches are two or more branches, determining whether each of the one or more branches is to be the branch to be removed or the branch to be left is The method according to any one of items b1 to b16, including determining, among the two or more branches, a branch other than the branch determined to be the branch to be retained as the branch to be removed.

[0046] [Item b18] A system for generating cutting point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut, according to a preferred embodiment of the present invention, One or more sensors for acquiring sensor data of one or more branches of the fruit tree, A data processing device for generating the cutting point data of the branches of the fruit tree based on the sensor data comprising The data processing device Based on the sensor data, for each of the one or more branches, obtains measurement values regarding two or more attributes, Obtains information on the priorities of the two or more attributes, Based on the measurement values and the priorities, determines for each of the one or more branches whether it is to be a branch to be removed or a branch to be retained, A system that generates the cutting point data for each of the branches determined to be the branches to be removed.

[0047] [Item b19] A system for generating cutting point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut, according to a preferred embodiment of the present invention, One or more sensors for acquiring sensor data of a plurality of branches of the fruit tree, Means for executing the steps of the method according to any one of items b1 to b17 A cutting point data generation system having

[0048] [Item b20] According to a preferred embodiment of the present invention, Further includes a cutter for cutting the branches of the fruit tree and a control device for controlling the three-dimensional position of the cutter, The data processing device inputs the generated cutting point data to the control device, The control device controls the three-dimensional position of the cutter based on the cutting point data, for the system described in item b18 or b19.

[0049] [Item b21] According to a preferred embodiment of the present invention, An agricultural machine having the system described in item b20.

[0050] [Item b22] According to a preferred embodiment of the present invention, The agricultural machine further includes an arm that supports the cutter, a support that supports the arm, and a drive device that moves the support, The control device controls the three-dimensional position of the cutter by controlling the operation of the arm, for the agricultural machine described in item b21.

[0051] [Item c1] A method for generating cutting point data including information indicating the three-dimensional position of the point to be cut on a fruit tree branch according to a preferred embodiment of the present invention is a method for generating cutting point data including information indicating the three-dimensional position of the point to be cut on a fruit tree branch using one or more computing devices, Sensor data acquired by one or more sensors, and based on the sensor data of two or more branches of the fruit tree, obtaining measurement values regarding one or more attributes for each of the two or more branches, Based on the measurement values, determining for each of the two or more branches whether it is a branch to be removed or a branch to be left, Generating the cutting point data for each of the branches determined to be branches to be removed including Determining for each of the two or more branches whether it is a branch to be removed or a branch to be left For each of the one or more attributes, classifying each of the two or more branches into one of a plurality of classes indicating evaluation criteria for the attribute based on the measurement values, For each of the above-mentioned attributes of 1 or more, based on the measured values, assign different ranks to the two or more branches. Based on the class and the rank of the two or more branches, determine for each of the two or more branches whether it is a branch to be removed or a branch to be retained. A method comprising the above.

[0052] [Item c2] According to a preferred embodiment of the present invention, The method according to item c1, further comprising inputting the generated cut point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree.

[0053] [Item c3] According to a preferred embodiment of the present invention, Assigning different ranks to the two or more branches For each of the above-mentioned attributes of 1 or more, when the two or more branches are classified into different classes from each other, based on the classified classes, assign different ranks to the two or more branches. The method according to item c1 or c2.

[0054] [Item c4] According to a preferred embodiment of the present invention, Assigning different ranks to the two or more branches For each of the above-mentioned attributes of 1 or more, when there are two or more branches classified into the same class among the plurality of classes, perform a relative evaluation of those branches. Based on the classified classes and the results of the relative evaluation, assign different ranks to the two or more branches. The method according to any one of items c1 to c3, comprising the above.

[0055] [Item c5] According to a preferred embodiment of the present invention, Determining for each of the two or more branches whether it is a branch to be removed or a branch to be retained For each of the above-mentioned attributes of 1 or more, assign any one of a plurality of scores corresponding to the plurality of classes to each of the above-mentioned 2 or more branches. For each of the above-mentioned 2 or more branches, calculate a factor score by multiplying the score by a coefficient according to the result of the relative evaluation for each of the above-mentioned attributes of 1 or more. Based on the factor scores for each of the above-mentioned attributes of 1 or more, determine for each of the above-mentioned 2 or more branches whether it is a branch to be removed or a branch to be retained. The method according to item c4, comprising the above.

[0056] [Item c6] According to a preferred embodiment of the present invention, The above-mentioned attributes of 1 or more are 2 or more attributes. Calculating the factor score The method according to item c5, comprising calculating the factor score by multiplying the score, which is normalized so that the maximum value of the plurality of scores is equal among the above-mentioned 2 or more attributes, by the coefficient.

[0057] [Item c7] According to a preferred embodiment of the present invention, Further comprising obtaining information on the priorities of the above-mentioned 2 or more attributes. Determining for each of the above-mentioned 2 or more branches whether it is a branch to be removed or a branch to be retained The method according to item c6, comprising determining for each of the above-mentioned 2 or more branches whether it is a branch to be removed or a branch to be retained based on the measured value and the priority.

[0058] [Item c8] According to a preferred embodiment of the present invention, Determining for each of the above-mentioned 2 or more branches whether it is a branch to be removed or a branch to be retained For each of the two or more branches, calculate the total score by adding up the values obtained by multiplying the factor scores for each of the two or more attributes by the priority weights corresponding to the priorities of the respective attributes; Based on the total score, determine for each of the two or more branches whether it is to be a branch to be removed or a branch to be retained; The method according to item c7, comprising:

[0059] [Item c9] According to a preferred embodiment of the present invention, Determining for each of the two or more branches whether it is to be a branch to be removed or a branch to be retained includes determining, as the branch to be retained, the branch among the two or more branches that has the highest total score, the method according to item c8;

[0060] [Item c10] According to a preferred embodiment of the present invention, Determining for each of the two or more branches whether it is to be a branch to be removed or a branch to be retained includes, when there are two or more branches having the highest total score among the two or more branches, determining, as the branch to be retained, the branch among them that has the highest factor score for the attribute with the highest priority, the method according to item c8 or c9;

[0061] [Item c11] According to a preferred embodiment of the present invention, Determining for each of the two or more branches whether it is to be a branch to be removed or a branch to be retained includes determining, as the branches to be retained, the branch having the highest total score and the branch having the second highest total score among the two or more branches, the method according to item c8 or c9;

[0062] [Item c12] According to a preferred embodiment of the present invention, Further comprising grouping the plurality of branches into a plurality of groups based on sensor data of the plurality of branches of the fruit tree, For two or more branches grouped into the same group among the plurality of groups, obtaining the measured value, determining whether to be the branch to be removed or the branch to be left, and generating the cut point data are executed, The method according to any one of items c1 to c11.

[0063] [Item c13] According to a preferred embodiment of the present invention, Further comprising obtaining information on the number of buds to be left on the branches to be left, The method according to any one of items c1 to c12, further comprising generating the cut point data of the branches determined to be the branches to be left based on the number of buds to be left.

[0064] [Item c14] According to a preferred embodiment of the present invention, Generating the cut point data for each of the branches determined to be the branches to be left is The method according to item c13, including generating the cut point data so that each of the branches determined to be the branches to be left has one or more buds after being cut.

[0065] [Item c15] According to a preferred embodiment of the present invention, The at least one of the one or more attributes includes at least one of the color of the branch, the direction in which the branch extends, the thickness of the branch, the height of the base of the branch, the size of the buds on the branch, the direction in which the buds on the branch face, the length of the branch, and the length of the internodes of the branch, The method according to any one of items c1 to c14.

[0066] [Item c16] According to a preferred embodiment of the present invention, Determining whether each of the two or more branches is to be the branch to be removed or the branch to be left is The method according to any one of items c1 to c15, including determining, among the two or more branches, a branch other than the branch determined to be the branch to be left as the branch to be removed.

[0067] [Item c17] A system for generating cutting point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut, according to a preferred embodiment of the present invention, One or more sensors for acquiring sensor data of two or more branches of the fruit tree, A data processing device for generating the cutting point data of the branches of the fruit tree based on the sensor data and comprising The data processing device acquires measurement values regarding one or more attributes of each of the two or more branches based on the sensor data, determines, based on the measurement values, whether each of the two or more branches is to be a branch to be removed or a branch to be left, generates the cutting point data for each of the branches determined to be the branches to be removed, The determination of whether a branch is to be removed or left includes classifying each of the two or more branches into one of a plurality of classes indicating evaluation criteria for the attribute based on the measurement values for each of the one or more attributes, and assigning different ranks to the two or more branches for each of the one or more attributes A system.

[0068] [Item c18] A system for generating cutting point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut, according to a preferred embodiment of the present invention, One or more sensors for acquiring sensor data of a plurality of branches of the fruit tree, means for executing the steps of the method according to any one of items c1 to c16 A cutting point data generation system having

[0069] [Item c19] According to a preferred embodiment of the present invention, further comprising a cutter for cutting the branches of the fruit tree and a control device for controlling the three-dimensional position of the cutter, the data processing device inputs the generated cutting point data into the control device, the control device controls the three-dimensional position of the cutter based on the cutting point data, the system according to item c17 or c18.

[0070] [Item c20] According to a preferred embodiment of the present invention, an agricultural machine having the system according to item c19.

[0071] [Item c21] According to a preferred embodiment of the present invention, further comprising an arm for supporting the cutter, a support for supporting the arm, and a drive device for moving the support, the control device controls the three-dimensional position of the cutter by controlling the operation of the arm, the agricultural machine according to item c20.

[0072] [Item d1] A method for generating cutting point data including information indicating the three-dimensional position of a point at which a branch of a fruit tree should be cut according to a preferred embodiment of the present invention is a method for generating cutting point data including information indicating the three-dimensional position of a point at which a branch of a fruit tree should be cut using one or more computing devices, acquiring information on the cultivation method of the fruit tree, acquiring measurement values regarding one or more attributes, each of the one or more attributes including attributes having different evaluation criteria according to the cultivation method, based on sensor data acquired by one or more sensors and including information indicating the three-dimensional structure of one or more branches of the fruit tree, determining, based on the measurement values, for each of the one or more branches whether to be a branch to be removed or a branch to be left, generating the cutting point data for each of the branches determined to be the branches to be removed A method comprising the above.

[0073] [Item d2] According to a preferred embodiment of the present invention, the method according to item d1, wherein the information on the cultivation method includes information on at least one of the shape of the trellis system of the fruit tree, the pruning method of the fruit tree, and the training method of the fruit tree.

[0074] [Item d3] According to a preferred embodiment of the present invention, The method according to item d1 or d2, further comprising inputting the generated cutting point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree.

[0075] [Item d4] According to a preferred embodiment of the present invention, acquiring the information on the cultivation method includes acquiring the information on the cultivation method based on the input of the user, the method according to any one of items d1 to d3.

[0076] [Item d5] According to a preferred embodiment of the present invention, acquiring the information on the cultivation method includes acquiring the information on the cultivation method based on the sensor data of the fruit tree, the method according to any one of items d1 to d3.

[0077] [Item d6] According to a preferred embodiment of the present invention, acquiring the information on the cultivation method includes acquiring the information on the cultivation method based on the image of the fruit tree acquired by an imaging device, the method according to item d5.

[0078] [Item d7] According to a preferred embodiment of the present invention, The method according to any one of Items d1 to d6, wherein the attributes having different evaluation criteria according to the cultivation method include at least one of the direction in which the branch extends, the height of the base of the branch, and the direction in which the buds of the branch face.

[0079] [Item d8] According to a preferred embodiment of the present invention, Determining whether each of the one or more branches is a branch to be removed or a branch to be left includes: For each of the one or more branches, determining a factor score based on the measured values for each of the one or more attributes; Based on the factor score, determining whether each of the one or more branches is a branch to be removed or a branch to be left and The method according to any one of Items d1 to d7, wherein the factor score is determined to be different according to the cultivation method.

[0080] [Item d9] According to a preferred embodiment of the present invention, The attribute having different evaluation criteria according to the cultivation method includes the direction in which the branch extends, Determining the factor score includes: The method according to Item d8, wherein for each of the one or more branches, the factor score is determined based on the inclination angle of the branch with respect to the direction opposite to the gravitational direction and the azimuth angle of the branch in the horizontal plane orthogonal to the gravitational direction.

[0081] [Item d10] According to a preferred embodiment of the present invention, The factor score of each of the one or more branches regarding the direction in which the branch extends is The method according to item d9, wherein when the shape of the trellis system in the cultivation method is VSP (vertical shoot position), the inclination angle of the branch is determined to be higher as it is smaller.

[0082] [Item d11] According to a preferred embodiment of the present invention, Attributes with different evaluation criteria according to the cultivation method include the height at the base of the branch, Determining the factor score The method according to item d8, which includes determining the factor score for each of the one or more branches based on the height of the base of the branch from the main branch when the pruning method in the cultivation method is short - shoot pruning.

[0083] [Item d12] According to a preferred embodiment of the present invention, For each of the one or more branches regarding the height at the base of the branch, when the shape of the trellis system in the cultivation method is VSP (vertical shoot position) and the pruning method in the cultivation method is short - shoot pruning, when the height of the base of the branch from the main branch is smaller than a predetermined range, it is lower than when the height of the base of the branch from the main branch is within the predetermined range, when the height of the base of the branch from the main branch is larger than the predetermined range, it is determined to be lower than when the height of the base of the branch from the main branch is smaller than the predetermined range, according to the method described in item d11.

[0084] [Item d13] According to a preferred embodiment of the present invention, Attributes with different evaluation criteria according to the cultivation method include the height at the base of the branch, Determining the factor score For each of the branches having a length of 1 or more, when the pruning method in the cultivation method is long shoot pruning, the method according to item d8, including determining the factor score based on the height from the root stock of the branch.

[0085] [Item d14] According to a preferred embodiment of the present invention, For each of the one or more branches regarding the height of the root of the branch, when the shape of the trellis system in the cultivation method is VSP (vertical shoot position) and the pruning method in the cultivation method is long shoot pruning, when the height from the root stock of the branch is smaller than a predetermined range, it is lower than when the height from the root stock of the branch is within the predetermined range, when the height from the root stock of the branch is larger than the predetermined range, it is determined to be lower than when the height from the root stock of the branch is smaller than the predetermined range, the method according to item d13.

[0086] [Item d15] According to a preferred embodiment of the present invention, The attributes for which the evaluation criteria differ according to the cultivation method include the direction in which the buds of the branch face, Determining the factor score For each of the one or more branches, the method according to item d8, including determining the factor score based on the direction in which the buds of the branch face.

[0087] [Item d16] According to a preferred embodiment of the present invention, further including obtaining information on the number of buds to be left on each of the branches to be left, Determining the factor score For each of the one or more branches, the method according to item d15, including determining the factor score based on the average value of the directions in which the number of buds to be left among the buds of the branch face.

[0088] [Item d17] According to a preferred embodiment of the present invention, for each of the one or more branches regarding the direction in which the buds of the branch face, when the shape of the trellis system in the cultivation method is VSP (vertical shoot position), the method according to item d16, which is determined such that when the average value of the directions in which the buds of the remaining buds of the branch face is upward with respect to the horizontal plane, it is higher than when the average value of the directions in which the buds of the remaining buds of the branch face is downward with respect to the horizontal plane.

[0089] [Item d18] According to a preferred embodiment of the present invention, the one or more attributes are two or more attributes, the method according to any one of items d1 to d17, wherein the two or more attributes further include attributes whose evaluation criteria do not change depending on the cultivation method.

[0090] [Item d19] According to a preferred embodiment of the present invention, further including obtaining information on the priority of the two or more attributes, determining whether each of the one or more branches is to be a branch to be removed or a branch to be left includes determining whether each of the one or more branches is to be a branch to be removed or a branch to be left based on the measured values and the priority, the method according to item d18.

[0091] [Item d20] According to a preferred embodiment of the present invention, further including grouping the plurality of branches into a plurality of groups based on sensor data of the plurality of branches of the fruit tree, For one or more branches grouped in the same group among the plurality of groups, obtaining the measurement value, determining whether to make the branch to be removed or the branch to be left, and generating the cutting point data are executed, the method according to any one of items d1 to d17.

[0092] [Item d21] According to a preferred embodiment of the present invention, further includes obtaining information on the number of buds to be left for each of the branches to be left, The method according to any one of items d1 to d20, further including generating the cutting point data for each of the branches to be left based on the number of buds to be left.

[0093] [Item d22] According to a preferred embodiment of the present invention, Generating the cutting point data for each of the branches determined to be the branches to be left is The method according to item d21, including generating the cutting point data so that each of the branches determined to be the branches to be left has one or more buds after being cut.

[0094] [Item d23] According to a preferred embodiment of the present invention, The one or more branches are two or more branches, Determining whether each of the one or more branches is to be the branch to be removed or the branch to be left is The method according to any one of items d1 to d22, including determining the branches other than the branches determined to be the branches to be left among the two or more branches as the branches to be removed.

[0095] [Item d24] A system for generating cutting point data including information indicating the three-dimensional position of the point to be cut of the branches of a fruit tree according to a preferred embodiment of the present invention is one or more sensors for obtaining sensor data including information indicating the three-dimensional structure of one or more branches of the fruit tree, A data processing device that generates the cutting point data of the branches of the fruit tree based on the sensor data comprising the data processing device acquires information on the cultivation method of the fruit tree Based on the sensor data, for each of the one or more branches, measurement values regarding one or more attributes including attributes with different evaluation criteria according to the cultivation method are obtained Based on the measurement values, it is determined for each of the two or more whether to be a branch to be removed or a branch to be left A system that generates the cutting point data for each of the branches determined to be the branches to be removed

[0096] [Item d25] A system for generating cutting point data including information indicating the three-dimensional position of the point to be cut of the branches of a fruit tree according to a preferred embodiment of the present invention One or more sensors that acquire sensor data including information indicating the three-dimensional structure of a plurality of branches of the fruit tree Means for executing the steps of the method according to any one of Items d1 to d23 A cutting point data generation system having

[0097] [Item d26] According to a preferred embodiment of the present invention further comprising a cutter for cutting the branches of the fruit tree and a control device for controlling the three-dimensional position of the cutter The data processing device inputs the generated cutting point data into the control device The control device controls the three-dimensional position of the cutter based on the cutting point data, the system according to Item d24 or d25

[0098] [Item d27] According to a preferred embodiment of the present invention An agricultural machine having the system according to Item d26

[0099] [Item d28] According to a preferred embodiment of the present invention, further comprising an arm for supporting the cutter, a support for supporting the arm, and a drive device for moving the support, The agricultural machine according to item d27, wherein the control device controls the three-dimensional position of the cutter by controlling the operation of the arm.

[0100] [Item e1] A method for generating cutting point data including information indicating the three-dimensional position of the point to be cut on a fruit tree branch according to a preferred embodiment of the present invention is a method for generating cutting point data including information indicating the three-dimensional position of the point to be cut on a fruit tree branch using one or more computing devices, sensor data acquired by one or more sensors, and based on the sensor data of one or more branches of the fruit tree, for each of the one or more branches, obtaining measurement values regarding two or more attributes including attributes regarding buds and attributes other than buds of the branch, determining, based on the measurement values, for each of the one or more branches, whether it is a branch to be removed or a branch to be left, generating the cutting point data for each of the branches determined to be branches to be removed including, method.

[0101] [Item e2] According to a preferred embodiment of the present invention, The method according to item e1, wherein the attributes other than the buds include at least one of the color of the branch, the direction in which the branch extends, the thickness of the branch, the height of the base of the branch, and the length of the branch.

[0102] [Item e3] According to a preferred embodiment of the present invention, The method according to item e1 or e2, wherein the attributes regarding the buds include at least one of the size of the buds on the branch, the direction in which the buds on the branch face, and the internode length.

[0103] [Item e4] According to a preferred embodiment of the present invention, further comprising obtaining information on the priorities of the two or more attributes, determining for each of the one or more branches whether it is to be a branch to be removed or a branch to be retained, The method according to any one of items e1 to e3, comprising determining for each of the one or more branches whether it is to be a branch to be removed or a branch to be retained based on the measured value and the priority.

[0104] [Item e5] According to a preferred embodiment of the present invention, determining for each of the one or more branches whether it is to be a branch to be removed or a branch to be retained, for each of the one or more branches, determining a factor score for each of the two or more attributes, and determining for each of the one or more branches whether it is to be a branch to be removed or a branch to be retained based on the factor score. The method according to item e4, comprising:

[0105] [Item e6] According to a preferred embodiment of the present invention, determining for each of the one or more branches whether it is to be a branch to be removed or a branch to be retained, for each of the one or more branches, calculating a total score by adding values obtained by multiplying the factor score for each of the two or more attributes by a priority weight corresponding to the priority of the attribute, and determining for each of the one or more branches whether it is to be a branch to be removed or a branch to be retained based on the total score. The method according to item e5, comprising:

[0106] [Item e7] According to a preferred embodiment of the present invention, the attributes other than the buds include the length of the branches, determining the factor score, The method according to item e5 or e6, including determining the factor score for each of the branches of 1 or more based on the length of the branch.

[0107] [Item e8] According to a preferred embodiment of the present invention, The factor score for each of the branches of 1 or more regarding the length of the branch is when the length of the branch is longer than a predetermined range, it is lower than when the length of the branch is within the predetermined range, The method according to item e7, determined such that when the length of the branch is shorter than the predetermined range, it is lower than when it is longer than the predetermined range.

[0108] [Item e9] According to a preferred embodiment of the present invention, The method according to item e8, further including generating the cutting point data of the remaining branches such that when the length of the branch determined for the remaining branches is longer than the predetermined range, the length of the remaining branches is below the predetermined range.

[0109] [Item e10] According to a preferred embodiment of the present invention, The method according to item e8 or e9, where the predetermined range includes a distance that is half of the distance between the main trunk of the fruit tree and the main trunk of the fruit tree adjacent to the fruit tree.

[0110] [Item e11] According to a preferred embodiment of the present invention, The method according to any one of items e8 to e10, further including determining the predetermined range based on the sensor data of the fruit tree and the fruit tree adjacent to the fruit tree.

[0111] [Item e12] According to a preferred embodiment of the present invention, The attribute regarding the bud includes the length of the internode of the branch, Determining the factor score is The method according to item e5 or e6, including determining the factor score for each of the branches with 1 or more based on the average value of the distances between adjacent buds of the branch.

[0112] [Item e13] According to a preferred embodiment of the present invention, The factor score for each of the 1 or more branches regarding the internode length of the branch is When the average value of the distances between adjacent buds of the branch is longer than a predetermined range, it is lower than when the average value of the distances between adjacent buds of the branch is within the predetermined range, The method according to item e12, determined such that when the average value of the distances between adjacent buds of the branch is shorter than the predetermined range, it is lower than when it is longer than the predetermined range.

[0113] [Item e14] According to a preferred embodiment of the present invention, The method according to any one of items e1 to e13, further including inputting the generated cutting point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree.

[0114] [Item e15] According to a preferred embodiment of the present invention, Further including grouping the plurality of branches of the fruit tree into a plurality of groups based on sensor data of the plurality of branches of the fruit tree, For 1 or more branches grouped into the same group among the plurality of groups, obtaining the measured value, determining whether to be the branch to be removed or the branch to be left, and generating the cutting point data, the method according to any one of items e1 to e14.

[0115] [Item e16] According to a preferred embodiment of the present invention, Further including obtaining information on the number of buds to be left on each of the branches to be left, The method according to any one of items e1 to e15, further comprising generating the cutting point data for each of the branches determined to be the branches to be left, based on the number of buds to be left.

[0116] [Item e17] According to a preferred embodiment of the present invention, generating the cutting point data for each of the branches determined to be the branches to be left, includes generating the cutting point data such that each of the branches determined to be the branches to be left has one or more buds after being cut, the method according to item e16.

[0117] [Item e18] According to a preferred embodiment of the present invention, the one or more branches are two or more branches, determining whether each of the one or more branches is to be a branch to be removed or a branch to be left, includes determining, among the two or more branches, the branches other than the branches determined to be the branches to be left as the branches to be removed, the method according to any one of items e1 to e17.

[0118] [Item e19] A system for generating cutting point data including information indicating the three-dimensional position of the point to be cut on a branch of a fruit tree according to a preferred embodiment of the present invention, one or more sensors for acquiring sensor data of one or more branches of the fruit tree, a data processing device for generating the cutting point data of the branches of the fruit tree based on the sensor data and comprising the data processing device acquires measurement values regarding two or more attributes including an attribute regarding a bud and an attribute other than a bud for each of the one or more branches based on the sensor data, determines whether each of the one or more branches is to be a branch to be removed or a branch to be left based on the measurement values, A system that generates the cutting point data for each of the branches determined to be the branches to be removed.

[0119] [Item e20] A system for generating cutting point data including information indicating the three-dimensional position of the points to be cut on the branches of a fruit tree according to a preferred embodiment of the present invention, one or more sensors that acquire sensor data of a plurality of branches of the fruit tree, means for executing the steps of the method according to any one of Items e1 to e18 and having a cutting point data generation system.

[0120] [Item e21] According to a preferred embodiment of the present invention, further comprising a cutter for cutting the branches of the fruit tree and a control device for controlling the three-dimensional position of the cutter, the data processing device inputs the generated cutting point data to the control device, the control device controls the three-dimensional position of the cutter based on the cutting point data, the system according to Item e19 or e20.

[0121] [Item e22] According to a preferred embodiment of the present invention, an agricultural machine having the system according to Item e21.

[0122] [Item e23] According to a preferred embodiment of the present invention, further comprising an arm that supports the cutter, a support that supports the arm, and a drive device that moves the support, the control device controls the three-dimensional position of the cutter by controlling the operation of the arm, the agricultural machine according to Item e22.

[0123] [Item f1] A method for generating cutting point data including information on the three-dimensional position of the point to be cut on a fruit tree branch according to a preferred embodiment of the present invention is a method for generating cutting point data including information on the three-dimensional position of the point to be cut on a fruit tree branch using one or more computing devices, Based on sensor data acquired by one or more sensors and including information indicating the three-dimensional structure of one or more branches of the fruit tree, for each of the one or more branches, measuring values regarding two or more attributes including an attribute whose evaluation criteria vary according to the cultivation method of the fruit tree and an attribute whose evaluation criteria do not change according to the cultivation method of the fruit tree are obtained, Based on the measured values, it is determined for each of the one or more branches whether it is a branch to be removed or a branch to be left, For each of the branches determined to be branches to be removed, the cutting point data is generated, A method including:

[0124] [Item f2] According to a preferred embodiment of the present invention, The method according to item f1, wherein the attribute whose evaluation criteria vary according to the cultivation method includes at least one of the direction in which the branch extends, the height of the base of the branch, and the direction in which the buds on the branch face.

[0125] [Item f3] According to a preferred embodiment of the present invention, The method according to item f1 or f2, wherein the attribute whose evaluation criteria do not change according to the cultivation method includes at least one of the color of the branch, the thickness of the branch, and the size of the buds on the branch.

[0126] [Item f4] According to a preferred embodiment of the present invention, Determining for each of the one or more branches whether it is a branch to be removed or a branch to be left For each of the one or more branches, determining a factor score for each of the two or more attributes, Based on the factor scores, determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained including the factor scores for attributes whose evaluation criteria vary according to the cultivation method are determined to vary according to the cultivation method, the factor scores for attributes whose evaluation criteria do not change with the cultivation method are determined not to vary with the cultivation method, the method according to any one of items f1 to f3.

[0127] [Item f5] According to a preferred embodiment of the present invention, further including obtaining information on the priorities of the two or more attributes, determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained includes determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained based on the factor scores and the priorities, the method according to item f4.

[0128] [Item f6] According to a preferred embodiment of the present invention, determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained for each of the one or more branches, calculating a total score by adding up the values obtained by multiplying the factor scores for each of the two or more attributes by a priority weight corresponding to the priority of the attribute, and determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained based on the total score including the method according to item f5.

[0129] [Item f7] According to a preferred embodiment of the present invention, the attributes whose evaluation criteria do not change with the cultivation method include the color of the branches, Determining, for each of the one or more branches, whether it is the branch to be removed or the branch to be retained involves for each of the one or more branches, determining the factor score based on the color of the branch, and based on the factor score, determining, for each of the one or more branches, whether it is the branch to be removed or the branch to be retained The method according to any one of items f4 to f6, including

[0130] [Item f8] According to a preferred embodiment of the present invention, The method according to item f7, wherein the factor score for each of the one or more branches regarding the color of the branch is determined such that the closer the color of the branch is to brown, the higher the score.

[0131] [Item f9] According to a preferred embodiment of the present invention, Determining, for each of the one or more branches, whether it is the branch to be removed or the branch to be retained involves determining, among the one or more branches, the branches with a factor score regarding the color of the branch lower than a predetermined value as branches outside the candidates that should not be selected as the branches to be retained, and determining the branches to be retained from among the branches obtained by removing the branches outside the candidates from the one or more branches The method according to item f7 or f8, including

[0132] [Item f10] According to a preferred embodiment of the present invention, When there are branches among the one or more branches with a factor score lower than the predetermined value, the method according to item f9 further includes notifying the user that there are branches with a factor score lower than the predetermined value.

[0133] [Item f11] According to a preferred embodiment of the present invention, The method according to any one of items f1 to f10, further comprising obtaining information on the cultivation method of the fruit tree.

[0134] [Item f12] According to a preferred embodiment of the present invention, The method according to any one of items f1 to f11, further comprising inputting the generated cut point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree.

[0135] [Item f13] According to a preferred embodiment of the present invention, The method further comprises grouping the plurality of branches of the fruit tree into a plurality of groups based on sensor data of the plurality of branches, For one or more branches grouped into the same group among the plurality of groups, obtaining the measured value, determining whether to be a branch to be removed or a branch to be left, and generating the cut point data are executed. The method according to any one of items f1 to f12.

[0136] [Item f14] According to a preferred embodiment of the present invention, The method further comprises obtaining information on the number of buds to be left for each of the branches determined to be the branches to be left, The method according to any one of items f1 to f13, further comprising generating the cut point data for each of the branches to be left based on the number of buds to be left.

[0137] [Item f15] According to a preferred embodiment of the present invention, Generating the cut point data for each of the branches determined to be the branches to be left The method according to item f14, comprising generating the cut point data so that each of the branches determined to be the branches to be left has one or more buds after being cut.

[0138] [Item f16] According to a preferred embodiment of the present invention, the one or more branches are two or more branches, determining whether each of the one or more branches is to be a branch to be removed or a branch to be retained includes determining, as branches to be removed, branches other than the branches determined to be branches to be retained among the two or more branches, the method according to any one of items f1 to f15.

[0139] [Item f17] A system for generating cut point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut according to a preferred embodiment of the present invention one or more sensors for acquiring sensor data including information indicating the three-dimensional structure of one or more branches of the fruit tree, a data processing device for generating the cut point data of the branches of the fruit tree based on the sensor data and the data processing device acquires measurement values regarding two or more attributes including attributes whose evaluation criteria vary according to the cultivation method of the fruit tree and attributes whose evaluation criteria do not change according to the cultivation method of the fruit tree for each of the one or more branches based on the sensor data, determines whether each of the one or more branches is to be a branch to be removed or a branch to be retained based on the measurement values, and generates the cut point data for each of the branches determined to be branches to be removed, the system.

[0140] [Item f18] A system for generating cut point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut according to a preferred embodiment of the present invention one or more sensors for acquiring sensor data including information indicating the three-dimensional structure of a plurality of branches of the fruit tree, means for executing the steps of the method according to any one of items f1 to f16 and a cut point data generation system having the same.

[0141] [Item f19] According to a preferred embodiment of the present invention, further comprising a cutter for cutting the branches of the fruit tree and a control device for controlling the three-dimensional position of the cutter, the data processing device inputs the generated cutting point data into the control device, the control device controls the three-dimensional position of the cutter based on the cutting point data, the system according to item f17 or f18.

[0142] [Item f20] According to a preferred embodiment of the present invention, an agricultural machine having the system according to item f19.

[0143] [Item f21] According to a preferred embodiment of the present invention, further comprising an arm for supporting the cutter, a support for supporting the arm, and a drive device for moving the support, the control device controls the three-dimensional position of the cutter by controlling the operation of the arm, the agricultural machine according to item f20.

[0144] [Item g1] A method for generating cutting point data including information indicating the three-dimensional position of the point to be cut on the branch of a fruit tree according to a preferred embodiment of the present invention is a method for generating cutting point data including information indicating the three-dimensional position of the point to be cut on the branch of a fruit tree using one or more computing devices, sensor data acquired by one or more sensors, based on the sensor data of one or more branches of the fruit tree, for each of the one or more branches, obtaining measurement values for one or more attributes including attributes related to the tree vigor of the fruit tree, determining, based on the measurement values, whether each of the one or more branches is to be a branch to be removed or a branch to be left, determining, based on the measurement values, the number of buds to be left on the branches determined to be branches to be left, Generating the cutting point data for each of the branches determined to be the branches to be removed; Generating the cutting point data for each of the branches determined to be the branches to be retained, based on the number of buds to be retained; A method comprising.

[0145] [Item g2] According to a preferred embodiment of the present invention, The method according to item g1, wherein the one or more attributes include at least one of the thickness of the branch, the size of the buds on the branch, and the length of the internodes of the branch.

[0146] [Item g3] According to a preferred embodiment of the present invention, The one or more attributes include the thickness of the branch, Determining whether each of the one or more branches is to be a branch to be removed or a branch to be retained includes: Determining a factor score for each of the one or more branches based on the thickness of the branch; Determining whether each of the one or more branches is to be a branch to be removed or a branch to be retained based on the factor score. The method according to item g2, comprising.

[0147] [Item g4] According to a preferred embodiment of the present invention, The factor score for each of the one or more branches regarding the thickness of the branch is: When the thickness of the branch is greater than a predetermined range, it is lower than when the thickness of the branch is within the predetermined range, When the thickness of the branch is smaller than the predetermined range, it is determined to be lower than when the thickness of the branch is greater than the predetermined range. The method according to item g3.

[0148] [Item g5] According to a preferred embodiment of the present invention, Further comprising obtaining information on a set value of the number of buds to be retained, Determining the number of buds to be left includes the method according to item g4, including determining the number of buds to be left such that when the thickness of the branch determined for the branch to be left is smaller than the predetermined range, the number of buds to be left has a value smaller than the set value.

[0149] [Item g6] According to a preferred embodiment of the present invention, acquiring information on the set value of the number of buds to be left includes the method according to item g5, including acquiring the information on the set value based on a user input.

[0150] [Item g7] According to a preferred embodiment of the present invention, the attributes regarding the buds include the size of the buds that the branches have, determining for each of the one or more branches whether to be a branch to be removed or a branch to be left includes determining a factor score for each of the one or more branches based on the size of the buds that the branch has, and determining for each of the one or more branches whether to be a branch to be removed or a branch to be left based on the factor score The method according to item g2 includes.

[0151] [Item g8] According to a preferred embodiment of the present invention, further including acquiring information on the set value of the number of buds to be left, and determining for each of the one or more branches whether to be a branch to be removed or a branch to be left includes determining the factor score for each of the one or more branches based on the average value of the sizes of the set number of buds among the buds that the branch has, and determining for each of the one or more branches whether to be a branch to be removed or a branch to be left based on the factor score The method according to item g7 includes.

[0152] [Item g9] According to a preferred embodiment of the present invention, acquiring information on the set value of the number of buds to be left includes acquiring the information on the set value based on a user input, the method according to item g8.

[0153] [Item g10] According to a preferred embodiment of the present invention, each of the factor scores of the one or more branches regarding the size of the buds that the branches have when the average value of the size of the buds that the branch has is greater than a predetermined range, is lower than when the average value of the size of the buds that the branch has is within the predetermined range, when the average value of the size of the buds that the branch has is smaller than the predetermined range, is determined to be lower than when the average value of the size of the buds that the branch has is greater than the predetermined range, the method according to item g8 or g9.

[0154] [Item g11] According to a preferred embodiment of the present invention, determining the number of buds to be left when the average value of the size of the buds that the branch determined to be the branch to be left has is greater than the predetermined range, determining the number of buds to be left so that the number of buds to be left has a value greater than the set value, when the average value of the size of the buds that the branch determined to be the branch to be left has is smaller than the predetermined range, includes determining the number of buds to be left so that the number of buds to be left has a value smaller than the set value, the method according to item g10.

[0155] [Item g12] According to a preferred embodiment of the present invention, further includes inputting the generated cutting point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree, the method according to any one of items g1 to g11.

[0156] [Item g13] According to a preferred embodiment of the present invention, further comprising grouping the plurality of branches into a plurality of groups based on sensor data of the plurality of branches of the fruit tree, For one or more branches grouped into the same group among the plurality of groups, obtaining the measured value, determining whether to be a branch to be removed or a branch to be left, determining the number of buds to be left, and generating the cutting point data are executed. The method according to any one of Items g1 to g12.

[0157] [Item g14] According to a preferred embodiment of the present invention, the one or more attributes are two or more attributes, further comprising obtaining information on the priorities of the two or more attributes, Determining whether each of the one or more branches is a branch to be removed or a branch to be left is including determining whether each of the one or more branches is a branch to be removed or a branch to be left based on the measured value and the priority. The method according to any one of Items g1 to g13.

[0158] [Item g15] According to a preferred embodiment of the present invention, further comprising obtaining information on the number of buds to be left for each of the branches determined to be branches to be left, further comprising generating the cutting point data for each of the branches to be left based on the number of buds to be left. The method according to any one of Items g1 to g14.

[0159] [Item g16] According to a preferred embodiment of the present invention, Generating the cutting point data for each of the branches determined to be branches to be left is The method according to item g15, including generating the cutting point data such that each of the branches determined to be the branches to be retained has one or more buds after being cut.

[0160] [Item g17] According to a preferred embodiment of the present invention, the one or more branches are two or more branches, determining whether each of the one or more branches is to be a branch to be removed or a branch to be retained, The method according to any one of items g1 to g16, including determining, among the two or more branches, the branches other than the branches determined to be the branches to be retained as the branches to be removed.

[0161] [Item g18] A system for generating cutting point data including information indicating the three-dimensional position of the points at which the branches of a fruit tree are to be cut according to a preferred embodiment of the present invention, one or more sensors for acquiring sensor data of one or more branches of the fruit tree, a data processing device for generating the cutting point data of the branches of the fruit tree based on the sensor data, comprising, the data processing device, acquiring measurement values regarding one or more attributes including attributes related to the tree vigor of the fruit tree for each of the one or more branches based on the sensor data, determining whether each of the one or more branches is to be a branch to be removed or a branch to be retained based on the measurement values, determining the number of buds to be retained on the branches determined to be the branches to be retained based on the measurement values, generating the cutting point data for each of the branches determined to be the branches to be removed, A system for generating the cutting point data for each of the branches determined to be the branches to be retained based on the number of buds to be retained.

[0162] [Item g19] A system for generating cutting point data including information indicating the three-dimensional position of the point to be cut on a branch of a fruit tree according to a preferred embodiment of the present invention, one or more sensors for acquiring sensor data of a plurality of branches of the fruit tree, and means for executing the steps of the method according to any one of items g1 to g17 A cutting point data generation system having.

[0163] [Item g20] According to a preferred embodiment of the present invention, further comprising a cutter for cutting the branch of the fruit tree and a control device for controlling the three-dimensional position of the cutter, the data processing device inputs the generated cutting point data to the control device, The control device controls the three-dimensional position of the cutter based on the cutting point data, and the system according to item g18 or g19.

[0164] [Item g21] According to a preferred embodiment of the present invention, An agricultural machine having the system according to item g20.

[0165] [Item g22] According to a preferred embodiment of the present invention, further comprising an arm for supporting the cutter, a support for supporting the arm, and a drive device for moving the support, The control device controls the three-dimensional position of the cutter by controlling the operation of the arm, and the agricultural machine according to item g21.

[0166] [Item h1] A method for generating cutting point data including information indicating the three-dimensional position of the point to be cut on a branch of a fruit tree according to a preferred embodiment of the present invention is a method for generating cutting point data including information indicating the three-dimensional position of the point to be cut on a branch of a fruit tree using one or more computing devices, Sensor data acquired by one or more sensors, based on the sensor data of a plurality of branches of the fruit tree, grouping the plurality of branches into a plurality of groups, Based on the sensor data, determining for each of the one or more branches grouped into any of the plurality of groups whether it is a branch to be removed or a branch to be retained, Based on the distribution of buds of the branches determined to be the branches to be retained for each of the plurality of groups, determining for each of the plurality of branches whether it is a branch to be removed or a branch to be retained, Generating the cutting point data for each of the branches determined to be the branches to be removed A method comprising

[0167] [Item h2] According to a preferred embodiment of the present invention, The method according to item h1, wherein the distribution of the buds includes the arrangement density of the buds in a direction along the direction in which the main branches supporting the plurality of branches extend.

[0168] [Item h3] According to a preferred embodiment of the present invention, Determining for each of the plurality of branches whether it is a branch to be removed or a branch to be retained The method according to item h2, including determining the branches to be retained from among the one or more branches grouped into the group located near the region when there is a region where the arrangement density of the buds is partially low among the plurality of branches.

[0169] [Item h4] According to a preferred embodiment of the present invention, The grouping The method according to any one of items h1 to h3, including grouping the plurality of branches into the plurality of groups based on the positions of the roots of the plurality of branches based on the sensor data.

[0170] [Item h5] According to a preferred embodiment of the present invention, the plurality of groups correspond to a plurality of spurs of a main branch that supports the plurality of branches, the grouping includes grouping, based on the sensor data, branches that grow from the same spur among the plurality of spurs of the plurality of branches into the same group, the method according to item h4.

[0171] [Item h6] According to a preferred embodiment of the present invention, the plurality of groups correspond to a plurality of regions arranged along the direction in which the main branch that supports the plurality of branches extends, the grouping includes grouping, based on the sensor data, branches that grow from the same region among the plurality of regions of the plurality of branches into the same group, the method according to item h4.

[0172] [Item h7] According to a preferred embodiment of the present invention, determining whether each of the plurality of branches is to be a branch to be removed or a branch to be left includes, when the plurality of groups include a first group in which branches are not grouped, determining the branch to be left from among the one or more branches grouped in the group adjacent to the first group, the method according to any one of items h1 to h6.

[0173] [Item h8] According to a preferred embodiment of the present invention, determining whether each of the plurality of branches is to be a branch to be removed or a branch to be left includes determining the branch to be left from among the branches that extend in the direction of the first group among the one or more branches grouped in the group adjacent to the first group, the method according to item h7.

[0174] [Item h9] According to a preferred embodiment of the present invention, determining for each of the plurality of branches whether it is a branch to be removed or a branch to be left includes when the plurality of groups includes a second group having no branches to be left, determining a branch to be left from among one or more branches grouped in a group adjacent to the second group, the method according to any one of items h1 to h8.

[0175] [Item h10] According to a preferred embodiment of the present invention, determining for each of the plurality of branches whether it is a branch to be removed or a branch to be left includes determining a branch to be left from among the branches extending in the direction of the second group among one or more branches grouped in a group adjacent to the second group, the method according to item h9.

[0176] [Item h11] According to a preferred embodiment of the present invention, further including obtaining information on the cultivation method of the fruit tree, determining for each of the plurality of branches whether it is a branch to be removed or a branch to be left includes determining for each of the plurality of branches whether it is a branch to be removed or a branch to be left based on the distribution of the buds and the cultivation method, the method according to any one of items h1 to h10.

[0177] [Item h12] According to a preferred embodiment of the present invention, further including obtaining information on the number of buds to be left on each of the branches to be left, determining for each of the plurality of branches whether it is a branch to be removed or a branch to be left includes obtaining information on the distribution of the buds based on the number of buds to be left and the branches to be left, the method according to any one of items h1 to h11.

[0178] [Item h13] According to a preferred embodiment of the present invention, determining which of the one or more branches will be the branch to be removed or the branch to be left depends on acquiring, based on the sensor data, measured values regarding one or more attributes for each of the one or more branches, and determining, based on the measured values, which of the one or more branches will be the branch to be removed or the branch to be left The method according to any one of Items h1 to h12, including

[0179] [Item h14] According to a preferred embodiment of the present invention, The method according to any one of Items h1 to h13, further including inputting the generated cut point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree

[0180] [Item h15] According to a preferred embodiment of the present invention, further including acquiring information on the number of buds to be left on each of the branches determined to be the branches to be left, The method according to any one of Items h1 to h14, further including generating the cut point data for each of the branches to be left based on the number of buds to be left

[0181] [Item h16] According to a preferred embodiment of the present invention, generating the cut point data for each of the branches determined to be the branches to be left involves generating the cut point data such that each of the branches determined to be the branches to be left has one or more buds after being cut. The method according to Item h15

[0182] [Item h17] According to a preferred embodiment of the present invention, the one or more branches are two or more branches, Determining whether each of the branches having 1 or more branches is to be the branch to be removed or the branch to be retained is The method according to any one of items h1 to h16, including determining, as the branches to be removed, the branches other than the branches determined to be the branches to be retained among the two or more branches.

[0183] [Item h18] A system for generating cutting point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut, according to a preferred embodiment of the present invention, One or more sensors for acquiring sensor data of a plurality of branches of the fruit tree, A data processing device that generates the cutting point data of the branches of the fruit tree based on the sensor data and comprising The data processing device groups the plurality of branches into a plurality of groups based on the sensor data, determines one or more branches to be retained from among one or more branches grouped into any of the plurality of groups based on the sensor data, determines one or more other branches to be retained from the plurality of branches based on the distribution of buds of the one or more branches to be retained determined for each of the plurality of groups, A system that generates the cutting point data for each of the branches other than the one or more branches to be retained and the one or more other branches to be retained among the plurality of branches.

[0184] [Item h19] A system for generating cutting point data including information indicating the three-dimensional position of the point at which a branch of a fruit tree should be cut, according to a preferred embodiment of the present invention, One or more sensors for acquiring sensor data of a plurality of branches of the fruit tree, Means for executing the steps of the method according to any one of items h1 to h17 and having a cutting point data generation system.

[0185] [Item h20] According to a preferred embodiment of the present invention, It further includes a cutter for cutting the branches of the fruit tree and a control device for controlling the three-dimensional position of the cutter. The data processing device inputs the generated cutting point data into the control device. The control device controls the three-dimensional position of the cutter based on the cutting point data, and the system according to item h18 or h19.

[0186] [Item h21] According to a preferred embodiment of the present invention, An agricultural machine having the system according to item h20.

[0187] [Item h22] According to a preferred embodiment of the present invention, It further includes an arm for supporting the cutter, a support for supporting the arm, and a driving device for moving the support. The control device controls the three-dimensional position of the cutter by controlling the operation of the arm, and the agricultural machine according to item h21.

[0188] [Item i1] A method performed using one or more computing devices, Receiving sensor data acquired by one or more sensors, the sensor data of one or more branches of a fruit tree, Based on the sensor data, determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained Including, a method.

[0189] [Item i2] According to a preferred embodiment of the present invention, The branches determined to be the branches to be retained include fruiting canes, and the method according to item i1.

[0190] [Item i3] According to a preferred embodiment of the present invention, The method according to item i1 or i2, wherein the determined branches to be left are the branches among the one or more branches on which fruits of the best quality in that season are expected to grow.

[0191] [Item i4] According to a preferred embodiment of the present invention, further comprising generating cutting point data including information indicating the three-dimensional position of the cutting point for each of the determined branches to be left, for each of the determined branches to be left, generating the cutting point data comprises generating the cutting point data such that each of the determined branches to be left has one or more buds after being cut, the method according to any one of items i1 to i3.

[0192] [Item i5] According to a preferred embodiment of the present invention, further comprising generating cutting point data including information indicating the three-dimensional position of the cutting point for each of the determined branches to be removed, for each of the determined branches to be removed, generating the cutting point data comprises generating the cutting point data such that each of the determined branches to be removed has no buds after being cut, the method according to any one of items i1 to i4.

[0193] [Item i6] According to a preferred embodiment of the present invention, further comprising inputting the generated cutting point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree, the method according to item i4 or i5.

[0194] [Item i7] According to a preferred embodiment of the present invention, for each of the one or more branches, obtaining measurement values regarding one or more attributes, Based on the measured values, determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained The method according to any one of items i1 to i6, further comprising this

[0195] [Item i8] According to a preferred embodiment of the present invention The one or more attributes are two or more attributes For each of the one or more branches, obtaining a measured value regarding the one or more attributes is For each of the one or more branches, including obtaining measured values regarding the two or more attributes Determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained is The method according to item i7, including determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained based on the measured values regarding the two or more attributes

[0196] [Item i9] According to a preferred embodiment of the present invention The method according to any one of items i1 to i8, wherein the fruit tree is a grape tree

[0197] [Item i10] According to a preferred embodiment of the present invention Further including obtaining information on the vineyard design of the grape tree Determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained is The method according to item i9, including determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained based on the sensor data and the information on the vineyard design of the grape tree

[0198] [Item i11] According to a preferred embodiment of the present invention The fruit tree is a grapevine tree, further comprising obtaining information on the vineyard design of the grapevine tree, wherein the one or more attributes include attributes with different evaluation criteria according to the information on the vineyard design of the grapevine tree, determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained includes determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained based on the measured values of the one or more attributes and the information on the vineyard design of the grapevine tree, according to the method described in item i7.

[0199] [Item i12] According to a preferred embodiment of the present invention, the one or more branches are two or more branches, determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained includes determining, among the two or more branches, that branches other than the branches determined to be branches to be retained are branches to be removed, according to the method described in any one of items i1 to i11.

[0200] [Item i13] A system according to a preferred embodiment of the present invention comprises one or more sensors for obtaining sensor data of one or more branches of a fruit tree, and a data processing device for determining, based on the sensor data, for each of the one or more branches whether it is a branch to be removed or a branch to be retained A system.

[0201] [Item i14] A system according to a preferred embodiment of the present invention comprises one or more sensors for obtaining sensor data of one or more branches of a fruit tree, and means for executing the steps of the method described in any one of items i1 to i12 A system having.

[0202] [Item i15] According to a preferred embodiment of the present invention, for each of the branches determined to be the branches to be removed, the data processing device generates cutting point data including information indicating the three-dimensional position of the point to be cut, further includes a cutter for cutting the branches of the fruit tree and a control device for controlling the three-dimensional position of the cutter, the data processing device inputs the generated cutting point data to the control device, the control device controls the three-dimensional position of the cutter based on the cutting point data, and the system according to Item i13 or i14.

[0203] [Item i16] According to a preferred embodiment of the present invention, an agricultural machine having the system according to Item i15.

[0204] [Item i17] According to a preferred embodiment of the present invention, further includes an arm for supporting the cutter, a support for supporting the arm, and a drive device for moving the support, the control device controls the three-dimensional position of the cutter by controlling the operation of the arm, and the agricultural machine according to Item i16.

[0205] The comprehensive or specific aspects of the present disclosure can be realized by an apparatus, a system, a method, an integrated circuit, a computer program, or a computer-readable non-transitory storage medium, or any combination thereof. The computer-readable storage medium may include a volatile storage medium or a non-volatile storage medium. The apparatus may be composed of a plurality of apparatuses. When the apparatus is composed of two or more apparatuses, the two or more apparatuses may be arranged in one device or may be separately arranged in two or more separate devices. [Advantages of the Invention]

[0206] According to an embodiment of the present disclosure, there are provided a method for generating cutting point data including information indicating a three-dimensional position of a point at which a branch of a fruit tree should be cut, a system for generating the cutting point data, and an agricultural machine, which can be used to promote automation and unmanned operation of pruning work of fruit trees while maintaining the yield and quality of fruits.

Brief Description of Drawings

[0207]

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DETAILED DESCRIPTION OF THE INVENTION

[0208] Hereinafter, with reference to the drawings, a method for generating cutting point data including information indicating the three-dimensional position of a point at which a branch of a fruit tree should be cut, a system for generating cutting point data, and an agricultural machine according to an embodiment of the present disclosure will be described. Note that portions denoted by the same reference numerals in a plurality of drawings indicate the same or equivalent portions.

[0209] The following embodiments are examples for embodying the technical idea of the present invention, and the present invention is not limited to the following embodiments. Descriptions of the size, material, shape, relative arrangement, etc. of the components are not intended to limit the scope of the present invention thereto, but are intended to be illustrative. The sizes and positional relationships of the members shown in each drawing may be exaggerated for ease of understanding.

[0210] In the present disclosure, "parallel" includes a case where the angle formed by two straight lines, sides, surfaces, etc. is in the range of 0° or more and 5° or less, unless otherwise specified. Also, in the present disclosure, "perpendicular" or "orthogonal" includes a case where the angle formed by two straight lines, sides, surfaces, etc. is in the range of 90° to ±5°, unless otherwise specified. The angle formed by two straight lines, sides, surfaces, etc. has a positive value and does not have a negative value, unless otherwise specified.

[0211] <Cutting System> FIG. 1 shows a front perspective view of a cutting system 1 according to a preferred embodiment of the present invention. As shown in FIG. 1, the cutting system 1 may have a moving body or the like. However, the cutting system 1 can be installed on a cart that can be towed by a moving body or a person, or a cart or moving body that is self-driven or self-propelled.

[0212] As shown in FIG. 1, the cutting system 1 has a base frame 10, side frames 12 and 14, a horizontal frame 16, and a vertical frame 18. The side frames 12 and 14 are installed on the base frame 10, and the side frames 12 and 14 directly support the horizontal frame 16. The vertical frame 18 is installed on the horizontal frame 16. One or more devices such as a camera 20, a robot arm 22, and / or a cutting tool 24 can be installed and supported, for example, on the vertical frame 18 and / or on other frames among the frames 10, 12, 14, or 16.

[0213] The base frame 10 has a base frame motor 26 that allows the side frames 12 and 14 to move along the base frame 10 so that one or more devices can be moved in the depth direction (z-axis shown in FIG. 1). The horizontal frame 16 has a horizontal frame motor 28 that allows the vertical frame 18 to move along the horizontal frame 16 so that one or more devices can be moved in the horizontal direction (x-axis shown in FIG. 1). The vertical frame 18 has a vertical frame motor 30 that allows one or more devices to move in the vertical direction (y-axis shown in FIG. 1) along the vertical frame 18. Each of the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30 can be, for example, a screw motor. The screw motor can provide relatively high accuracy for accurately moving and positioning one or more devices. However, each of the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30 can be any motor that provides, for example, a continuous torque of about 0.2 Nm or more, preferably any motor that provides a continuous torque of about 0.3 Nm or more.

[0214] Each of the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30 can be designed and / or sized according to the total weight of one or more devices. Also, the couplers for each of the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30 can be changed according to the diameter of the motor shaft and / or the corresponding mounting hole pattern.

[0215] The base frame 10 can be installed on the base 32, and the base electronics 34 can also be installed on the base 32. A plurality of wheels 36 can be installed on the base 32. For example, as shown in FIGS. 3A and 3B, the plurality of wheels 36 can be controlled by the base electronics 34, and the base electronics 34 can have a power source 35 for driving an electric motor 37 or the like. As an example, the plurality of wheels 36 can be driven by an electric motor 37 having a target capacity of about 65 kW to about 75 kW, and the power source 35 for the electric motor 37 can be a battery having a capacity of about 100 kWh.

[0216] The base electronics system 34 also includes a processor and memory elements programmed or configured to perform autonomous navigation of the cutting system 1. Further, as shown in FIG. 1, a LiDAR (light detection and ranging) system 38 and a Global Navigation Satellite System (GNSS) 40 are installed or supported on, for example, the base frame 10 or the base 32, and / or other frames among the frames 10, 12, 14, or 16 so that the position data of the cutting system 1 can be determined. The LiDAR system 38 and the GNSS 40 can be used for obstacle avoidance and navigation when the cutting system 1 moves autonomously. Preferably, for example, the cutting system 1 is implemented using a remote control interface and can communicate via one or more of Ethernet, USB, wireless communication, and GPS RTK (real time kinematics). The remote control interface and the communication device can be included in one or both of the base electronics system 34 and the imaging electronics system 42 (described later). As shown in FIG. 1, the cutting system 1 includes a display device 43 that displays data and / or images obtained by one or more devices and / or information provided by the base electronics system 34 (e.g., the location, speed, battery life, etc. of the cutting system 1), or can be communicatively connected to such a display device 43. Alternatively, the data and / or images obtained by one or more devices and provided by the base electronics system 34 may be displayed to the user through a user platform.

[0217] FIG. 2 is an enlarged view of a part of the cutting system 1 having one or more of the above devices. As shown in FIG. 2, the one or more devices include, for example, a camera 20, a robotic arm 22, and a cutting tool 24, which can be installed on the vertical frame 18 and / or other frames among the frames 10, 12, 14, or 16. Further devices among the one or more devices can also be provided, for example, on the vertical frame 18 and / or other frames among the frames 10, 12, 14, or 16.

[0218] The camera 20 may include a stereo camera, an RGB camera, etc. As shown in FIG. 2, the camera 20 may have a main body 20a including a first camera / lens 20b (e.g., a left camera / lens) and a second camera / lens 20c (e.g., a right camera / lens). Alternatively, the main body 20a may include two or more cameras / lenses. The resolution of the camera 20 may be, for example, 1536×2048 pixels or 2448×2048 pixels, but the camera 20 may have a different resolution. The camera 20 may have, for example, a PointGrey CM3-U3-31S4C-CS or PointGrey CM3-U3-50S5C sensor and a 3.5 mm f / 2.4 or 5 mm f / 1.7 lens, and the field of view may be 74.2535×90.5344 or 70.4870×80.3662. The camera 20 may have other sensors and lenses and may have a different field of view.

[0219] One or more light sources 21 may be attached to one or more sides of the main body 20a of the camera 20. The light source 21 may have, for example, an LED light source facing the same direction as one or more devices such as the camera 20 along the z-axis shown in FIG. 1. The light source 21 may provide illumination to the object(s) imaged by the camera 20. For example, the light source 21 may operate as a flash to compensate for ambient light when imaging by the camera 20 during daytime operation. During nighttime operation, the light source 21 may operate as a flash for the camera 20, or the light source may provide constant illumination to the camera 20. In a preferred embodiment, the one or more light sources 21 have, for example, a 100-watt LED module, but an LED module having a different wattage (e.g., 40 watts or 60 watts) may be used.

[0220] The robot arm 22 may include robot arms known to those skilled in the art, such as Universal Robot 3 e-series robot arms and Universal Robot 5 e-series robot arms. For example, the robot arm 22, also known as an articulated robot arm, may have a plurality of joints that act as axes enabling a degree of movement. Here, the more rotary joints the robot arm 22 has, the higher the freedom of movement the robot arm has. For example, the robot arm 22 can have 4 to 6 joints, and the number of rotational axes of the movement they provide is the same.

[0221] In a preferred embodiment of the present invention, the controller may be configured or programmed to control the movement of the robot arm 22. For example, the controller may be configured or programmed to control the movement of the robot arm 22 to which the cutting tool 24 is attached according to the steps described later and position the cutting tool 24. For example, the controller may be configured or programmed to control the movement of the robot arm 22 based on the location of the cutting point located on the target crop.

[0222] In a preferred embodiment of the present invention, the cutting tool 24 has a main body 24a and a blade portion 24b, as shown in FIG. 2, for example. The blade portion 24b may have a driven blade that moves relative to the fixed blade and is operated to perform a cutting operation together with the fixed blade. The cutting tool 24 may have, for example, a cutting device disclosed in U.S. Patent Application No. 17 / 961,666 (U.S. Patent Application Publication No. 2024 / 0116193), the entire content of which is incorporated herein by reference, having the name "End Effector Including cutting blade and pulley assembly".

[0223] In a preferred embodiment of the present invention, the cutting tool 24 can be attached to the robotic arm 22 using the robotic arm mount assembly 23. The robotic arm mount assembly 23 can have, for example, the robotic arm mount assembly disclosed in U.S. Patent Application No. 17 / 961,668 (published as U.S. Patent Application Publication No. 2024 / 0116173) entitled "Robotic Arm Mount Assembly including Rack and Pinion", which is incorporated herein by reference in its entirety.

[0224] The cutting system 1 can have an imaging electronics 42 that can be installed on the side frame 12 or the side frame 14, as shown in FIG. 1 for example. The imaging electronics 42 can supply power to and control each of the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30. That is, the imaging electronics 42 can have a power source that supplies power to each of the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30. Also, the imaging electronics 42 can have a processor and memory elements that are programmed or configured to control each of the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30. The processor and memory elements of the imaging electronics 42 can also be configured or programmed to control one or more devices including the camera 20, the robotic arm 22, the robotic arm mount assembly 23, and the cutting tool 24. Further, the processor and memory elements of the imaging electronics 42 can be configured or programmed to process the image data obtained by the camera 20.

[0225] As described above, the imaging electronic system 42 and the base electronic system 34 can include a group of processors and memory elements. The group of processors can be a hardware processor, a multipurpose processor, a microprocessor, a dedicated processor, a digital signal processor (DSP), and / or other types of processing components configured or programmed to process data. The memory elements can include one or more of volatile, non-volatile, and / or replaceable data storage elements. For example, the memory elements can include magnetic, optical, and / or flash memory elements that can be integrally or partially integrated with the processor. The memory elements can store instructions and / or instruction sets or programs that can be read and / or executed by the processor.

[0226] In another preferred embodiment of the present invention, the imaging electronic system 42 can be implemented, in part or in whole, by the base electronic system 34. For example, each of the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30 can receive power from and / or be controlled by the base electronic system 34 instead of the imaging electronic system 42.

[0227] In a further preferred embodiment of the present invention, the imaging electronic system 42 can be connected to a power source (s) different from the base electronic system 34. For example, a power source can be included in one or both of the imaging electronic system 42 and the base electronic system 34. Also, the base frame 10 can be detachably attached to the base 32 so that the base frame 10, the side frames 12 and 14, the horizontal frame 16, the vertical frame 18, and the components installed thereon can be installed on another moving body or the like.

[0228] The base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30 can move one or more devices along three separate directions or three separate axes. However, in another preferred embodiment of the present invention, only some of the one or more devices, such as the camera 20, the robot arm 22, and the cutting tool 24, may be moved by the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30. For example, the base frame motor 26, the horizontal frame motor 28, and the vertical frame motor 30 may move only the camera 20. Further, the cutting system 1 may be configured to linearly move the camera 20 along only a single axis while the camera takes a plurality of images, as described later. For example, the horizontal frame motor 28 may be configured to linearly move the camera 20 across a target crop such as a grapevine, and the camera 20 can take a plurality of images of the grapevine.

[0229] The imaging electronics 42 and the base electronics 32 of the cutting system 1 are each, for example, NVIDIA(R) JETSON TMA mobile platform may be provided by being partially or fully implemented by edge computing using an AGX computer or the like. In a preferred embodiment of the present invention, edge computing provides all of the computing and communication needs of the cutting system 1. FIGS. 3A and 3B show an example of a block diagram of a cloud system that includes a mobile platform and interacts with a cloud platform and a user platform. As shown in FIGS. 3A and 3B, the edge computing of the mobile platform includes a cloud agent, which is a service-based component that facilitates communication between the mobile platform and the cloud platform. For example, the cloud agent can receive commands and instruction data from a cloud platform (e.g., a web application on the cloud platform) and transfer the commands and instruction data to corresponding components of the mobile platform. As another example, the cloud agent can send operation data and production data to the cloud platform. Preferably, the cloud platform can include software components and data storage for maintaining the overall operation of the cloud system. The cloud platform preferably provides enterprise-level services with on-demand capabilities, fault tolerance, and high availability (e.g., AMAZON WEB SERVICES TM)。The cloud platform includes one or more application programming interfaces (APIs) for communicating with the mobile platform and the user platform. Preferably, the APIs are protected with a high level of security, and the capabilities of each API can be automatically adjusted according to the computing load. The user platform provides a dashboard for controlling the cloud system and receiving data obtained by the mobile platform and the cloud platform. The dashboard can be implemented by a web-based (e.g., Internet browser) application, a mobile application, a desktop application, etc.

[0230] As an example, the edge computing of the mobile platform shown in FIGS. 3A and 3B can obtain data from HW (hardware) GPS (Global Positioning System) (e.g., GNSS40) and LiDAR data (e.g., from the LiDAR system 38). Also, the mobile platform can obtain data from the camera 20. The edge computing of the mobile platform can have, for example, temporary storage for storing the raw data obtained by the camera 20. The edge computing of the mobile platform may also have, for example, persistent storage for storing the processed data. As a specific example, the camera data stored in the temporary storage can be processed by an artificial intelligence (AI) model, and then the camera data can be stored in the persistent storage, and the cloud agent can retrieve and transmit the camera data from the persistent storage.

[0231] <Method and System for Generating Fruit Tree Branch Cutting Point Data> With reference to FIGS. 4A, 4B, and 4C, a method for generating branch cutting point data of a fruit tree according to an embodiment of the present disclosure (hereinafter sometimes referred to as the "cutting point data generation method") and a system for generating branch cutting point data of a fruit tree according to an embodiment of the present disclosure (hereinafter sometimes referred to as the "cutting point data generation system") will be described. FIGS. 4A, 4B, and 4C are flowcharts showing an example of a procedure for generating branch cutting point data of a fruit tree by the branch cutting point data generation method according to an embodiment of the present disclosure or the branch cutting point data generation system according to an embodiment of the present disclosure. That is, the cutting point data generation method according to an embodiment of the present disclosure includes the following steps. The processing of the following steps is executed using one or more computing devices. The one or more computing devices may include not only the processor of the ECU (Electric Control Unit) mounted on the cutting system 1 but also the processors of one or more servers (computers) and / or terminal devices (including portable and fixed types) connected to the cutting system 1 via a communication network as shown in FIGS. 3A and 3B. Further, the data processing device included in the cutting point data generation system according to an embodiment of the present disclosure performs the processing of the following steps. Part or all of the functions of the data processing device included in the cutting point data generation system according to an embodiment of the present disclosure may be realized by one or more servers (computers) and / or terminal devices (including portable and fixed types) connected to the cutting system 1 via a communication network.

[0232] Here, an example of generating cutting point data of the branches of the fruit tree 200 using the agricultural machine 101 equipped with the cutting system will be described as in the example shown in FIG. 5. In the example shown in FIG. 5, the cutting system is mounted on the agricultural machine 101 having a moving body, and while the agricultural machine 101 moves between a plurality of fruit tree rows 201 in an orchard (for example, a vineyard), cutting point data of the branches of the fruit tree (for example, a grape tree) 200 is generated. The cutting system 1 moves between a plurality of fruit tree rows 201 in the orchard along a path indicated by a dashed arrow in FIG. 5, for example. The agricultural machine 101 may autonomously move between the plurality of fruit tree rows 201. The agricultural machine having the cutting system is not limited to a work vehicle such as a tractor, and may be a transport vehicle, a mobile robot, a multi-copter, or an unmanned aerial vehicle (UAV, so-called drone). In this specification, a grape tree may be described as an example of the fruit tree, but the embodiments of the present disclosure are not limited to grape trees and can be applied.

[0233] First, refer to FIG. 4A. In step S100, sensor data acquired by one or more sensors is obtained, which is sensor data of one or more branches of the fruit tree 200. The sensor data may include information indicating the three-dimensional structure of a plurality of branches of the fruit tree 200. For example, the LiDAR sensor included in the LiDAR system 38 of the cutting system 1 repeatedly outputs sensor data indicating the distance and direction to each measurement point of the branches of the fruit tree 200, or the three-dimensional coordinate values of each measurement point. An image of the branches of the fruit tree 200 acquired by the camera 20 may be obtained, and the estimated depth of the branches of the fruit tree may be obtained based on the acquired image. The sensor data does not necessarily have to include information indicating the three-dimensional structure of a plurality of branches of the fruit tree 200. For example, an image of the branches of the fruit tree 200 acquired by the camera 20 may be used as the sensor data. Regarding the method of acquiring the sensor data and the method of processing the acquired sensor data, all of the disclosure content of U.S. Patent Application No. 18 / 379,630 (Specification of U.S. Patent Application Publication No. 2024 / 0282105) is incorporated herein by reference. Each of the fruit trees 200 may be given an identifier. The sensor data acquired for each fruit tree 200 may be associated with the identifier of the corresponding fruit tree 200 and stored in the memory.

[0234] In step S200, based on the sensor data acquired in step S100, it is determined for each of one or more branches among the plurality of branches of the fruit tree 200 whether it is a branch to be removed or a branch to be left. The "one or more branches" are one or more branches that are the processing targets in step S200 among the branches of the fruit tree 200. For example, as will be described later, they are one or more branches that are the targets for selecting the result mother branches. The "one or more branches" are, for example, as will be described later, one or more branches grouped in the same group when the plurality of branches of the fruit tree 200 are grouped into a plurality of groups. In step S200, each of the one or more branches to be processed is classified as either a branch to be removed or a branch to be left. The "branch to be removed" means a branch that is mostly or entirely removed so as not to include a bud. The "branch to be left" is a branch that is not a branch to be removed, that is, a branch that is not removed at all or a branch that is only partially removed so as to include at least one bud. Examples of the "branch to be removed" and the "branch to be left" will be described later. In this specification, the "bud" of a branch does not include the bud closest to the root of the branch (basal bud) unless otherwise specified.

[0235] In step S300, for each of the branches determined to be branches to be removed in step S200, cutting point data including information indicating the three-dimensional position of the point to be cut is generated. Note that, as will be described later, depending on, for example, the pruning method of the fruit tree, there are cases where cutting point data is also generated for the branches determined to be branches to be left and cases where cutting point data is not generated for the branches determined to be branches to be left. Examples of the pruning method of the fruit tree will be described for short shoot pruning and long shoot pruning with reference to FIGS. 6A to 6C and FIGS. 7A to 7D described later.

[0236] As shown in the example shown in FIG. 4B, the procedure for generating the cutting point data of the branches of the fruit tree may further include step S400. In step S400, the cutting point data generated in step S300 is input to a control device that controls the three-dimensional position of a cutter (cutting tool 24 in the example of FIG. 1) that cuts the branches of the fruit tree 200. In this way, the cutter can be made to cut the branches of the fruit tree 200.

[0237] The acquisition of the sensor data in step S100 can be performed, for example, at a cycle of once or multiple times per second. After acquiring the sensor data at a certain time, the processes of step S200 and step S300 may be performed by the data processing device within the period until the next sensor data is acquired. In such a case, while the agricultural machine having the cutter moves along the fruit tree row, for each fruit tree, cutting the branches with the cutter based on the generated cutting point data can be sequentially executed. Note that the data processing device that performs the processes of step S200 and step S300 may be mounted on the agricultural machine, or one or more computing devices located outside the agricultural machine may function as part or all of the data processing device.

[0238] As shown in the example shown in FIG. 4C, the method and system according to the embodiment of the present disclosure may omit step S300. Such a system can output, for example, data indicating information that determines whether to remove or leave each of the one or more branches determined in S200. Such data may be input to another system in order to generate cutting point data. Alternatively, such data may be used, for example, for predicting the fruit yield.

[0239] In the example of FIG. 4C, the method according to an embodiment of the present disclosure includes obtaining sensor data acquired by one or more sensors, the sensor data being of one or more branches of a fruit tree (step S100), and determining, based on the sensor data, for each of the one or more branches whether it is a branch to be removed or a branch to be left (step S200).

[0240] FIG. 4D is a block diagram showing a schematic configuration example of a cutting system according to an embodiment of the present disclosure. As shown in FIG. 4D, a cutting system 1000 according to an embodiment of the present disclosure includes one or more sensors (sensor group) 520, and a data processing device 530 that generates cutting point data of a branch of a fruit tree based on the sensor data acquired from the sensor group 520. The data processing device 530 can be connected to, for example, a cutter control device (sometimes simply referred to as a "control device") 600 that controls the three-dimensional position of a cutter (cutting tool) 620 for cutting a branch of a fruit tree. The cutting system 1000 may further include the cutter control device 600. The cutting system 1000 may further include the cutter control device 600 and the cutter 620.

[0241] The sensor group 520 acquires sensor data of a branch of a fruit tree (for example, sensor data including information indicating the three-dimensional structure of a branch of a fruit tree). The sensor group 520 may include, for example, an imaging device such as a camera (for example, a stereo camera) that acquires an image of a branch of a fruit tree, a LiDAR sensor that acquires point cloud data by sensing a branch of a fruit tree, and the like. The sensor group 520 may include a plurality of imaging devices and / or a plurality of LiDAR sensors.

[0242] The data processing device 530 is one or more computing devices that process sensor data acquired by the sensor group 520. For example, it can be realized by an electronic control unit (ECU) for image recognition. The data processing device 530 may include one or more processors and one or more memories. A part of the processing executed by the data processing device 530 may be executed inside, for example, the sensor group 520 (imaging device) (inside the camera module). When both the sensor group 520 and the data processing device 530 are included in an agricultural machine, the sensor group 520 and the data processing device 530 are communicably connected, for example, via a bus.

[0243] The cutter control device 600 is one or more computing devices that control the three-dimensional position of the cutter 620 based on the cutting point data generated by the data processing device 530. For example, it is realized by a computing device such as one or more electronic control units (ECUs). In the examples of FIGS. 1 and 2, the cutting tool 24 is supported by the robot arm 22. As in the examples of FIGS. 1 and 2, when the cutter 620 is supported by an arm, the cutter control device 600 further controls the operation of the arm that supports the cutter 620.

[0244] As in the example of FIG. 1, when the cutting system is mounted on an agricultural machine having a moving body, the cutting system includes the cutting tool 24, the robot arm 22 that supports the cutting tool 24, the base (support) 32 that supports the robot arm 22, and a driving device that moves the base 32. The driving device may include various devices necessary for driving the agricultural machine, such as a prime mover and a transmission. One or more ECUs of the agricultural machine control the movement (e.g., traveling) of the agricultural machine by controlling the prime mover, the transmission, the traveling device (a plurality of wheels 36), etc. included in the driving device.

[0245] The cutting system 1000 may be mounted on an agricultural machine that cuts the branches of fruit trees as in the example shown in FIG. 1, or part or all of the processes executed by the cutting system 1000 may be executed by one or more computing devices located outside the agricultural machine that cuts the branches of fruit trees. For example, sensor data acquired by sensors of another agricultural machine different from the agricultural machine that cuts the branches of fruit trees may be used. Also, a server computer connected to a network may function as part or all of the data processing device 530.

[0246] FIG. 4E is a block diagram showing a configuration example of the data processing device 530. In the example of FIG. 4E, the data processing device 530 includes a processor 531, a ROM (Read Only Memory) 533, a RAM (Random Access Memory) 535, a communication device 537, and a storage device 539. These components may be interconnected via a bus B.

[0247] The processor 531 is a semiconductor integrated circuit, also referred to as a central processing unit (CPU) or a microprocessor. The processor 531 may include a graphics processing unit (GPU). The processor 531 sequentially executes a computer program that describes a predetermined set of instructions stored in the ROM 533, and realizes the processes necessary for generating the cutting point data of the present disclosure. The data processing device 530 may include a plurality of processors 531. The processes necessary for generating the cutting point data of the present disclosure may be executed collaboratively by the plurality of processors 531. Part or all of the processor 531 may be an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or an ASSP (Application Specific Standard Product) equipped with a CPU.

[0248] The communication device 537 is an interface for performing data communication between the data processing device 530 and an external computing device. The communication device 537 can perform wired communication such as CAN (Controller Area Network), or wireless communication compliant with the Bluetooth (registered trademark) standard and / or the Wi-Fi (registered trademark) standard.

[0249] The storage device 539 can store sensor data acquired from the sensor group 520, sensor data during processing, or data during the process of generating cut point data. The storage device 539 includes, for example, a hard disk drive or a non-volatile semiconductor memory.

[0250] The hardware configuration of the data processing device 530 is not limited to the above example. It is not necessary for part or all of the data processing device 530 to be mounted on an agricultural machine that cuts branches of fruit trees. By using the communication device 537, it is also possible to cause one or more computing devices located outside the agricultural machine that cuts branches of fruit trees to function as part or all of the data processing device 530. For example, one or more server computers connected to a network and / or one or more computing devices included in a terminal device can function as part or all of the data processing device 530. On the other hand, one or more computing devices mounted on an agricultural machine that cuts branches of fruit trees may execute all the functions required of the data processing device 530.

[0251] One example of a "control device" in the present disclosure is a computing device comprising at least one processor and at least one memory storing a computer program (code) that defines a control process executed by the processor. Another example of a "control device" is a computing device comprising a hardware accelerator such as an FPGA (Field-Programmable Gate Array), an ASSP (Application Specific Standard Product), or an ASIC (Application-Specific Integrated Circuit) configured to execute a control process.

[0252] Similarly, one example of a "data processing device" in the present disclosure is a computing device comprising at least one processor and at least one memory storing a computer program (code) that defines a processing process executed by the processor. Another example of a "data processing device" is a computing device comprising a hardware accelerator such as an FPGA or an ASIC configured to execute a processing process.

[0253] The "processor" in the present disclosure is a hardware electronic circuit such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), DSP (Digital Signal Processor), ISP (Image Signal Processor), or NPU (Neural Network Processing Unit). The "memory" is a hardware electronic circuit such as a ROM (Read Only Memory) or RAM (Random Access Memory). A part of the memory may be storage media connected to the processor by wiring or a network. These hardware electronic circuits can be implemented by one or more integrated circuits (ICs) or large-scale integrated circuits (LSIs). Each functional unit or block within the electronic circuit, and related components, may be manufactured individually as separate integrated circuit chips, or a part or all of these functional units or blocks may be combined and manufactured as a single integrated circuit chip.

[0254] The program that defines the operation of the processor is designed such that the processor executes one or more functions, operations, steps, or processes in the embodiments of the present invention.

[0255] FIG. 4F is a schematic diagram showing a configuration example of the cutting system. The data processing device 530 is not limited to the example of being mounted on the agricultural machine 101. That is, some or all of the functions of the data processing device 530 may be realized by one or more servers (computers) 500 or terminal devices (including portable and fixed types) 600 connected to the communication device 537 of the data processing device 530 via the communication network N. Such a communication network N is connected to other agricultural machines (for example, tractors) 700, and communication may be performed between the agricultural machine 101 having the data processing device 530 and the other agricultural machines 700. A part of the data used for the processing of the data processing device 530 may be provided from the other agricultural machines 700 to the data processing device 530 via the communication network N.

[0256] [Fruit tree pruning methods: Spur pruning and long cane pruning] Referring to FIGS. 6A to 6C, spur pruning will be described. FIGS. 6A to 6C are schematic diagrams for explaining spur pruning. FIG. 6A shows a fruit tree 200a before pruning, schematically showing the fruit tree 200a in a state where harvesting has ended and leaves have fallen, and the blown-out part shows an enlarged part of the fruit tree 200a. FIG. 6B shows the fruit tree 200a after pruning from the state of FIG. 6A, and FIG. 6C shows the subsequent fruit tree 200a, respectively, schematically. In the figures, for simplicity, only the buds 59 and new shoots 61 are shown on some of the branches 58, and the illustration is omitted for the remaining branches 58.

[0257] As shown in FIG. 6A, the fruit tree 200a has a plurality of spurs 56, and a plurality of canes 58 grow from each spur 56. Each cane 58 has a bud 59. The basal bud 60 located closest to the base (the side close to the spur 56) of each cane 58 is shown separately from the other buds 59. In this specification, unless otherwise specified, the "buds" of a cane do not include the basal bud. Among the buds 59 of each cane 58, the distance between two adjacent buds 59 is called the internode length Ln. In spur pruning, as shown in FIG. 6B, among the plurality of canes 58 growing from each spur 56, typically only one is left, and the other canes 58 are removed. Further, the remaining canes 58 are also cut short so that they have only a few (for example, about 2 to 3) buds 59. The cane 58 that is cut short and left is indicated by the reference numeral "58a", and the remaining cane 58a may be called a fruiting cane. By growing from the state of FIG. 6B, as shown in FIG. 6C, a new shoot 61 germinated from the bud 59 of the fruiting cane 58a grows into a branch and bears fruit. As the new shoot 61 grows and changes into a branch, it becomes the object of pruning in the next dormant period (for example, the next winter). Note that in the pruning in the next dormant period, the fruiting cane 58a and the spur 56 can be called a spur.

[0258] In the illustrated example, a plurality of short shoots 56 are supported by a thick branch 54 extending in a substantially horizontal direction. The thick branch 54 is supported by a trunk 52 extending in a substantially vertical direction from the ground. The thick branch 54 may be called a cordon. Such a vine training method may be called cordon training. A training method in which two main branches 54 extend from the trunk 52 (for example, two main branches 54 extend on both the left and right sides of the trunk 52) as in the illustrated example is called double-cordon training or bilateral-cordon training. In contrast, a training method in which only one main branch 54 extends from the trunk 52 is called single-cordon training. Note that depending on the training method, the fruit tree may not have a main branch 54 extending in a substantially horizontal direction. For example, in head training, all of a plurality of canes grow from a head located at the upper part of the trunk, and at this time, there is no main branch between the trunk and the canes.

[0259] In the illustrated example, among the plurality of branches 58 growing from each short shoot 56, only one branch 58 is left as a fruiting mother branch, but this is not limited to this example. For example, in addition to the fruiting mother branch, a renewal cane may be further left. The renewal cane is also cut short so as to have only a few (for example, about two to three) buds.

[0260] Cane pruning will be described with reference to FIGS. 7A to 7D. FIGS. 7A to 7D are schematic diagrams for explaining cane pruning. FIG. 7A shows a fruit tree 200b before pruning, in a state where the harvest has ended and the leaves have fallen. FIG. 7B shows the fruit tree 200b after pruning from the state of FIG. 7A. FIG. 7C shows the fruit tree 200b after guiding and tying operations from the state of FIG. 7B. FIG. 7D shows the subsequent fruit tree 200b, each schematically. In the figures, for simplicity, buds 59 and new shoots 61 are shown only on some of the branches 58, and illustration of the remaining branches 58 is omitted.

[0261] In long shoot pruning, as shown in FIG. 7A, among the plurality of branches 58 growing from the head 53 of the trunk 52, as shown in FIG. 7B, several (for example, about 2 to 4) branches 58 are left, and the other branches 58 are removed. In the example of the figure, among the plurality of branches 58, two branches 58_1 and 58_2 are left. In long shoot pruning, the branches 58 (58_1 and 58_2) to be left are basically not cut. As shown in FIG. 7C, after the pruning operation, the guiding and tying operations of the remaining branches 58 are performed. In order to determine the direction of the new shoots growing from the buds 59 of the remaining branches 58, the remaining branches 58 are bent in a desired direction and fixed to the wire 71. For example, in this example, it is fixed to the wire 71 extending in a substantially horizontal direction so that the remaining branches 58 extend in a substantially horizontal direction. The wire 71 may be supported by a support column extending, for example, in a substantially vertical direction. By growing from the state of FIG. 7C, as shown in FIG. 7D, the new shoots 61 germinated from the buds 59 of the remaining branches 58 grow and bear fruit. The remaining branches 58 are sometimes called fruiting canes.

[0262] In long shoot pruning, the number of branches 58 to be left may vary depending on, for example, the training method of the fruit tree. When extending the fruiting mother branches 58 on both the left and right sides of the trunk 52 as in the illustrated example, two branches 58 to be left are selected. The training method in which two fruiting mother branches 58 extend from the trunk 52 as in the illustrated example is called the Giyot double guyot. On the other hand, the training method in which only one fruiting mother branch extends from the trunk 52 is called the Giyot single guyot. Note that the illustrated training method has no thick branches extending in a substantially horizontal direction, and all of the plurality of fruiting mother branches 58 grow from the head 53 located above the trunk 52, so it may be classified as head-trained.

[0263] As shown in the illustrated example, the shape of a trellis system configured such that new shoots (or branches) grow vertically upward is called VSP (vertical shoot position). The trellis system is composed of supports, wires, nets, etc. for supporting the branches and vines of plants.

[0264] As shown in the illustrated example, in addition to a predetermined number (two in the figure) of fruiting mother branches 58, spare fruiting mother branches 58b may be further left. The spare fruiting mother branches 58b are left after being cut short so as to have a predetermined number (for example, several) of buds 59.

[0265] FIG. 8 is a flowchart showing an example of a procedure for generating cutting point data of branches of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 8 mainly differs from the flowchart of FIG. 4A in that it further has steps S010 and S012. The example of FIG. 8 can also be applied to the flowcharts of FIGS. 4B and 4C.

[0266] As described above, in the case of long shoot pruning, it is different from the case of short shoot pruning in that cutting point data may not be generated for the determined branches 58 to be left. The determined branches to be left include, for example, fruiting mother branches in both the case of short shoot pruning and the case of long shoot pruning. The determined branches to be left may further include spare fruiting mother branches in addition to the fruiting mother branches in both the case of short shoot pruning and the case of long shoot pruning. The branches to be removed are all branches other than the determined branches to be left.

[0267] In step S010 of FIG. 8, it is determined whether to generate cutting point data for the branches determined to be the remaining branches. The determination is made, for example, based on the input of the user. The user can input whether to generate the cutting point data of the remaining branches according to, for example, the pruning method of fruit trees, the tailoring method, etc. For example, in the case of short shoot pruning, it is Yes in step S010. Note that even in the case of long shoot pruning, there may be a case where cutting point data is generated for the branch 58 determined to be the remaining branch. For example, for the branch 58 determined to be the remaining branch as the result mother branch, cutting point data may be generated, for example, to adjust the number of buds 59 that the branch 58 has. Also, for the branch 58 determined to be the remaining branch as the preliminary result mother branch, cutting point data can be generated so as to have a predetermined number of buds 59. Such cases are also included in the case of Yes in step S010.

[0268] If it is Yes in step S010, proceed to step S012.

[0269] In step S012, information on the number of remaining buds for each of the remaining branches is acquired. The information on the number of remaining buds is acquired, for example, based on the input of the user. The user can input the number of remaining buds for the remaining branches according to, for example, the variety of fruit trees, the pruning method, the tailoring method, the cultivation plan based on the yield plan, the field design (for example, if the fruit tree is a grape tree, the vineyard design), etc. The field design and the vineyard design can be determined by, for example, at least one element of the shape of the trellis system, the pruning method, and the tailoring method.

[0270] Next, proceed to step S100 (acquisition of sensor data) and step S200 (determination of branches to be removed or remaining branches). The processes of step S100 and step S200 are performed in the same manner as the processes in the example of FIG. 4A. One or more branches to be processed in step S200 are, for example, one or more branches to be selected as the result mother branches, and do not include the main branches or the main trunk.

[0271] Next, proceed to step S300 (generation of cutting point data). When the answer in step S010 is Yes, step S300 includes step S302 and step S304.

[0272] In step S302, for each of the branches 58 determined to be branches to be removed in step S200, generate cutting point data. The cutting point data for the branches to be removed is generated such that each of the branches 58 determined to be branches to be removed has no buds after being cut (i.e., the number of buds it has after being cut becomes zero). For example, in the case of short pruning and cordon training, generate the cutting point data such that it is cut at a position close to the short shoot 56 at the base of the branch 58. For example, generate the cutting point data such that it is cut between the short shoot 56 at the base of the branch 58 and the bud 59 closest to the short shoot 56 among the buds 59 that the branch 58 has. In the case of bush training (for example, in the case of long pruning), generate the cutting point data such that it is cut at a position close to the bush 53 at the base of the branch 58. For example, generate the cutting point data such that it is cut between the bush 53 at the base of the branch 58 and the bud 59 closest to the bush 53 among the buds 59 that the branch 58 has.

[0273] In step S304, for each of the branches 58 determined to be branches to be retained, generate cutting point data. Based on the information on the number of buds to be retained on each of the branches to be retained obtained in step S012, generate the cutting point data for each of the branches 58 determined to be branches to be retained. The cutting point data for the branches to be retained is generated such that each of the branches 58 determined to be branches to be retained has one or more buds 59 after being cut. Note that the order of step S302 and step S304 is not limited, and they may be performed simultaneously (in parallel).

[0274] When the answer in step S010 is No, proceed to step S100. When the answer in step S010 is No, it is different from the case when the answer in step S010 is Yes in that step S012 and step S304 are not performed.

[0275] In the example of FIG. 8, steps S010 and S012 are performed before step S100, but are not limited thereto. Steps S010 and S012 may be performed before step S300. For example, they may be performed between step S100 and step S200.

[0276] [Grouping] FIG. 9A is a flowchart showing an example of a procedure for generating cutting point data of a fruit tree branch according to an embodiment of the present disclosure. The flowchart of FIG. 9A differs from the flowchart of FIG. 4A in that it includes steps S220 and S222 as the processes performed in step S200. The example of FIG. 9A can be combined with any of the above-described flowcharts. For example, the example of FIG. 9A can be applied to the flowcharts of FIGS. 4B, 4C, or 8. The same applies to the following examples of flowcharts.

[0277] In the example of FIG. 9A, the procedure for generating cutting point data of a fruit tree branch includes sensor data acquired by one or more sensors, and includes acquiring sensor data of a plurality of branches of the fruit tree (step S100), grouping the plurality of branches into a plurality of groups based on the sensor data (step S220), determining, for each of one or more branches grouped into the same group among the plurality of groups, whether to be a branch to be removed or a branch to be left based on the sensor data (step S222), and generating cutting point data including information indicating the three-dimensional position of the point to be cut for each of the branches determined to be branches to be removed (step S300).

[0278] The process of step S100 is performed in the same manner as the process in the example of FIG. 4A.

[0279] After step S100, in step S220, based on the sensor data obtained in step S100, a plurality of branches 58 of the fruit tree are grouped into a plurality of groups. The grouping of the plurality of branches 58 can be performed based on the position of the root of each of the plurality of branches 58. For example, in the case of short shoot pruning and cordon training, the plurality of groups respectively correspond to a plurality of short shoots 56 of the fruit tree. Among the plurality of branches 58 of the fruit tree, the branches 58 growing from the same short shoot 56 may be grouped into the same group. Among the plurality of branches 58 of the fruit tree, the branches 58 growing from within a predetermined range of regions may be grouped into the same group.

[0280] In the case of long shoot pruning and / or bush training, the plurality of branches of the fruit tree are grouped into one or a plurality of groups of different numbers, for example, according to the number of branches to be left as fruiting mother branches. For example, examples of combinations of the number Nn of branches to be left as fruiting mother branches and the number Ng of groups are (Nn, Ng) = (1, 1), (2, 2), (3, 3 or 2), (4 or more, 2), etc. For example, in the case of using a training method (Goyo double training) in which two fruiting mother branches extend from the bush, the plurality of branches of the fruit tree can be grouped into two groups. When the plurality of branches of a fruit tree include branches extending in a certain direction (for example, either the left or right direction) centered on the bush or the main trunk and branches extending in the opposite direction (for example, the other of the left or right direction) from the bush or the main trunk, one or more branches extending in one direction are grouped into a first group, and one or more branches extending in the other direction are grouped into a second group, respectively. When the plurality of branches of a fruit tree extend only in one direction centered on the bush or the main trunk (for example, in the case of Goyo single training), all the branches are treated as one group, that is, the grouping process as described above can be omitted.

[0281] FIG. 10A and FIG. 10C are examples of images used in step S220, and FIG. 10B is an example of the image acquired in step S100. The image 51a shown in FIG. 10A can be obtained by applying instance segmentation to the image 51_0 of the fruit tree obtained by the camera 20 shown in FIG. 10B. "Segmentation (region division)" is a general term for algorithms that classify objects or individuals (instances) included in an image into classes or categories on a pixel-by-pixel basis and are used in deep learning. Among segmentations, instance segmentation is an algorithm that classifies individuals included in an image. By applying instance segmentation to an image including branches of a fruit tree, individual parts of the fruit tree can be identified or extracted. The segmented image includes masks for extracting each of the parts of the fruit tree (for example, the main trunk, main branches, fruiting mother branches, branches, short shoots, etc.) in the input image data. For example, the segmented image 51a includes short shoot masks M56_1, M56_2, and M56_3 that extract short shoots 56_1, 56_2, and 56_3, respectively, and branch masks M58_1, M58_2, M58_3, M58_4, M58_5, and M58_6 that extract branches 58_1, 58_2, 58_3, 58_4, 58_5, and 58_6, respectively, and a main branch mask M54 that extracts the main branch 54. In the figure, the regions extracted by the respective masks are shown with hatching (or color) and the reference signs of the masks. The branch masks M58_1 to M58_6 may be collectively referred to as the branch mask M58, and the short shoot masks M56_1 to M56_3 may be collectively referred to as the short shoot mask M56.

[0282] In step S220, based on a segmented image 51a as shown in, for example, FIG. 10A, a plurality of branches 58 of a fruit tree can be grouped into a plurality of groups. Based on the segmented image 51a, for each branch 58, an association with a corresponding short shoot 56 can be made. Branches 58 associated with an identifier indicating the same short shoot 56 are grouped into the same group. For example, each branch mask M58 can be associated with the nearest short shoot mask M56. For example, by identifying pixels included in both the branch mask M58 and the short shoot mask M56, an overlap between the branch mask M58 and the short shoot mask M56 is shown, so that a connection point between the branch mask M58 and the short shoot mask M56 is identified. By identifying the connection point between the branch mask M58 and the short shoot mask M56, each branch mask M58 can be associated with the short shoot mask M56 in contact at the connection point. For example, in the example of FIG. 10A, since the branch masks M58_1 to M58_6 are associated with the short shoot mask M56_1, it is recognized that the six branches 58_1 to 58_6 indicated by the branch masks M58_1 to M58_6 grow from the short shoot 56_1 indicated by the short shoot mask M56_1, and thus the six branches 58_1 to 58_6 growing from the short shoot 56_1 are grouped into the group associated with the short shoot 56_1. FIG. 10C shows a segmented image 51b including only these masks to schematically show the grouping result. The segmented image 51b shown in FIG. 10C is different from the segmented image 51a shown in FIG. 10A in that it does not include other masks.

[0283] Note that FIGS. 10A and 10B show an example of an image including only the vicinity of the short shoot 56_1 of the fruit tree, but in step S220 (grouping process), actually an image including a wider range can be used. An image including the entire fruit tree may also be used.

[0284] Regarding the buds on the branches, similar to the example in Fig. 10A, they may be identified or extracted by applying instance segmentation to an image including the branches of the fruit tree, or may be identified or detected using object detection. For each bud identified by instance segmentation or object detection, an association with an identifier indicating the corresponding branch (e.g., branch mask M58) is made. When a bud is identified by instance segmentation, the connection point between the bud mask and the branch mask M58 is identified by identifying the pixels included in both the bud mask for extracting the bud and the branch mask M58. The bud mask of each bud can be associated with the branch mask M58 that it touches at the connection point. For the object detection process, an object detection model trained using an algorithm based on deep learning can be used. Also, object detection algorithms such as YoloV5 and Yolov4 can be used. The image to which the object detection process is applied may include a rectangular bounding box for detecting each bud. Either the center point of the bounding box or any of the four vertices of the rectangle may be used as a reference coordinate indicating the position of the bud. Based on the positional relationship between the position (reference coordinate) of each bud and the branch mask M58, each bud can be associated with the nearest branch mask M58.

[0285] Next, in step S222, based on the sensor data obtained in step S100, for each of one or more branches 58 grouped into the same group in step S220, it is determined whether to make it a branch to be removed or a branch to be retained. Among the plurality of branches of the fruit tree, since a branch to be retained (for example, a fruiting mother branch) can be selected from among one or more branches 58 grouped into the same group, efficient pruning work can be performed while maintaining the fruit yield and quality. When a plurality of groups correspond to a plurality of short shoots 56, since a fruiting mother branch 58 can be selected for each short shoot 56, cutting point data adapted to the needs in the pruning work can be generated. For example, from among one or more branches 58 grouped into the same group, a branch 58 to be retained may be selected and determined, and all branches 58 other than the determined branch 58 to be retained may be determined as branches to be removed. At this time, the one or more branches grouped into the same group are two or more branches. Information on the number and type of branches to be retained may be obtained based on, for example, user input, and based on the obtained information, a branch 58 to be retained may be selected. The process of step S222 can be performed based on, for example, a segmented image as shown in FIG. 10C.

[0286] Note that it may be the case that all of one or more branches 58 grouped into the same group are determined as branches to be removed. For example, when a branch to be retained cannot be selected from among one or more branches 58 grouped into the same group, or when there is no suitable branch 58 as a branch to be retained, all of one or more branches 58 may be determined as branches to be removed. Also, it may be the case that all of one or more branches 58 grouped into the same group are determined as branches to be retained. For example, when it is determined that all of one or more branches 58 grouped into the same group are not in a state suitable for pruning (cutting) (for example, too early in terms of time), all of one or more branches 58 may be determined as branches to be retained. At this time, generation of the cutting point data may not be performed.

[0287] Based on the determination in step S222, the process of step S300 is performed. The process of step S300 is performed in the same manner as the process in the example of FIG. 4A or FIG. 8.

[0288] After step S300, step S400 may be further included as in the example of FIG. 4B. That is, the cut point data generated in step S300 may be input to a control device that controls the three-dimensional position of a cutter (for example, the cutting tool 24 included in the cutting system 1 of FIG. 1) that cuts the branches of the fruit tree. In this way, the cutter can be made to cut the branches of the fruit tree.

[0289] FIG. 9B is a flowchart showing an example of a procedure for generating cut point data for branches of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 9B differs from the flowchart of FIG. 9A in that it has step S230 and step S240 instead of step S222.

[0290] The processes of step S100 and step S220 are performed in the same manner as the processes in the example of FIG. 9A.

[0291] In step S230, based on the sensor data acquired in step S100, for each of one or more branches 58 grouped into the same group in step S220, measurement values regarding one or more attributes are acquired. The one or more attributes include the color of the branch 58, the direction in which the branch 58 extends, the thickness of the branch 58, the height of the base of the branch 58, the size of the bud 59 included in the branch 58, the direction in which the bud 59 included in the branch 58 faces, the length of the branch 58, the length of the internodes of the branch 58 (that is, the distance between adjacent buds 59), and the like. Any two or more of the above may be included. In the present disclosure, the "attributes" of a branch refer to attributes that appear in the appearance of the branch, and can also be paraphrased as morphological features, appearance characteristics, or external features. Details regarding each attribute will be described later. For example, based on a segmented image as shown in FIG. 10C, for each of one or more branches 58 grouped into the same group, measurement values regarding each attribute are acquired.

[0292] Next, in step S240, based on the measurement values obtained in step S230, for each of the one or more branches 58 grouped into the same group in step S220, it is determined whether to make it a branch to be removed or a branch to be left. A specific example of a method for determining whether to make it a branch to be removed or a branch to be left based on the measurement values regarding one or more attributes will be described later.

[0293] Based on the determination in step S240, the process of step S300 is performed.

[0294] [Detection of branches outside candidates] As will be described with reference to FIG. 11A, in step S240, branches that should not be selected as branches to be left (sometimes referred to as "branches outside candidates") may be detected and excluded from the candidates for branches to be left. For example, branches with potential problems in the health state can be excluded from the candidates for branches to be left. As described above, the branches to be left include, for example, fruiting mother branches. Since it is possible to avoid selecting a branch with a poor health state as a fruiting mother branch, a decrease in the fruit harvest amount and quality can be suppressed.

[0295] FIG. 11A shows a flowchart of a process for detecting branches outside candidates that can be performed in step S240.

[0296] In step S240a, it is determined whether to exclude branches outside candidates. For example, based on the input of the user, it is determined whether to perform the process of excluding branches outside candidates. If Yes in step S240a, the process proceeds to step S240b. If No in step S240a, the process proceeds to step S240h. Steps S240h and subsequent step S240j can be performed in the same manner as step S240 shown in FIG. 9b, for example.

[0297] In step S240b, based on the sensor data obtained in step S100, it is determined whether one or more branches grouped into the same group among the plurality of groups in step S220 contain branches outside the candidates. For example, based on the segmented image 51a shown in FIG. 10A, it is determined whether the six branches 58_1 to 58_6 grouped into the same group contain branches outside the candidates. Based on the segmented image 51a, for each of the six branches 58_1 to 58_6, information regarding one or more attributes (for example, the color of the branch, the shape of the branch, the thickness of the branch, the length of the branch, the variety of the fruit tree, the age of the fruit tree, the geographical condition, etc.) is obtained, and based on the obtained information, it is determined whether the branch is outside the candidates. Specifically, for example, healthy branches are usually brown, while branches in poor health may be at least partially black or white on the surface. For example, a branch determined to have a predetermined range (or a predetermined ratio) of the surface area being black or white can be detected as a branch outside the candidates (a diseased branch). If the six branches 58_1 to 58_6 contain a branch outside the candidates (Yes in step S240c), the process proceeds to step S240d. If the six branches 58_1 to 58_6 do not contain a branch outside the candidates (No in step S240c), the process proceeds to step S240h.

[0298] The determination of whether one or more branches grouped into the same group contain a branch outside the candidates can be performed by any one or any combination of the following methods, for example.

[0299] (i) For example, by obtaining a measurement value regarding the color of the branch, it can be determined whether the branch is a branch outside the candidates. The determination is performed by the following steps of processing shown in FIG. 11C, for example. FIG. 11C is a flowchart showing an example of the processing performed in step S240b.

[0300] Step S10-1: Using one or more sensors (for example, a camera), sensor data of the branch (for example, an image including the branch) is obtained.

[0301] Step S10-2: Using the acquired sensor data, extract the parts corresponding to the branches. For example, apply segmentation (e.g., instance segmentation) using AI to the acquired image.

[0302] Step S10-3: Obtain information regarding the color of the parts corresponding to the extracted branches (e.g., RGB values, HSL values, and their statistical values).

[0303] Step S10-4: Based on the acquired color-related information, determine whether the branch is outside the candidates. For example, store the relationship between the color-related information and the evaluation criteria for whether it is a branch outside the candidates (e.g., a table) in a storage device, and make the determination by referring to the stored information (table).

[0304] (ii) It is possible to determine whether the branch is a branch outside the candidates based on whether there are cane spots and / or knots on the surface of the branch. This is because if there are cane spots and / or knots on the surface of the branch, the branch is likely to be diseased. It may be combined with the determination method in (i) above. The determination is performed, for example, by the following steps of processing shown in FIG. 11D. FIG. 11D is a flowchart showing an example of the processing performed in step S240b.

[0305] Step S12-1: Using one or more sensors (e.g., cameras), acquire sensor data of the branches (e.g., an image including the branches).

[0306] Steps S12-2 and S12-3: Using the acquired sensor data, extract the parts corresponding to the branches (step S12-2), and determine whether there are cane spots and / or knots on the branches (step S12-3). For example, in step S12-2, apply segmentation (e.g., instance segmentation) to the acquired image to extract the parts corresponding to the branches. The detection of cane spots and knots in step S12-3 can be performed, for example, by object detection using artificial intelligence (AI).

[0307] (iii) Using a machine learning model, it is possible to determine whether the branch is a non-candidate branch. The determination is performed, for example, by the following steps shown in FIG. 11E. FIG. 11E is a flowchart showing an example of the process performed in step S240b.

[0308] Step S14-1: Using one or more imaging devices (e.g., cameras), obtain an image of the branch.

[0309] Step S14-2: Prepare images of diseased branches and healthy branches as a training dataset, and prepare a trained model obtained by training this using supervised learning. Note that the order of step S14-1 and step S14-2 does not matter, and they can be performed simultaneously (in parallel).

[0310] Step S14-3: Input the image of the branch obtained in step S14-1 into the trained model prepared in step S14-2, and determine (output) whether the branch has a high possibility of being diseased.

[0311] (iv) Based on the input of other information, it is possible to determine whether the branch is a non-candidate branch. For example, information indicating a suspicion of disease that can be obtained in operations other than pruning performed on the fruit tree (e.g., quality measurement operations), information on the presence or absence (history) of past diseases of the fruit tree, disease prediction information, etc. are obtained (or available). In such cases, these information can be input into the system and stored. When these information are input into the system, based on these information, it is possible to determine whether the branch is a non-candidate branch.

[0312] In step S240d, it is determined, for example, based on a user input, whether to include the branches outside the candidates in the process of determining whether each of them is a branch to be removed or a branch to be left. For example, the user can pre-enter a setting on whether to automatically continue the process of determining whether each of the branches other than the branches outside the candidates is a branch to be removed or a branch to be left when a branch outside the candidates is detected. If the result in step S240d is Yes (for example, if the setting is made to automatically continue the process of determining whether each of the branches outside the candidates is a branch to be removed or a branch to be left), the process proceeds to step S240e.

[0313] In step S240e, branches to be left are selected and determined from among the branches obtained by removing the branches outside the candidates from one or more branches grouped in the same group. Then, in step S240f, from among the branches obtained by removing the branches outside the candidates from the one or more branches to be processed, branches other than the branches determined to be the branches to be left are determined to be the branches to be removed. At this time, the branches outside the candidates are also determined to be the branches to be removed.

[0314] If the result in step S240d is No (for example, if the setting is made not to automatically continue the process of determining whether each of the branches outside the candidates is a branch to be removed or a branch to be left), the process proceeds to step S240g. In step S240g, the user is notified that a branch outside the candidates has been detected. Information identifying the branch outside the candidates may be further notified to the user.

[0315] After step S240g, as described below in the processing of step S240r and step S240s, it is determined whether to include the branches outside the candidates in the processing of determining whether to determine the branches outside the candidates as the branches to be removed, or to be either the branches to be removed or the branches to be left, or not to include them in the processing of determining whether to be either the branches to be removed or the branches to be left (for example, after attaching information indicating that it is a branch that is neither a branch to be removed nor a branch to be left, do not generate cut point data for the branches outside the candidates), and a decision is made on which process to perform. For example, the user can receive a notification that a branch outside the candidate has been detected, check data such as an image of the detected branch outside the candidate, and then select and input which of the above processes to perform.

[0316] After step S240g, in step S240r, it is determined, for example, based on user input, whether to include the branches outside the candidates in the process of determining whether each of the branches to be removed or the branches to be left. If the result in step S240r is Yes, then in step S240s, it is determined, for example, based on user input, whether to determine the branches outside the candidates as the branches to be removed. If the result in step S240s is Yes, the process proceeds to step S240e and subsequent step S240f described above. For example, if it is highly likely that the detected branches outside the candidates are diseased branches, and thus the detected branches outside the candidates are determined as the branches to be removed at that time, then the result in step S240r should be Yes and the result in step S240s should be Yes. In this case, in steps S240e and S240f, the branches to be left are selected from among the branches excluding the branches outside the candidates and the detected branches. If the result in step S240s is No, the process proceeds to step S240h and subsequent step S240j described above, and for each of one or more branches grouped in the same group including the branches outside the candidates and the detected branches, it is determined whether each of them is the branch to be removed or the branch to be left. For example, if it is unlikely that the detected branches outside the candidates are diseased branches, and thus there is little need to immediately determine the detected branches outside the candidates as the branches to be removed, then the result in step S240r should be Yes and the result in step S240s should be No. In this case, in steps S240h and S240j, the process of selecting the branches to be left is performed from among the branches outside the candidates and the detected branches.

[0317] In the case of No in step S240r, that is, when not including a branch outside the candidates in the process of determining whether to make it a branch to be removed or a branch to be left, proceed to step S240t. In step S240t, for each of the branches obtained by removing the branches outside the candidates from one or more branches grouped in the same group, determine whether to make it a branch to be removed or a branch to be left. The determination of whether to make it a branch to be removed or a branch to be left can apply the example described above. For example, when it is difficult to determine whether the detected branch outside the candidates is a diseased branch, set it as No in step S240r and proceed to step S240t. For the detected branch outside the candidates, for example, after attaching information indicating that it is neither a branch to be removed nor a branch to be left, defer the determination of whether to make it a branch to be removed or a branch to be left.

[0318] Figure 10D is an example of an image used in step S240. To schematically show the result of detecting a branch outside the candidates, the segmented image 51c shown in Figure 10D is different from the segmented image 51b shown in Figure 10C in that it does not include the branch mask M58_4. For example, based on the segmented image 51a shown in Figure 10A, when it is determined that the branch 58_4 among the six branches 58_1 to 58_6 grouped in the same group is a branch outside the candidates, it is possible to select a branch to be left from the five branches excluding the branch 58_4. Alternatively, when it is determined that the branch 58_4 is a branch outside the candidates, the process of generating cut point data for the entire fruit tree may be skipped and the process may proceed to the next fruit tree.

[0319] [Acquisition and evaluation of measurement values regarding one or more attributes] With reference to Figure 11B, a specific example of the process of acquiring and evaluating measurement values regarding one or more attributes performed in step S240 will be described.

[0320] FIG. 11B is a flowchart showing a specific example of a process of obtaining and evaluating measurement values related to one or more attributes, which is performed in step S240. Here, a case where there are two or more branches grouped into the same group among a plurality of groups will be described. As will be described below, in the example of FIG. 11B, based on the factor scores for each attribute of each branch, the total score of each branch is calculated, and based on the total score, for each of two or more branches grouped into the same group, a determination is made as to whether to make the branch to be removed or the branch to be left. Note that in step S240, both the process of FIG. 11A and the process of FIG. 11B may be performed.

[0321] As shown in FIG. 11B, in step S240k, for each of two or more branches grouped into the same group among the plurality of groups in step S220, for each of one or more attributes, a factor score is determined based on the measurement value obtained in step S230.

[0322] The factor score for each attribute of each branch can be determined so that the higher the factor score, the more favorable the branch is as a result mother branch with respect to that attribute. A branch that is favorable as a result mother branch is, for example, a branch on which fruits of good quality are expected to grow. For example, the factor score of each branch regarding the thickness of the branch can be determined to be the highest when the thickness of the branch is within a predetermined range, and lower when it is larger or smaller than the predetermined range. This is because if the branch is too thin, the productivity of the fruit may be poor, and if the branch is too thick, the quality of the fruit may decrease. A specific example of a method (for example, evaluation criteria) for determining the factor score for each attribute of each branch will be described later.

[0323] In step S240l, for each of two or more branches, a total score Ts is calculated based on the factor scores for each of one or more attributes determined in step S240k. The total score Ts for each branch may be the sum of the factor scores for each of one or more attributes (when there is one attribute, that factor score is used as the total score Ts).

[0324] In step S240m, among two or more branches, the branch with the highest total score Ts calculated in step S240l is determined as the branch to be retained. The branch with the highest total score Ts can be selected as the branch to be retained (for example, the resulting mother branch).

[0325] If the factor scores for each attribute of each branch are determined such that the higher the factor scores, the more favorable the state of the branch as the resulting mother branch with respect to that attribute, then the higher the total score Ts obtained by adding these up, the more favorable the branch is considered as the resulting mother branch. By selecting the branch with the highest total score Ts as the resulting mother branch, among two or more branches, the branch on which it is expected that the highest-quality fruits will bear fruit in that season or the next season can be selected as the resulting mother branch, so that the automation of pruning work can be promoted while maintaining the fruit yield and quality.

[0326] In step S240n, in addition to the branch with the highest total score Ts calculated in step S240l, it is determined whether to also determine the second-highest branch as the branch to be retained, based on, for example, user input. For example, when retaining a preliminary resulting mother branch in addition to the resulting mother branch, in addition to the branch with the highest total score Ts, the branch with the second-highest total score Ts is also determined as the branch to be retained. The branch with the second-highest total score Ts is likely to be the second-most favorable branch as the resulting mother branch. By selecting the branch with the second-highest total score Ts as the preliminary resulting mother branch, the automation of pruning work can be promoted while maintaining the fruit yield and quality.

[0327] In step S240n, when all of the total scores Ts of two or more branches grouped into the same group are lower than a predetermined value, it may be determined that the branch with the second highest total score Ts is not a branch to be left. That is, it may be determined that only one branch (i.e., only the branch with the highest total score Ts) is to be left. In this case, for example, it corresponds to not leaving the preliminary result mother branch. When all of the total scores Ts of two or more branches grouped into the same group are lower than a predetermined value, by not leaving the preliminary result mother branch, the nutrient state of the result mother branch can be improved, and a decrease in the fruit harvest amount and quality can be suppressed.

[0328] When all of the total scores Ts of two or more branches grouped into the same group are lower than a predetermined value, in generating the cut point data (step S300), the cut point data may be generated such that the number of buds remaining on the branch determined to be the branch to be left is less than the value set by user input or the like.

[0329] If the answer is Yes in step S240n, in step S240p, among two or more branches grouped into the same group, the branch with the second highest total score Ts is also determined to be a branch to be left. After step S240p, the process proceeds to step S240q. If the answer is No in step S240n, the process also proceeds to step S240q.

[0330] In step S240q, among two or more branches grouped into the same group, all branches other than the branches determined to be the branches to be left are determined to be branches to be removed.

[0331] Note that the process of step S240 is not limited to the above example. For example, even when there is only one branch grouped into the same group among a plurality of groups, based on the total score calculated as described above, a decision may be made as to whether to remove or keep the branch to be removed. For example, if the total score is lower than a predetermined value, it may be determined as the branch to be removed, and if the total score is equal to or higher than the predetermined value, it may be determined as the branch to be kept. Further, the number of buds remaining on the branches determined to be kept may be determined so that the number of buds remaining on the branches to be kept is less than the value set by the user input or the like.

[0332] FIG. 12 is a flowchart showing an example of a procedure for generating cutting point data of fruit tree branches according to an embodiment of the present disclosure. The flowchart of FIG. 12 differs from the flowchart of FIG. 9B in that it does not have step S220 (grouping). Depending on the pruning method and tailoring method of the fruit tree, the grouping process may not be necessary.

[0333] The processing in each step of FIG. 12 is performed in the same manner as in the example of FIG. 9B. However, in the example of FIG. 9B, the processing performed on one or more branches grouped into the same group among a plurality of groups in step S220 is, in the example of FIG. 12, performed on one or more branches that are the processing targets of step S200 (including step S230 and step S240).

[0334] [Setting of Attribute Priorities] The setting of attribute priorities will be described with reference to FIG. 13A.

[0335] FIG. 13A is a flowchart showing an example of a procedure for generating cutting point data of fruit tree branches according to an embodiment of the present disclosure. The flowchart of FIG. 13A differs from the flowchart of FIG. 4A in that it has step S230, step S250, and step S252 as the processing performed in step S200. The example of FIG. 13A can be combined with any of the above-described flowcharts.

[0336] In the example of FIG. 13A, the procedure for generating cutting point data of a fruit tree branch includes obtaining sensor data acquired by one or more sensors, which is sensor data of one or more branches of the fruit tree (step S100); obtaining measured values regarding two or more attributes for each of the one or more branches based on the sensor data (step S230); obtaining information on the priorities of the two or more attributes (step S250); determining, based on the measured values and the priorities, for each of the one or more branches whether it is a branch to be removed or a branch to be retained (step S252); and generating cutting point data including information indicating the three-dimensional position of the point to be cut for each of the branches determined to be branches to be removed (step S300).

[0337] The process of step S100 is performed in the same manner as the process in the example of FIG. 4A.

[0338] The process of step S230 is performed in the same manner as the process in the example of FIG. 9B.

[0339] In step S250, information on the priorities of two or more attributes is obtained. The priority information is obtained, for example, based on user input. Note that the order of step S250 and step S230 does not matter. Step S250 may be performed simultaneously (in parallel) with step S230.

[0340] In step S252, based on the measurement values obtained in step S230 and the priorities obtained in step S250, it is determined for each of one or more branches to be processed whether it is a branch to be removed or a branch to be retained. Step S252 will be described with reference to FIGS. 13B, 14A, 14B, and 14C. FIG. 13B is a flowchart showing a specific example of the process performed in step S252. FIGS. 14A, 14B, and 14C are diagrams for explaining an example of the process performed in step S252. In the example of FIG. 13B, the case where there are two or more branches to be processed is described. As will be described below, in the example of FIG. 13B, based on the factor scores regarding each attribute of each branch and the priorities of each attribute, the total score of each branch is calculated, and based on the calculated total scores, for each of two or more branches to be processed, it is determined whether it is a branch to be removed or a branch to be retained.

[0341] As shown in FIG. 13B, in step S252a, for each of two or more branches to be processed, for each of two or more attributes, based on the measurement values obtained in step S230, factor scores are determined. For example, FIG. 14A shows an example of obtaining and evaluating measurement values regarding six attributes A to F for six branches 58_1 to 58_6 grouped in the same group. Specifically, for each of the six branches 58_1 to 58_6, factor scores F A ~F F are determined for each of the six attributes A to F. Among the blank cells in the table of FIG. 14A, the factor scores F A ~F F are determined in step S252a, and the total score Ts is determined in step S252b described below. Note that the number of branches to be processed and the number of attributes are merely examples and are not limited thereto. The process of step S252a can be performed in the same manner as the process of step S240k in the example of FIG. 11B. Also in step S252a, the factor scores regarding each attribute of each branch can be determined such that the higher the score, the more preferable the state of the branch as the result parent branch regarding that attribute.

[0342] In step S252b, for each of two or more branches to be processed, a total score Ts is calculated based on the factor scores for each of the two or more attributes determined in step S252a and the priority information obtained in step S250.

[0343] FIG. 14B is a diagram for explaining an example of calculating a total score Ts for each branch based on the factor scores for the respective attributes and the priorities of the respective attributes. For example, in the example of FIG. 14B, the priorities (order of priority) are set in the order of attribute D, attribute A, attribute B, attribute C, attribute E, and attribute F from the highest priority. In the example of FIG. 14B, the total score Ts for each branch is the priority weight W P multiplied by the factor score for each attribute and the values are added together. The priority weight W P An example of the value is shown in FIG. 14B, but it is not limited to this example. As long as the priority weight W P is set to increase as the priority increases. Also, the method of calculating the total score Ts is not limited to this example. For example, the total score Ts can be calculated based on the factor scores for the respective attributes corrected according to the priorities of the attributes.

[0344] The priorities of the respective attributes are not limited to the case where different ranks are assigned to all the attributes as in the example of FIG. 14B. Referring to FIGS. 14D and 14E, another example will be described. For example, as in the example of FIG. 14D, among the six attributes, the same rank may be assigned to two or more attributes. In such a case, for the two or more attributes with the same rank (attributes A and B in the example of FIG. 14D), the same value of the priority weight W Pis applied. In the example of FIG. 14D, for the sake of convenience in expressing the calculation formula of the total score Ts, different numbers are assigned to attribute A as the second and attribute B as the third, but the priorities of attribute A and attribute B are both the second. Also, as in the example of FIG. 14E, in calculating the total score Ts, there may be a way of setting the priority such that among the six attributes, the factor scores regarding one or more attributes are not included. In such a case, for the attribute (attribute A in the example of FIG. 14E) that is set not to be included in the calculation of the total score Ts, the priority weight W P is set to zero. The examples of FIGS. 14D and 14E may be combined.

[0345] Regarding the total score Ts of each branch calculated in step S252b, when there is only one branch having the highest total score Ts among two or more branches to be processed (Yes in step S252c), in step S252d, the branch having the highest total score Ts is determined as the remaining branch. Among the six branches 58_1 to 58_6, the branch having the highest total score Ts can be set as the remaining branch (for example, the result mother branch).

[0346] When there are a plurality of branches having the highest total score Ts among two or more branches to be processed (No in step S252c), in step S252g, among the branches having the highest total score Ts, the branch having the highest factor score regarding the attribute with the highest priority is determined as the remaining branch. For example, when the result of the total score Ts calculated in the example of FIG. 14B is as shown in the table of FIG. 14C, among the six branches 58_1 to 58_6, branches 58_1 and 58_6 have the highest total score Ts. Among branches 58_1 and 58_6, the branch 58_6 having a higher factor score F D regarding attribute D, which is the attribute with the highest priority, can be set as the remaining branch (for example, the result mother branch). Note that the values of the factor scores and the total score Ts shown in FIG. 14C are merely examples, and only the values in some parts are shown.

[0347] After step S252d, in step S252e, in addition to the branch with the highest total score Ts, it is determined whether to select the second-highest scoring branch as a branch to be retained, for example, based on user input. For example, if the preliminary result main branch is also to be retained, the branch with the second-highest total score Ts is also determined to be a branch to be retained. In step S252e, if the total scores Ts of all of the two or more branches to be processed are lower than a predetermined value, it may be determined that the branch with the second-highest total score Ts is not a branch to be retained, and that only one branch (i.e., only the branch with the highest total score Ts) is to be the branch to be retained. In this case, for example, it corresponds to not retaining the preliminary result main branch.

[0348] If the total scores Ts of all of the two or more branches to be processed are lower than a predetermined value, in generating cut point data (step S300), the cut point data may be generated such that the number of buds remaining on the branch determined to be the branch to be retained is less than a value set by user input or the like.

[0349] If the result in step S252e is Yes, in step S252f, among the two or more branches to be processed, the branch with the second-highest total score Ts is also determined to be a branch to be retained. After step S252f, the process proceeds to step S252j. Even if the result in step S252e is No, the process proceeds to step S252j.

[0350] In step S252h as well, the same determination as in step S252e is made. However, here, since there are a plurality of branches having the highest total score Ts, in addition to the branch already determined to be the branch to be retained in step S252g, it is determined whether to also select another branch as a branch to be retained.

[0351] If the answer is Yes in step S252h, in step S252i, among two or more branches to be processed, the branch with the second highest factor score regarding the attribute with the highest priority among the branches having the highest total score Ts is determined as the branch to be retained. For example, in the example of FIG. 14C, among the six branches 58_1 to 58_6, among the branches 58_1 and 58_6 having the highest total score Ts, the branch 58_1 with the second highest factor score F D regarding the attribute D which is the attribute with the highest priority is determined as the branch to be retained. Here, it is preferable that the factor scores regarding each attribute are determined such that two or more branches to be processed have different factor scores from each other. For example, among the six branches 58_1 to 58_6, it is preferable that the factor scores regarding each attribute are different from each other. If the factor scores regarding each attribute are different among the six branches 58_1 to 58_6, when there are a plurality of branches having the same total score Ts, the factor score F D regarding the attribute D with the highest priority can be used to determine superiority or inferiority. When there are two or more attributes with the highest priority (that is, when the highest priority in the same rank is set for two or more attributes), superiority or inferiority can be determined by the sum of the factor scores regarding the two or more attributes with the highest priority. Such a method for determining factor scores will be described later.

[0352] After step S252i, the process proceeds to step S252j. Even if the answer is No in step S252h, the process proceeds to step S252j.

[0353] In step S252j, among two or more branches to be processed, all branches other than the branch determined as the branch to be retained are determined as branches to be removed.

[0354] Based on the determination in step S252, the process of step S300 is performed. The process of step S300 is performed in the same manner as the process in the example of FIG. 4A or FIG. 8.

[0355] After step S300, step S400 may further be included as in the example of FIG. 4B. That is, the cutting point data generated in step S300 may be input to a control device that controls the three-dimensional position of a cutter (for example, the cutting tool 24 included in the cutting system 1 of FIG. 1) that cuts the branches of the fruit tree. In this way, the cutter can be made to cut the branches of the fruit tree.

[0356] In the example described with reference to FIG. 11B, the branches to be left are determined based on the total score Ts calculated based on the factor scores for each attribute of each branch. On the other hand, in the examples of FIGS. 13A and 13B, the total score Ts is calculated based on the factor scores for each attribute and the information on the priority of each attribute, and the branches to be left are determined based on the total score Ts. Typically, the branch with the highest total score Ts is selected as the result mother branch. The priority of the attributes in selecting the result mother branch can be set and reflected in the total score Ts. For example, it can be customized according to the variety of the fruit tree, the conditions of the orchard, the vineyard design, the preferences of the user, etc. It can correspond to various fruit tree varieties and orchard conditions. Also, when there are a plurality of branches having the same total score Ts when selecting the branches to be left, the superiority and inferiority can be determined based on the priority of the attributes.

[0357] FIG. 13C is a flowchart showing an example of a procedure for generating cutting point data of the branches of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 13C differs from the flowchart of FIG. 13A in that it further includes step S220 (grouping).

[0358] In step S220 of FIG. 13C, based on the sensor data acquired in step S100, a plurality of branches 58 of the fruit tree are grouped into a plurality of groups. The process of step S220 is performed in the same manner as the process of step S220 in FIG. 9A.

[0359] The processing in each step other than step S220 in FIG. 13C is performed in the same manner as in the example of FIG. 13A. However, in the example of FIG. 13A, the processing performed on one or more branches to be processed is, in the example of FIG. 13C, performed on one or more branches grouped into the same group among a plurality of groups in step S220.

[0360] FIG. 13D is a flowchart showing another example of the processing performed in step S252. In the example of FIG. 13D, the case where there are one or more branches to be processed is described.

[0361] In step S252k of FIG. 13D, for each of the one or more branches to be processed, a factor score is determined based on the measurement value obtained in step S230 for each of two or more attributes. In step S252l, for each of the one or more branches to be processed, a total score Ts is calculated based on the factor score for each of the two or more attributes determined in step S252k and the priority information obtained in step S250. Steps S252k and S252l can be performed in the same manner as steps S252a and S252b in FIG. 13B.

[0362] In step S252m, it is determined whether a branch for which the total score Ts calculated in step S252l is equal to or greater than a predetermined value is included in one or more branches to be processed. If all of the total scores Ts of the one or more branches to be processed are lower than the predetermined value (No in step S252m), then in step S252p, it is determined that only one branch (for example, only the branch with the highest total score Ts) is to be left. For example, this corresponds to not leaving the preliminary result mother branch. Further, in step S252q, the number of buds remaining on the branches determined to be the branches to be left is determined so that the number of buds remaining on the branches to be left is less than the value set by the user input or the like. If a branch for which the total score Ts is equal to or greater than the predetermined value is included in one or more branches to be processed (Yes in step S252m), then in step S252n, two or more branches to be left are determined from among the one or more branches to be processed. For example, it is determined that the branch with the highest total score Ts and the branch with the second highest total score Ts are the branches to be left. The method for determining the branches to be left may be the example described above.

[0363] In step S252r, among the one or more branches to be processed, all branches other than the branches determined to be the branches to be left are determined to be branches to be removed.

[0364] Note that the process of step S252 is not limited to the examples of FIGS. 13B and 13D. For example, even when there is only one branch to be processed (for example, a branch grouped in the same group among a plurality of groups), based on the total score calculated as described above, a determination may be made as to whether the branch is to be a branch to be removed or a branch to be left. For example, if the total score of the branch is lower than the predetermined value, the branch may be determined to be a branch to be removed, and if the total score of the branch is equal to or greater than the predetermined value, the branch may be determined to be a branch to be left. The predetermined value here may be set to a value lower than the predetermined value used in step S252m of FIG. 13D, for example.

[0365] [Method for determining branches to be removed / branches to be left: Classification and ranking] Referring to FIGS. 15A and 15B, an example of a method for determining whether to remove or retain branches based on the classification and ranking results of two or more branches to be processed will be described.

[0366] FIGS. 15A and 15B are flowcharts showing an example of a procedure for generating branch cutting point data of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 15A is a modified example of the flowchart of FIG. 12, and is different from the flowchart of FIG. 12 in that it has steps S244, S246, and S248 instead of step S240 in the example of FIG. 12. The flowchart of FIG. 15B is different from the flowchart of FIG. 15A in that the order of steps S244 and S246 is reversed. The examples of FIGS. 15A and 15B can be combined with any of the above-described flowcharts.

[0367] In the example of FIG. 15A, the procedure for generating branch cutting point data of a fruit tree is sensor data acquired by one or more sensors, and includes acquiring sensor data of two or more branches of the fruit tree (step S100); acquiring measurement values regarding one or more attributes for each of the two or more branches based on the sensor data (step S230); classifying each of the two or more branches into any of a plurality of classes indicating evaluation criteria for the attribute based on the measurement values for each of the one or more attributes (step S244); ranking the two or more branches differently from each other based on the measurement values for each of the one or more attributes (step S246); determining, based on the classes and ranks of the two or more branches, whether to remove or retain each of the two or more branches (step S248); and generating cutting point data including information indicating the three-dimensional position of the point to be cut for each of the branches determined to be branches to be removed (step S300).

[0368] The process of step S100 is performed in the same manner as the process in the example of FIG. 4A.

[0369] The process of step S230 is performed in the same manner as the process in the example of FIG. 9B or FIG. 12.

[0370] In step S244, for each of one or more attributes, each of two or more branches to be processed is classified into any one of a plurality of classes indicating the evaluation criteria for the attribute based on the measurement value obtained in step S230. A plurality of classes are predetermined based on the evaluation criteria for each attribute. A plurality of the two or more branches to be processed may be classified into the same class.

[0371] In step S246, for each of one or more attributes, different ranks are assigned to two or more branches to be processed based on the measurement value obtained in step S230.

[0372] Steps S244 and S246 may be performed independently. As in the example shown in FIGS. 15A and 15B, the order of steps S244 and S246 may be either first. Steps S244 and S246 may be performed simultaneously (in parallel).

[0373] FIG. 16A is a schematic diagram for explaining an example of the processes performed in step S244 and step S246. Table T3a in FIG. 16A shows the measured values regarding attribute x obtained for five branches a to e, which are two or more branches to be processed. Table T3b shows three classes regarding attribute x and their respective evaluation criteria. Table T3c shows the ranking result regarding attribute x and the classifying result regarding attribute x for the five branches a to e. In step S244, based on the measured values regarding attribute x (Table T3a) obtained in step S230 and the evaluation criteria of attribute x (Table T3b), each of branches a to e is classified into one of the three classes of attribute x, "Good", "Okay", and "Bad" (Table T3c). In step S246, based on the measured values regarding attribute x (Table T3a) obtained in step S230, rankings are assigned in descending order of the measured values (Table T3c). Here, it is assumed that the higher the measured value regarding attribute x, the more preferable it is as the result mother branch. Also, all the numerical values, classes, etc. in FIG. 16A are merely illustrative.

[0374] In step S248, based on the classes classified in step S244 and the rankings assigned in step S246, it is determined for each of the two or more branches to be processed whether it is a branch to be removed or a branch to be retained. In the example of FIG. 16A, based on Table T3c, it is determined for each of branches a to e whether it is a branch to be removed or a branch to be retained.

[0375] The ranking in step S246 corresponds to performing a relative evaluation of two or more branches to be processed, and the classification in step S244 corresponds to performing an absolute evaluation of two or more branches to be processed. The ranking in step S246 and the classification in step S244 are performed for each attribute. By only performing the ranking (relative evaluation) of two or more branches to be processed, it is possible to select the branches to be left from among the two or more branches to be processed. For example, the branch with the highest rank can be selected as the result mother branch. However, with only ranking (relative evaluation), although it is possible to select the most preferable branch from among two or more branches to be processed, it is not possible to evaluate whether each branch is in a state suitable as a result mother branch. For example, even if all of the two or more branches to be processed are not in a state suitable as a result mother branch, when only ranking (relative evaluation) is performed, the branch with the highest rank will uniformly be selected as the branch to be left, so it may not be possible to suppress a decrease in the fruit yield and quality. Also, with only classification (absolute evaluation), when multiple branches are classified into the same class, their superiority or inferiority cannot be determined. There may be cases where it is inferior from the perspective of improving the efficiency of the pruning work. In contrast, in the examples of FIGS. 15A and 15B, based on the result of the ranking in step S246 and the result of the classification in step S244, each of the two or more branches to be processed is determined to be either a branch to be removed or a branch to be left, so the occurrence of the above problems can be suppressed. For example, the fact that all of the two or more branches to be processed are not in a state suitable as a result mother branch can be detected based on the result of the classification in step S244. In such a case, for example, all of the two or more branches to be processed can be determined to be branches to be removed. Also, the information on the result of the classification in step S244 can also be used as information for grasping the state of the entire fruit tree (for example, health state, tree vigor, etc.) and for planning and / or executing work other than pruning (for example, fertilization, irrigation, fruit picking, etc.).

[0376] Based on the determination in step S248, the process of step S300 is performed.

[0377] After step S300, step S400 may be further included as in the example of FIG. 4B. That is, the cut point data generated in step S300 may be input to a control device that controls the three-dimensional position of a cutter (for example, the cutting tool 24 included in the cutting system 1 of FIG. 1) that cuts the branches of fruit trees. In this way, the cutter can be made to cut the branches of fruit trees.

[0378] FIG. 15C is a flowchart showing an example of the process performed in step S246.

[0379] In the example of FIG. 15C, ranking in step S246 is performed based on the classification result in step S244.

[0380] In step S244, when two or more branches to be processed are classified into different classes for each of one or more attributes (Yes in step S246a), in step S246b, different ranks are assigned to the two or more branches to be processed based on the classes classified in step S244.

[0381] In step S244, when there are two or more branches classified into the same class among a plurality of classes for each of one or more attributes (No in step S246a), in step S246c, a relative evaluation of the branches classified into the same class is performed.

[0382] In step S246d, different ranks are assigned to the two or more branches to be processed based on the classes classified in step S244 and the result of the relative evaluation in step S246c.

[0383] For example, in the example of FIG. 16A, after classifying five branches a to e, a relative evaluation may be performed only for branches a, b, d, and e classified into the same class "Bad". Different ranks are assigned to branches a to e based on the result of the relative evaluation and the result of the classification.

[0384] FIG. 16B is a schematic diagram for explaining another example of the processes performed in step S244 and step S246. Similar to the table T3a in FIG. 16A, it is assumed that measurement values regarding the attribute x are acquired for five branches a to e, which are two or more branches to be processed.

[0385] FIG. 15D is a flowchart showing another example of the process performed in step S246.

[0386] In step S246e, for each of one or more attributes, a score corresponding to the class classified in step S244 is assigned to each of two or more branches to be processed. A plurality of scores corresponding to a plurality of classes are predetermined for each attribute. The table T3d in FIG. 16B shows three classes regarding the attribute x and the scores corresponding to each class.

[0387] In step S244, for each of one or more attributes, when there are two or more branches classified into the same class among the plurality of classes (in the case of No in step S246f), in step S246h, a relative evaluation of the branches classified into the same class is performed. Step S246h can be performed in the same manner as step S246c in FIG. 15C.

[0388] In step S246i, for each of two or more branches, a factor score regarding each attribute is calculated by multiplying the score given in step S246e by a coefficient according to the result of the relative evaluation in step S246h. Table T3e in FIG. 16B shows an example of the coefficient according to the result of the relative evaluation in step S246h, and Table T3f shows the score Sx given regarding attribute x and the coefficient Wr applied for five branches a to e. Among the branches classified into the same class, the score Sx multiplied by the coefficient Wr(n) is applied to the branch whose relative evaluation is the n-th. The value of the coefficient Wr is not limited to the example described in FIG. 16B, and may be set so that it becomes larger as the result (rank) of the relative evaluation is higher. However, it is preferable that the value obtained by multiplying the coefficient Wr by the score Sx is a value that maintains the vertical relationship of the branches by the given score Sx. That is, it is preferable that the coefficient Wr is set so that the value obtained by multiplying the score Sx by the coefficient Wr is between the value of the score one higher and the value of the score one lower than that score. In the example of FIG. 16B, when the given score Sx is "2", it is preferable that the coefficient Wr is set so that the value of Wr×Sx is more than 1 and less than 3. When there is only one branch classified into the same class, the coefficient Wr = 1 is used for that branch (alternatively, the coefficient is not multiplied by the score). In the example of FIG. 16B, since the only branch classified into the class "Okay" is branch c, the coefficient Wr = 1 for branch c, and the factor score Fx(c) regarding attribute x for branch c is determined as the score "2" given in step S246e.

[0389] In step S244, when two or more branches to be processed are classified into different classes for each of one or more attributes (Yes in step S246f), in step S246g, the score given in step S246e is determined as the factor score for each branch regarding that attribute.

[0390] The scores of each attribute may be normalized so that the maximum values of the multiple scores of each attribute are equal among the attributes with a maximum value of 2 or more. In the example of FIG. 16B, in the calculation formula for calculating the factor score Fx(m) for attribute x of branch m, the value obtained by multiplying the assigned score Sx by the coefficient Wr is divided by 3, which corresponds to normalizing by the maximum value "3" of the score of attribute x. In particular, when determining the branches to be removed or retained using two or more attributes, such normalization enables the results of evaluations regarding two or more attributes to be handled in parallel. The method of calculation is not limited to the example of FIG. 16B. For example, the multiple scores of each attribute may be pre-normalized values. In the example of FIG. 16B, the scores associated with each of the three classes "Good", "Okay", and "Bad" of attribute x may be "1", "2 / 3", and "1 / 3", respectively.

[0391] FIG. 15E is a flowchart showing an example of a procedure for generating cutting point data of branches of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 15E is a modified example of the flowchart of FIG. 15A and is different from the flowchart of FIG. 15A in that it further has step S250 (acquisition of information on the priorities of two or more attributes).

[0392] In step S250 of FIG. 15E, information on the priorities of two or more attributes is acquired. The process of step S250 is performed in the same manner as the process of step S250 in FIG. 13A.

[0393] In step S248, based on the priorities of two or more attributes acquired in step S250, the classes classified in step S244, and the ranks assigned in step S246, it is determined for each of the two or more branches to be processed whether it is a branch to be removed or a branch to be retained.

[0394] In step S248, a total score Ts may be calculated based on the factor scores for each of the two or more attributes determined for each of the two or more branches and the priority information obtained in step S250. The factor scores can be determined by the same process as step S248 in FIG. 15A. By determining the factor scores for each attribute of each branch in the same manner as described with reference to FIG. 16B, the factor scores in the example of FIG. 14A will be determined. The calculation of the total score Ts based on the factor scores and the priority is performed by, for example, the same process as step S252b in FIG. 13B.

[0395] The flowcharts of FIGS. 15A, 15B, and 15E can be further modified in combination with the above-described flowcharts or processes. For example, based on the sensor data obtained in step S100, further grouping of the plurality of branches of the fruit tree into a plurality of groups may be performed. Such a process can be performed by the same process as step S220 in FIG. 9A. When further performing the process of grouping the plurality of branches of the fruit tree into a plurality of groups, in the examples of FIGS. 15A, 15B, and 15E, the process performed on the two or more branches to be processed is performed on the two or more branches grouped into the same group among the plurality of groups in step S220.

[0396] [Examples of Attribute Classification] Examples of the types of one or more attributes used in determining the branches to be retained will be described with reference to FIGS. 17A to 17C, FIGS. 18A, 18B, FIGS. 19A, 19B, FIGS. 20A, 20B, and FIGS. 21A to 21C. FIGS. 17A to 17C are diagrams schematically showing examples of attribute classification. FIGS. 18A, 18B, FIGS. 19A, 19B, FIGS. 20A, 20B, and FIGS. 21A to 21C are flowcharts showing an example of a procedure for generating cutting point data of the branches of a fruit tree according to an embodiment of the present disclosure, and are modified examples of the flowchart of FIG. 12.

[0397] (1) Attributes related to the buds of the branch and attributes other than the buds FIG. 18A is a flowchart showing an example of a procedure for generating cutting point data of a fruit tree branch according to an embodiment of the present disclosure. The flowchart of FIG. 18A is a modified example of the flowchart of FIG. 12, and is different from the flowchart of FIG. 12 in that it has step S232 instead of step S230 in FIG. 12. The example of FIG. 18A can be combined with any of the above-described flowcharts.

[0398] In the example of FIG. 18A, the procedure for generating the cutting point data of the fruit tree branch includes sensor data acquired by one or more sensors, and acquiring sensor data of one or more branches of the fruit tree (step S100); based on the sensor data, for each of the one or more branches, acquiring measurement values of two or more attributes including attributes related to the buds of the branch and attributes other than the buds (step S232); based on the measurement values, determining for each of the one or more branches whether it is a branch to be removed or a branch to be left (step S240); and for each of the branches determined to be branches to be removed, generating cutting point data including information indicating the three-dimensional position of the point to be cut (step S300).

[0399] The processing other than step S232 is performed in the same manner as the processing in the example of FIG. 9B or FIG. 12.

[0400] In step S232, based on the sensor data acquired in step S100, for each of the one or more branches 58 to be processed, measurement values of two or more attributes including attributes related to the buds of the branch and attributes other than the buds are acquired. In the example of FIG. 17A, they are classified into attributes related to the buds of the branch and attributes other than the buds. As shown in FIG. 17A, the attributes other than the buds include at least one of the color of the branch, the direction in which the branch extends, the thickness of the branch, the height of the base of the branch, and the length of the branch. The attributes related to the buds of the branch include at least one of the size of the buds of the branch, the direction in which the buds of the branch face, and the length of the internodes of the branch.

[0401] In step S240, based on the measurement values obtained in step S232, for each of one or more branches to be processed, it is determined whether to make a branch to be removed or a branch to be left. The method for determining whether to remove a branch or leave a branch may be the same as the example described above. For example, it is performed in the same manner as the process of step S240 in the example of FIG. 9B.

[0402] For each branch, by determining whether to make a branch to be removed or a branch to be left based on the measurement values obtained for two or more attributes including the attributes related to the buds of the branch and the attributes other than the buds, a preferable branch as a result mother branch can be selected in consideration of the attributes related to the buds of the branch and the attributes other than the buds, which can lead to an improvement in the fruit yield and quality.

[0403] Based on the determination in step S240, the process of step S300 is performed.

[0404] After step S300, step S400 may further be included as in the example of FIG. 4B. That is, the cutting point data generated in step S300 may be input to a control device that controls the three-dimensional position of a cutter (for example, the cutting tool 24 included in the cutting system 1 of FIG. 1) for cutting the branches of the fruit tree. In this way, the cutter can be made to execute the cutting of the branches of the fruit tree.

[0405] FIG. 18B is a flowchart showing an example of a procedure for generating cutting point data of a branch of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 18B is different from the flowchart of FIG. 18A in that it further includes step S220 (grouping).

[0406] In step S220 of FIG. 18B, based on the sensor data obtained in step S100, a plurality of branches 58 of the fruit tree are grouped into a plurality of groups. The process of step S220 is performed in the same manner as the process of step S220 in FIG. 9A. The processes in each step other than step S220 in FIG. 18B are performed in the same manner as the example in FIG. 21A. However, in the example of FIG. 18A, the processes performed on one or more branches to be processed are, in the example of FIG. 18B, performed on one or more branches grouped into the same group among the plurality of groups in step S220.

[0407] The flowcharts of FIGS. 18A and 18B can be further modified in combination with the above-described flowcharts or processes. For example, it may be further performed to obtain information on the priorities of two or more attributes and to determine, based on the priorities and the measured values obtained in step S232, whether each of one or more branches is to be a branch to be removed or a branch to be left. Such a process can be performed by a process similar to steps S250 and S252 in FIGS. 13A and 13B.

[0408] (2) Attributes whose evaluation criteria vary depending on the cultivation method and attributes whose evaluation criteria do not change depending on the cultivation method FIG. 19A is a flowchart showing an example of a procedure for generating cutting point data of branches of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 19A is a modified example of the flowchart of FIG. 12 and is different from the flowchart of FIG. 12 in that it has steps S270 and S234 instead of step S230 in the example of FIG. 12. The example of FIG. 19A can be combined with any of the above-described flowcharts.

[0409] In the example of FIG. 19A, the procedure for generating branch cutting point data of a fruit tree includes: obtaining sensor data acquired by one or more sensors, the sensor data including information indicating the three-dimensional structure of one or more branches of the fruit tree (step S100); obtaining information on the cultivation method of the fruit tree (step S270); based on the sensor data, for each of the one or more branches, obtaining measurement values regarding one or more attributes including attributes with different evaluation criteria according to the cultivation method (step S234); based on the measurement values, determining for each of the one or more branches whether it is a branch to be removed or a branch to be left (step S240); and for each of the branches determined to be branches to be removed, generating cutting point data including information indicating the three-dimensional position of the point to be cut (step S300).

[0410] The processes other than step S270 and step S234 are performed in the same manner as the processes in the example of FIG. 9B or FIG. 12. In the example of FIG. 19A, in step S234, when obtaining measurement values regarding one or more attributes including attributes with different evaluation criteria according to the cultivation method, since sensor data including information indicating the three-dimensional structure of the branches of the fruit tree is required, in step S100, sensor data including information indicating the three-dimensional structure of the branches of the fruit tree is obtained.

[0411] In step S270, information on the cultivation method of fruit trees is acquired. The information on the cultivation method of fruit trees includes, for example, at least one piece of information (e.g., type) such as the shape of the trellis system of the fruit trees, the pruning method of the fruit trees, and the vine training method of the fruit trees. The information on the cultivation method of fruit trees may include information on field design. When the fruit tree is a grapevine, the information on the cultivation method of the grapevine may include information on vineyard design. The field design and the vineyard design can be determined by at least one element of the shape of the trellis system, the pruning method, and the vine training method. The information on the cultivation method of fruit trees may be acquired based on the user's input or based on the sensor data of the fruit trees. For example, based on the image data of the fruit trees acquired by the imaging device (camera 20), the information on the cultivation method of the fruit trees may be acquired. It may also be acquired based on the sensor data including the information indicating the three-dimensional structure of the fruit trees.

[0412] In step S234, based on the sensor data acquired in step S100, for each of the one or more branches 58 to be processed, measurement values regarding one or more attributes including attributes with different evaluation criteria according to the cultivation method are acquired. In the example of FIG. 17B, the attributes are classified into attributes with different evaluation criteria according to the cultivation method of the fruit trees and attributes with evaluation criteria that do not change according to the cultivation method of the fruit trees. When the fruit tree is a grapevine, the attributes with different evaluation criteria according to the cultivation method of the fruit trees are attributes with different evaluation criteria according to the information on vineyard design, and the attributes with evaluation criteria that do not change according to the cultivation method of the fruit trees are attributes with evaluation criteria that do not change according to the information on vineyard design. As shown in FIG. 17B, the attributes with different evaluation criteria according to the cultivation method of the fruit trees include, for example, at least one of the direction in which the branch extends, the height of the base of the branch, the direction in which the buds on the branch face, the length of the branch, and the length of the internodes of the branch.

[0413] The above-mentioned one or more attributes may be two or more attributes including attributes with different evaluation criteria according to the cultivation method and attributes with unchanging evaluation criteria according to the cultivation method. As shown in FIG. 17B, the attributes with unchanging evaluation criteria according to the cultivation method of fruit trees include, for example, at least one of the color of the branches, the thickness of the branches, and the size of the buds on the branches.

[0414] In step S240, based on the measured values obtained in step S234, for each of the one or more branches to be processed, it is determined whether to make it a branch to be removed or a branch to be retained. The method for determining whether to remove a branch or retain a branch may be the same as the above-described example. For example, by performing the same processing as the processing from step S240k to step S240q shown in FIG. 11B, for each of the one or more branches to be processed, for each of the one or more attributes, a factor score is determined based on the measured values obtained in step S234, and the branches to be retained are determined based on the factor score. As described above, the factor score for each attribute of each branch can be determined such that the higher the factor score, the more favorable the branch is as a resultant mother branch with respect to that attribute. However, for attributes with different evaluation criteria according to the cultivation method, the favorable state as a resultant mother branch changes depending on the cultivation method of the fruit tree. Therefore, the factor scores for attributes with different evaluation criteria according to the cultivation method of the fruit tree are determined to be different according to the cultivation method of the fruit tree. For attributes with unchanging evaluation criteria according to the cultivation method, since the favorable state as a resultant mother branch does not change depending on the cultivation method of the fruit tree, the factor scores for these attributes are determined not to be different according to the cultivation method of the fruit tree.

[0415] Based on the determination in step S240, the processing of step S300 is performed.

[0416] After step S300, step S400 may further be included as in the example of FIG. 4B. That is, the cutting point data generated in step S300 may be input to a control device that controls the three-dimensional position of a cutter (for example, the cutting tool 24 included in the cutting system 1 of FIG. 1) that cuts the branches of the fruit tree. In this way, the cutter can be made to cut the branches of the fruit tree.

[0417] FIG. 19B is a flowchart showing an example of a procedure for generating cutting point data of a branch of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 19B differs from the flowchart of FIG. 19A in that it further includes step S220 (grouping).

[0418] In step S220 of FIG. 19B, based on the sensor data acquired in step S100, a plurality of branches 58 of the fruit tree are grouped into a plurality of groups. The process of step S220 is performed in the same manner as the process of step S220 in FIG. 9A. The processes in each step other than step S220 in FIG. 19B are performed in the same manner as in the example of FIG. 19A. However, in the example of FIG. 19A, the process performed on one or more branches that are the processing targets is, in the example of FIG. 19B, performed on one or more branches grouped into the same group among the plurality of groups in step S220.

[0419] The flowcharts of FIGS. 19A and 19B can be further modified in combination with the above-described flowchart or process. For example, it may further be performed to acquire information on the priorities of two or more attributes and to determine, based on the priorities and the measured values acquired in step S234, for each of one or more branches whether to be a branch to be removed or a branch to be left. Such a process can be performed by a process similar to steps S250 and S252 in FIGS. 13A and 13B.

[0420] FIG. 20A is a flowchart showing an example of a procedure for generating cutting point data of a branch of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 20A is a modified example of the flowchart of FIG. 12, and is different from the flowchart of FIG. 12 in that it has step S236 instead of step S230 in the example of FIG. 12. The example of FIG. 20A can be combined with any of the above-described flowcharts.

[0421] In the example of FIG. 20A, the procedure for generating cutting point data of a branch of a fruit tree includes obtaining sensor data acquired by one or more sensors, the sensor data including information indicating the three-dimensional structure of one or more branches of the fruit tree (step S100); obtaining measurement values regarding two or more attributes including attributes whose evaluation criteria differ according to the cultivation method of the fruit tree and attributes whose evaluation criteria do not change depending on the cultivation method of the fruit tree, for each of the one or more branches, based on the sensor data (step S236); determining, based on the measurement values, for each of the one or more branches, whether it is a branch to be removed or a branch to be left (step S240); and generating cutting point data including information indicating the three-dimensional position of the point to be cut, for each of the branches determined to be branches to be removed (step S300).

[0422] The processing other than step S236 is performed in the same manner as the processing in the example of FIG. 9B or FIG. 12. In the example of FIG. 20A, in step S236, when obtaining measurement values regarding two or more attributes including attributes whose evaluation criteria differ according to the cultivation method of the fruit tree and attributes whose evaluation criteria do not change depending on the cultivation method of the fruit tree, sensor data including information indicating the three-dimensional structure of the branch of the fruit tree is required. Therefore, in step S100, sensor data including information indicating the three-dimensional structure of the branch of the fruit tree is obtained.

[0423] In step S236, based on the sensor data obtained in step S100, for each of one or more branches 58 to be processed, measurement values regarding two or more attributes are acquired, including attributes whose evaluation criteria vary according to the fruit tree cultivation method and attributes whose evaluation criteria do not change according to the fruit tree cultivation method. As shown in FIG. 17B, attributes whose evaluation criteria vary according to the fruit tree cultivation method include, for example, at least one of the direction in which the branch extends, the height of the base of the branch, the direction in which the buds on the branch face, the length of the branch, and the length of the internodes of the branch. As shown in FIG. 17B, attributes whose evaluation criteria do not change according to the fruit tree cultivation method include, for example, at least one of the color of the branch, the thickness of the branch, and the size of the buds on the branch.

[0424] In step S240, based on the measurement values obtained in step S236, for each of one or more branches to be processed, it is determined whether to make each branch a branch to be removed or a branch to be retained. The method for determining whether to remove a branch or retain a branch may be the same as the example described above. For example, by performing the same processing as the processing from step S240k to step S240q shown in FIG. 11B, for each of one or more branches to be processed, for each of one or more attributes, a factor score may be determined based on the measurement values obtained in step S234, and the branches to be retained may be determined based on the factor scores. As described above, the factor score for each attribute of each branch can be determined such that the higher the factor score, the more favorable the state of the branch as a result mother branch with respect to that attribute. However, for attributes whose evaluation criteria vary according to the fruit tree cultivation method, the favorable state as a result mother branch varies depending on the fruit tree cultivation method. Therefore, the factor scores for attributes whose evaluation criteria vary according to the fruit tree cultivation method are determined to vary according to the fruit tree cultivation method. For attributes whose evaluation criteria do not change according to the cultivation method, since the favorable state as a result mother branch does not change according to the fruit tree cultivation method, the factor scores for these attributes are determined not to vary according to the fruit tree cultivation method.

[0425] Based on the determination in step S240, the process of step S300 is performed.

[0426] After step S300, step S400 may further be included as in the example of FIG. 4B. That is, the cutting point data generated in step S300 may be input to a control device that controls the three-dimensional position of a cutter (for example, the cutting tool 24 included in the cutting system 1 of FIG. 1) that cuts the branches of the fruit tree. In this way, the cutter can be made to cut the branches of the fruit tree.

[0427] It may further include acquiring information on the cultivation method of the fruit tree. The acquisition of information on the cultivation method of the fruit tree can be performed by the same process as step S270 in FIG. 19A.

[0428] FIG. 20B is a flowchart showing an example of a procedure for generating cutting point data of branches of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 20B differs from the flowchart of FIG. 20A in that it further has step S220 (grouping).

[0429] In step S220 of FIG. 20B, a plurality of branches 58 of the fruit tree are grouped into a plurality of groups based on the sensor data acquired in step S100. The process of step S220 is performed in the same manner as the process of step S220 in FIG. 9A. The processes in each step other than step S220 in FIG. 20B are performed in the same manner as in the example of FIG. 20A. However, in the example of FIG. 20A, the processes performed on one or more branches that are the processing targets are, in the example of FIG. 20B, performed on one or more branches grouped into the same group in step S220.

[0430] The flowcharts of FIGS. 20A and 20B can be further modified in combination with the above-described flowcharts or processes. For example, it may further include acquiring information on the priorities of two or more attributes, and determining for each of one or more branches whether to be a branch to be removed or a branch to be left based on the priorities and the measured values acquired in step S236. Such a process can be performed by the same process as steps S250 and S252 in FIGS. 13A and 13B.

[0431] (3) Attributes related to the vigor of fruit trees Figure 21A is a flowchart showing an example of a procedure for generating cutting point data of branches of a fruit tree according to an embodiment of the present disclosure. The flowchart of Figure 21A is a modified example of the flowchart of Figure 12, and is mainly different from the flowchart of Figure 12 in that it has step S238 instead of step S230 in the example of Figure 12 and further has step S280. The example of Figure 21A can be combined with any of the above-described flowcharts.

[0432] In the example of Figure 21A, the procedure for generating cutting point data of branches of a fruit tree includes sensor data acquired by one or more sensors, acquiring sensor data of one or more branches of the fruit tree (step S100), based on the sensor data, for each of the one or more branches, acquiring measurement values of one or more attributes including attributes related to the vigor of the fruit tree (step S238), based on the measurement values, determining for each of the one or more branches whether it is a branch to be removed or a branch to be left (step S240), based on the measurement values, determining the number of buds to be left on the branches determined to be branches to be left (step S280), for each of the branches determined to be branches to be removed, generating cutting point data including information indicating the three-dimensional position of the point to be cut (step S302), and based on the number of buds to be left, generating cutting point data for each of the branches determined to be branches to be left (step S304).

[0433] The process of step S100 is performed in the same manner as the process in the example of Figure 4A.

[0434] In step S238, based on the sensor data obtained in step S100, for each of one or more branches 58 to be processed, measurement values regarding one or more attributes including attributes related to the tree vigor of the fruit tree are acquired. In the example of FIG. 17C, they are classified into attributes related to the tree vigor of the fruit tree and other attributes. As shown in FIG. 17C, attributes related to the tree vigor of the fruit tree include, for example, the thickness of the branch, the size of the buds on the branch, and the length of the internodes of the branch. For example, when the thickness of the branch is thinner than a predetermined value or the size of the buds is smaller than a predetermined value, the tree vigor may be too weak, and when the thickness of the branch is thicker than a predetermined value or the size of the buds is larger than a predetermined value, the tree vigor may be too strong. When the length of the internodes of the branch (that is, the distance between adjacent buds) is longer than a predetermined value, the number of buds per unit length is small, so the tree vigor may be too strong, and when the length of the internodes of the branch is shorter than a predetermined value, the number of buds per unit length is large, so the tree vigor may be too weak. When the tree vigor is strong, the fruit yield can increase, and when the tree vigor is too weak, the fruit yield can decrease. Note that "tree vigor" usually refers to the growth state and health state of the entire fruit tree, but also includes cases where it refers to the growth state and health state of a specific branch of the fruit tree. In a fruit tree, a specific branch may have tree vigor that is too strong or too weak compared to other parts. As will be described below, by changing the number of buds to be left according to the tree vigor of the branches to be left and generating cutting point data, it is possible to suppress a decrease in fruit yield and quality. Examples of attributes related to the tree vigor of the fruit tree are not limited to the attributes listed in FIG. 17C, and other attributes related to the tree vigor of the fruit tree may be included as in these examples.

[0435] In step S240, based on the measurement values obtained in step S238, for each of one or more branches to be processed, it is determined whether the branch is to be removed or left. The method for determining whether the branch is to be removed or left may be the same as the above-described example. For example, by performing the same processing as the processing from step S240k to step S240q shown in FIG. 11B for each of one or more branches to be processed, for each of one or more attributes, a factor score may be determined based on the measurement values obtained in step S234, and the branches to be left may be determined based on the factor score.

[0436] In step S280, based on the measured value obtained in step S238, determine the number of buds to be left on the branches determined to be the branches to be left in step S240.

[0437] FIG. 21C is a flowchart showing a specific example of the process performed in step S280.

[0438] In step S280a, acquire information on the set value of the number of buds to be left on the branches to be left, based on, for example, user input.

[0439] In step S280b, based on the measured value obtained in step S238, determine the strength of the tree vigor of the branches determined to be the branches to be left in step S240. For example, if the thickness of the branch is thinner than a predetermined value or the size of the bud is smaller than a predetermined value, it is determined that the tree vigor is weaker than a predetermined range, and if the thickness of the branch is thicker than a predetermined value or the size of the bud is larger than a predetermined value, it is determined that the tree vigor is stronger than a predetermined range. For example, if the thickness of the branch is within a predetermined range or the size of the bud is within a predetermined range, it is determined that the tree vigor is within a predetermined range.

[0440] If it is determined in step S280b that the tree vigor is too strong, proceed to step S280c. In step S280c, determine that the number of buds to be left has a value larger than the set value obtained in step S280a.

[0441] If it is determined in step S280b that the tree vigor is too weak, proceed to step S280e. In step S280e, determine that the number of buds to be left has a value smaller than the set value obtained in step S280a.

[0442] If it is determined in step S280b that the tree vigor is within a predetermined range, proceed to step S280d. In step S280d, determine that the number of buds to be left is the set value obtained in step S280a.

[0443] If the tree vigor of the resultant mother branch is weaker than a predetermined range, the fruit yield and quality may decrease. Therefore, by generating cutting point data so that the number of buds to be left is less than a set value (for example, a value input by the user), it is possible to suppress a decrease in the fruit yield and quality. When the tree vigor of the resultant mother branch is stronger than a predetermined range, it is expected to obtain a higher yield without degrading the quality by generating cutting point data so that the number of buds to be left is more than the set value. By generating cutting point data according to the tree vigor of the fruit tree in this way, it is possible to suppress a decrease in the fruit yield and quality.

[0444] Based on the determination in step S240 and the determination in step S280, the processes in steps S302 and S304 are performed. The order of the processes in steps S302 and S304 does not matter and they can be performed simultaneously (in parallel).

[0445] After steps S302 and S304, step S400 may further be included as in the example of FIG. 4B. That is, the cutting point data generated in step S300 may be input to a control device that controls the three-dimensional position of a cutter (for example, the cutting tool 24 included in the cutting system 1 of FIG. 1) that cuts the branches of the fruit tree. In this way, the cutter can be made to execute cutting of the branches of the fruit tree.

[0446] FIG. 21B is a flowchart showing an example of a procedure for generating cutting point data for branches of a fruit tree according to an embodiment of the present disclosure. The flowchart of FIG. 21B differs from the flowchart of FIG. 21A in that it further includes step S220 (grouping).

[0447] In step S220 of FIG. 21B, based on the sensor data obtained in step S100, a plurality of branches 58 of the fruit tree are grouped into a plurality of groups. The process of step S220 is performed in the same manner as the process of step S220 in FIG. 9A. The processes in each step other than step S220 in FIG. 21B are performed in the same manner as in the example of FIG. 21A. However, in the example of FIG. 21A, the processes performed on one or more branches to be processed are, in the example of FIG. 21B, performed on one or more branches grouped into the same group among the plurality of groups in step S220.

[0448] The flowcharts of FIGS. 21A and 21B can be further modified in combination with the above-described flowchart or process. For example, it may be further performed to obtain information on the priorities of two or more attributes and to determine, based on the priorities and the measured values obtained in step S238, for each of one or more branches whether to be a branch to be removed or a branch to be left. Such a process can be performed by a process similar to steps S250 and S252 in FIGS. 13A and 13B.

[0449] [Evaluation Criteria for Each Attribute] With reference to FIGS. 22A to 22N, specific examples of the evaluation criteria for each attribute exemplified above will be described. FIGS. 22A, 22C, 22E, 22G, 22I, 22K, and 22M show schematic diagrams for explaining the images used for obtaining the measured values related to each attribute or the obtaining of the measured values. FIGS. 22B, 22D, 22F, 22H, 22J, 22L, and 22N show tables showing examples of a plurality of classes related to each attribute, the evaluation criteria for each of the plurality of classes, and the scores corresponding to each of the plurality of classes. The factor scores for each attribute for each branch can be determined based on these tables. For example, based on these tables, the factor scores for each attribute for each branch can be calculated by the method described with reference to FIG. 16B.

[0450] (1) Color of the branch For each of one or more branches to be processed, the measurement value regarding the color of the branch is obtained using a segmented image as shown in, for example, FIG. 10A. Based on the measurement value regarding the color of the branch, a factor score regarding the color of the branch can be determined. The factor score regarding the color of the branch is determined, for example, such that the closer the color of the branch is to brown, the higher it becomes. When the color of the outside of the branch is brown, the branch is dry and suitable for pruning, while, for example, a green branch is often not yet suitable for pruning as it is too early for pruning. For example, when the factor score regarding the color of the branch is lower than a predetermined value, it may be detected as a branch outside the candidates that should not be selected as a branch to be left. When the factor score regarding the color of the branch is lower than a predetermined value, the user may be notified that there is a branch outside the candidates.

[0451] The factor score regarding the color of each of one or more branches to be processed is determined by the following steps of processing shown in, for example, FIG. 22O. FIG. 22O is a flowchart showing an example of the process of determining the factor score regarding the color of the branch.

[0452] Step S1-1: Using one or more sensors (for example, a camera), acquire sensor data of the branch (for example, an image including the branch).

[0453] Step S1-2: Using the acquired sensor data, extract the part corresponding to the branch. For example, apply segmentation using AI (for example, instance segmentation) to the acquired image.

[0454] Step S1-3: Acquire information regarding the color of the part corresponding to the extracted branch (for example, RGB values, HSL values, and their statistical values).

[0455] Step S1-4: Based on the acquired information regarding the color, obtain a factor score. For example, store a table representing the relationship between the information regarding the color and the factor score in a storage device, and obtain the factor score by referring to the table.

[0456] (2) Direction of branch growth For each of one or more branches to be processed, measurement values regarding the direction of growth of the branches are obtained using, for example, a segmented image as shown in FIG. 10A. FIG. 22A is a segmented image including a part (branch) of a fruit tree. For each branch, the inclination angle θp of the branch with respect to the direction opposite to the gravitational direction (+z direction in the figure) and the azimuth angle θa of the branch in the horizontal plane (xy plane in the figure) orthogonal to the gravitational direction are calculated. In calculating the inclination angle θp and the azimuth angle θa, it is preferable to use a portion close to the base of the branch (that is, a portion of the branch close to the short shoot). Based on the inclination angle θp and the azimuth angle θa, a factor score regarding the direction of growth of the branch can be determined. When the shape of the trellis system of the fruit tree is VSP (vertical shoot position), it is preferable that the inclination angle θp is small. Also, regarding the azimuth angle θa, there may be a case where it is preferable that the branch is inclined more in the left-right direction (±x direction in the figure) than in the front-back direction (±y direction in the figure). For example, the branch to be cut may be located in the forward direction of the cutter for cutting the branch of the fruit tree (+y direction in the figure). When the xy plane is regarded as the dial of a clock, the +x direction is regarded as the 3 o'clock direction, and the +y direction is regarded as the 0 o'clock direction, and the 3 o'clock direction is defined as 0° and the counterclockwise direction is defined as positive, it is more preferable that the azimuth angle θa is within a first range Ra including 0° and 180° (for example, 0° to 45°, 135° to 225°, and 315° to 360°) than within a second range Rb including 90° and 270° (for example, 45° to 135° and 225° to 315°).

[0457] The inclination angle θp and the azimuth angle θa of each branch are calculated by, for example, the following steps of processing shown in FIG. 22P. FIG. 22P is a flowchart showing an example of the processing for calculating the inclination angle θp and the azimuth angle θa of each branch.

[0458] Step S2-1: Use one or more sensors to acquire sensor data including information indicating the three-dimensional structure of the branch, and apply segmentation to the sensor data to obtain data for identifying the branch as segmentation information. The acquisition of the sensor data may, for example, acquire point cloud data of the branch by a LiDAR sensor, or acquire an image of the branch by an imaging device (camera). The acquisition of the segmentation information may acquire information obtained by segmenting two-dimensional image data, or may acquire information obtained by segmenting point cloud data. When using a two-dimensional image in addition to the point cloud data, a step of aligning the coordinate system of the two-dimensional image and the coordinate system of the point cloud data is further performed.

[0459] Step S2-2: Identify the point cloud data belonging to the region extracted as a branch by segmentation.

[0460] Step S2-3: Set a three-dimensional orthogonal coordinate system with the position of the base of the branch as the origin. The +z-axis direction is the direction opposite to the gravitational direction (i.e., vertically upward). For example, in the case of short tip pruning, by using the segmentation information to identify the boundary (connection point) between the branch and the short tip or the main branch, the connection point between the branch and the short tip or the main branch is defined as the position of the base of the branch. In the case of long tip pruning, by using the segmentation information to identify the boundary (connection point) between the branch and the stock, the connection point between the branch and the stock is defined as the position of the base of the branch.

[0461] Step S2-4: In the coordinate system defined in Step S2-3, calculate a vector from the point cloud data using a portion in the range of about 50 cm to 60 cm from the base of the branch. The vector can also be calculated using the entire branch, but it is preferable to use the range close to the base of the branch. For example, by using singular value decomposition (SVD), the structure of a local portion (range close to the root) of the branch can be extracted from the point cloud data, and a vector can be calculated using that portion.

[0462] Step S2-5: Obtain the tilt angle θp and the azimuth angle θa from the obtained vector.

[0463] Table Tb1 and Table Tb2 are shown in FIG. 22B as examples of a plurality of classes regarding the extending direction of a branch, scores corresponding to the respective classes, and evaluation criteria for the respective classes. The factor score regarding the extending direction of a branch is determined such that, for example, when the shape of the trellis system of a fruit tree is VSP, the smaller the inclination angle θp, the higher the score. For example, when the shape of the trellis system of a fruit tree is VSP, it can be determined such that, in order from the higher factor score, when the inclination angle θp is smaller than a predetermined range, when the inclination angle θp is within the predetermined range, and when the inclination angle θp is larger than the predetermined range. Alternatively, when the shape of the trellis system of a fruit tree is VSP, it can be determined such that, in order from the higher factor score, when the inclination angle θp is smaller than a predetermined range and the azimuth angle θa is within a first range Ra including 0° and 180°, when the inclination angle θp is smaller than a predetermined range and the azimuth angle θa is within a second range Rb including 90° and 270°, when the inclination angle θp is within the predetermined range and the azimuth angle θa is within the first range Ra including 0° and 180°, when the inclination angle θp is within the predetermined range and the azimuth angle θa is within the second range Rb including 90° and 270°, and when the inclination angle θp is larger than the predetermined range.

[0464] Note that, for example, the main trunk of a fruit tree may be inclined with respect to the direction opposite to the gravitational direction (the +z direction in the figure), and even in such a case, the factor score regarding the extending direction of the branch may be determined based on the inclination angle θp of the branch with respect to the direction opposite to the gravitational direction and the azimuth angle θa of the branch in the horizontal plane orthogonal to the gravitational direction.

[0465] When the shape of the trellis system of a fruit tree is not VSP, the factor score regarding the extending direction of the branch can be determined according to evaluation criteria different from the exemplified evaluation criteria.

[0466] (3) Thickness of branch For each of one or more branches to be processed, the measured value regarding the thickness of the branch is obtained using a segmented image as shown in, for example, FIG. 10A. FIG. 22C is an image showing an enlarged part of the segmented image and includes a branch mask M58_a. For example, using the branch mask included in such a segmented image, the measured value of the thickness of each branch is obtained. The measured value regarding the thickness of the branch is obtained, for example, by calculating the length in a direction orthogonal to the direction in which the branch extends at each predetermined distance from the base of the branch and calculating the average value thereof. The calculation of the length in the direction orthogonal to the direction in which the branch extends may be performed using a part of the branch other than the part having a bud, that is, using a part of the branch that does not have a bud.

[0467] Based on the measured value regarding the thickness of the branch, a factor score regarding the thickness of the branch can be determined. FIG. 22D shows an example of a plurality of classes regarding the thickness of the branch, the score corresponding to each class, and the evaluation criteria for each class. The factor score regarding the thickness of the branch is determined, for example, such that, in order from the highest factor score, when the thickness of the branch (for example, the above average value) is within a predetermined range, when the thickness of the branch (for example, the above average value) is greater than the predetermined range, and when the thickness of the branch (for example, the above average value) is less than the predetermined range.

[0468] (4) Height of the base of the branch For each of one or more branches to be processed, the measured value regarding the height of the base of the branch is obtained using a segmented image as shown in, for example, FIG. 10A. FIG. 22E is an image showing an enlarged part of the segmented image, and includes a branch mask M58_a, a short shoot mask M56_a for extracting a short shoot from which a branch corresponding to the branch mask M58_a grows, and a main branch mask M54_a. When the pruning method for the fruit tree is short shoot pruning, for example, using such a segmented image, the measured value of the height from the main branch at the position of the base of the branch is obtained. For example, the distance Dt between the position P1 of the base of the branch mask M58_a (the portion in contact with the short shoot mask M56_a) and the center line Lt of the main branch mask M54_a is calculated. At this time, as described with reference to FIG. 10A, for each branch 58, association with the corresponding short shoot 56 can be performed based on the segmented image, in the same manner as when performing the grouping process. When the pruning method for the fruit tree is long shoot pruning, for example, using the segmented image, the measured value of the height from the stock at the position of the base of the branch is obtained.

[0469] Based on the measured values related to the height of the base of the branch, a factor score related to the height of the base of the branch can be determined. FIG. 22F shows examples of a plurality of classes related to the height of the base of the branch, scores corresponding to each class, and evaluation criteria for each class. The factor score related to the height of the base of the branch is, for example, when the shape of the trellis system of the fruit tree is VSP (vertical shoot position) and the pruning method of the fruit tree is short shoot pruning or long shoot pruning, in descending order of the factor score, when the height of the base of the branch is within a predetermined range, when the height of the base of the branch is lower than the predetermined range, and when the height of the base of the branch is higher than the predetermined range. This is because when the shape of the trellis system of the fruit tree is VSP, it is preferable to bear fruits within a predetermined range (for example, an area about 10 cm from the main branch) from the main branch or the stock. As described above, the height of the base of the branch is evaluated by the height from the main branch at the position of the base of the branch in the case of short shoot pruning, and by the height from the stock at the position of the base of the branch in the case of long shoot pruning. When the shape of the trellis system of the fruit tree is not VSP, the factor score related to the height of the base of the branch can be determined according to evaluation criteria different from the exemplified evaluation criteria.

[0470] (5) Size of buds on the branch The measured values related to the size of the buds on each of the one or more branches to be processed are obtained, for example, using a segmented image as shown in FIG. 10A. The measured value related to the size of the buds on the branch is obtained, for example, by calculating the average value of the sizes of a predetermined number of buds among the buds on the branch. The predetermined number can be, for example, a set value of the number of buds to be left on the branches to be left. Before obtaining the measured value related to the size of the buds on the branch, information on the set value of the number of buds to be left on the branches to be left is obtained, for example, based on the user's input. FIG. 22G shows an image of a part (branch) of the fruit tree. The sizes of a predetermined number (for example, 2) of buds are determined starting from the bud closest to the base of the branch (the bud closest to the short shoot or the main branch), and the average value is calculated. The measured value of the size of the bud is obtained, for example, by calculating the area of the bud mask region that extracts the bud in the segmented image.

[0471] Based on the measurement value regarding the size of the buds on the branch, a factor score regarding the size of the buds on the branch can be determined. FIG. 22H shows an example of a plurality of classes regarding the size of the buds on the branch, the score corresponding to each class, and the evaluation criteria for each class. The factor score regarding the size of the buds on the branch is determined such that, for example, in order from the highest factor score, when the size of the buds on the branch (e.g., the above average value) is within a predetermined range, when the size of the buds on the branch (e.g., the above average value) is larger than the predetermined range, and when the size of the buds on the branch (e.g., the above average value) is smaller than the predetermined range.

[0472] (6) Direction in which the buds on the branch face The measurement value regarding the direction in which the buds on each of one or more branches to be processed face is obtained using, for example, a segmented image as shown in FIG. 10A. The measurement value regarding the direction in which the buds on the branch face is obtained, for example, by calculating the average value of the directions in which a predetermined number of buds among the buds on the branch face. The predetermined number can be, for example, a set value of the number of buds to be left on the branches to be left. Before obtaining the measurement value regarding the size of the buds on the branch, information on the set value of the number of buds to be left on the branches to be left is obtained based on, for example, user input. FIG. 22I shows an image showing a part (branch) of a fruit tree. The direction in which a predetermined number (e.g., 2) of buds face is determined starting from the bud closest to the base of the branch, and the average value is calculated. As the measurement value of the direction in which the buds face, for example, the inclination angle of the buds with respect to the direction orthogonal to the horizontal plane (xy plane in the figure) (±z direction in the figure) is obtained. As a modification, among all the buds on the branch, the ratio of the number of buds whose inclination angle in the z direction is a value equal to or greater than a predetermined value can be used as the measurement value.

[0473] Based on the measured values regarding the direction in which the buds of a branch face, a factor score regarding the direction in which the buds of the branch face can be determined. FIG. 22J shows examples of a plurality of classes regarding the direction in which the buds of a branch face, scores corresponding to each class, and evaluation criteria for each class. The factor score regarding the direction in which the buds of a branch face is determined such that, for example, when the shape of the trellis system of a fruit tree is VSP (vertical shoot position), from the highest factor score in order, when the direction in which the buds of the branch face (for example, the above average value) is upward from the horizontal plane, and when the direction in which the buds of the branch face (for example, the above average value) is downward from the horizontal plane. Note that the pruning method of the fruit tree may be either short shoot pruning or long shoot pruning. This is because VSP is configured such that new shoots (or branches) grow vertically upward from the buds. When the shape of the trellis system of the fruit tree is not VSP, the factor score regarding the direction in which the buds of the branch face can be determined according to evaluation criteria different from the illustrated evaluation criteria.

[0474] The inclination angle of the bud with respect to the direction orthogonal to the horizontal plane (the xy plane in the figure, the ±z direction in the figure) is calculated, for example, by the following steps of processing shown in FIG. 22Q. FIG. 22Q is a flowchart showing an example of the process of calculating the inclination angle of the bud with respect to the direction orthogonal to the horizontal plane.

[0475] Step S6-1: Using one or more sensors, acquire sensor data including information indicating the three-dimensional structure of the branch, apply segmentation or object detection to the sensor data, and acquire data for identifying the bud as segmentation information. The acquisition of the sensor data is, for example, acquiring point cloud data of the branch by a LiDAR sensor. An image of the branch may be further acquired by an imaging device (camera). The acquisition of the segmentation information may be acquiring information segmented using two-dimensional image data, or acquiring information segmented from point cloud data. When using a two-dimensional image in addition to the point cloud data, a step of aligning the coordinate system of the two-dimensional image and the coordinate system of the point cloud data is further performed.

[0476] Step S6-2: Identify the point cloud data belonging to the region classified as a bud by segmentation or object detection.

[0477] Step S6-3: Set up a three-dimensional orthogonal coordinate system with the position of the root of the bud as the origin. The +z-axis direction is the direction opposite to the gravitational direction (i.e., vertically upward). By using the segmentation information to identify the boundary (connection point) between the bud and the branch, the connection point between the bud and the branch is defined as the position of the root of the bud.

[0478] Step S6-4: Calculate a vector from the point cloud data indicating the bud in the coordinate system defined in Step S6-3.

[0479] Step S6-5: Determine the inclination angle of the bud with respect to the direction orthogonal to the horizontal plane from the obtained vector.

[0480] (7) Length of the branch For each of one or more branches to be processed, the measured value regarding the length of the branch is obtained, for example, by using a segmented image as shown in FIG. 10A. FIG. 22K shows an image of a part (branch) of a fruit tree. The length of the branch is defined as the length from the root of the branch (for example, in the case of spur pruning, the boundary (connection point) between the branch and the spur or the main branch; in the case of long shoot pruning, the boundary (connection point) between the branch and the stock) to the outermost end of the branch (the end farthest from the root). When the outermost end of the branch is not included in the image (outside the field of view), the point farthest from the root of the branch within the image is taken as the outermost end of the branch. The length of the branch is not limited to the straight-line distance between two points, and the curved length along the length direction of the branch can be used.

[0481] Based on the measurement values related to the branch length, a factor score related to the branch length can be determined. Fig. 22L shows an example of a plurality of classes related to the branch length, the scores corresponding to each class, and the evaluation criteria for each class. The factor score related to the branch length is determined, for example, in the case of long shoot pruning, in descending order of the factor score, when the branch length is within a predetermined range, when the branch length is longer than the predetermined range, and when the branch length is shorter than the predetermined range. As a threshold value, for example, a distance that is half of the distance between the main trunks of fruit trees can be used. The "predetermined range" in the evaluation criteria of Fig. 22L includes a distance that is half of the distance between the main trunks of adjacent fruit trees. The information on the length of the distance between the main trunks of adjacent fruit trees may be obtained based on the sensor data of two or more adjacent fruit trees (for example, sensor data including information indicating the three-dimensional structure of the fruit tree), or may be obtained based on the input of the user.

[0482] In the case of long shoot pruning, basically, cutting point data is not generated for the branches to be left. However, when the determined branch length of the branches to be left is longer than the predetermined range (for example, when classified into class "2" in the example of Fig. 22L), cutting point data for the branches to be left may be generated so that it is below the predetermined range.

[0483] In the case of short shoot pruning, the factor score related to the branch length can be determined according to evaluation criteria different from the exemplified evaluation criteria.

[0484] (8) Length of the internodes of the branch The measurement values related to the length of the internodes of each of one or more branches to be processed are obtained, for example, using a segmented image as shown in Fig. 10A. Fig. 22M shows an image of a part (branch) of a fruit tree. As shown in Fig. 22M, it is obtained by calculating the average value of the distances between adjacent buds among the buds of the branch. As described above, similar to the example of Fig. 10A, the buds of the branch may be extracted by instance segmentation, or the buds may be detected by object detection. For example, the distance between adjacent buds can be obtained by using the center point of the extracted or detected bud as the reference coordinate of the bud.

[0485] Based on the measurement values regarding the distance between adjacent buds, a factor score regarding the internode length of the branch can be determined. FIG. 22N shows examples of a plurality of classes regarding the internode length of the branch, the scores corresponding to the respective classes, and the evaluation criteria for the respective classes. The factor score regarding the internode length of the branch is, for example, in the case of long shoot pruning, in order from the highest factor score, when the distance between adjacent buds of the branch (for example, the above average value) is within a predetermined range, when the distance between adjacent buds of the branch (for example, the above average value) is longer than the predetermined range, and when the distance between adjacent buds of the branch (for example, the above average value) is shorter than the predetermined range. In the case of short shoot pruning, the factor score regarding the internode length of the branch can be determined by evaluation criteria different from the illustrated evaluation criteria.

[0486] The factor score regarding the internode length of each of one or more branches to be processed is determined, for example, by the following steps shown in FIG. 22R. FIG. 22R is a flowchart showing an example of a process for calculating the average value of the distances between the coordinates of two adjacent buds.

[0487] Step S8-1: Using one or more sensors, acquire sensor data including information indicating the three-dimensional structure of the branch, and apply segmentation or object detection to the sensor data to acquire, as segmentation information, data for identifying buds. The acquisition of the sensor data is, for example, acquiring point cloud data of the branch by a LiDAR sensor. An image of the branch may be further acquired by an imaging device (camera). The acquisition of the segmentation information may be acquiring information segmented using two-dimensional image data, or acquiring information segmented from point cloud data. When using a two-dimensional image in addition to the point cloud data, a step of aligning the coordinate system of the two-dimensional image and the coordinate system of the point cloud data is further performed.

[0488] Step S8-2: Define the center coordinates of the point cloud data belonging to the region classified as a bud by segmentation or object detection as the coordinates of the bud.

[0489] Step S8-3: Of the buds associated with the same branch, obtain the straight-line distance between the coordinates of two adjacent buds. As a variant, instead of the straight-line distance between the coordinates of two adjacent buds among the buds associated with the same branch, a curved distance (the distance along the direction in which the branch extends) may be obtained and used.

[0490] Step S8-4: Obtain the average value of a predetermined number of the distances between the coordinates of two adjacent buds obtained in Step S8-3.

[0491] [Bud distribution] FIG. 23 is a flowchart showing an example of a procedure for generating cutting point data of a fruit tree branch according to an embodiment of the present disclosure. The flowchart of FIG. 23 is different from the flowchart of FIG. 9A in that it further has Step S290. The example of FIG. 23 can be combined with any of the above-described flowcharts.

[0492] In the example of FIG. 23, the procedure for generating cutting point data of a fruit tree branch is sensor data acquired by one or more sensors, and includes acquiring sensor data of a plurality of branches of a fruit tree (Step S100), grouping the plurality of branches into a plurality of groups based on the sensor data (Step S220), determining, based on the sensor data, for each of one or more branches grouped into the same group among the plurality of groups, whether it is a branch to be removed or a branch to be left (Step S222), determining, for each of the plurality of groups, for each of the branches, whether it is a branch to be removed or a branch to be left, based on the distribution of the buds of the branches determined to be branches to be left (Step S290), and generating cutting point data including information indicating the three-dimensional position of the point to be cut for each of the branches determined to be branches to be removed (Step S300).

[0493] In the example of FIG. 23, after determining whether to remove or retain the branches for each group, for each of the plurality of branches of the fruit tree, based on the distribution of buds (the distribution of buds in the entire fruit tree) of the branches determined to be retained, a determination is made again as to whether to remove or retain the branches. After determining whether to remove or retain the branches for each group, it can be corrected so that the distribution of buds in the entire fruit tree becomes more uniform, which can lead to further improvement in the fruit yield and quality. Note that the example of FIG. 23 can be applied to the case of short shoot pruning.

[0494] With reference to FIG. 24, the processing performed in the flowchart of FIG. 23 will be described. FIG. 24 is a schematic diagram for explaining the processing in the flowchart of FIG. 23. FIG. 24 shows a virtual state of a fruit tree assuming that pruning is performed in accordance with the determination in step S220. For simplicity, buds 59 are shown only on some branches 58.

[0495] The processing in step S100 is performed in the same manner as the processing in the example of FIG. 4A.

[0496] In step S220, based on the sensor data obtained in step S100, a plurality of branches 58 of the fruit tree are grouped into a plurality of groups. The processing in step S220 is performed in the same manner as the processing in the example of FIG. 9A.

[0497] The grouping of the plurality of branches 58 can be performed based on the respective root positions of the plurality of branches 58. For example, the plurality of groups respectively correspond to the plurality of short shoots 56 of the main branch 54 that supports the plurality of branches 58 of the fruit tree. Among the plurality of branches 58 of the fruit tree, the branches 58 growing from the same short shoot 56 may be grouped into the same group. Among the plurality of branches 58 of the fruit tree, the branches 58 growing from within a region of a predetermined range may be grouped into the same group. Alternatively, the plurality of groups may correspond to a plurality of regions R1, R2,... arranged along the direction (left - right direction in the figure) in which the main branch 54 extends of the main branch 54 that supports the plurality of branches 58 of the fruit tree. Among the plurality of branches 58 of the fruit tree, the branches growing from within the same region among the plurality of regions may be grouped into the same group.

[0498] In step S222, based on the sensor data acquired in step S100, for each of the one or more branches 58 grouped into the same group in step S220, it is determined whether it is a branch to be removed or a branch to be left. The process of step S222 is performed in the same manner as the process in the example of FIG. 9A. The process of step S222 is performed for each group. That is, in step S222, for each group, for each of the one or more branches grouped into that group, it is determined whether it is a branch to be removed or a branch to be left.

[0499] In step S290, for each of the plurality of groups, based on the distribution of buds on the branches determined to be the branches to be left in step S222, for each of the plurality of branches of the fruit tree, a determination is made again as to whether to make the branch a branch to be removed or a branch to be left. The distribution of buds is the distribution of buds throughout the fruit tree. For example, it is the arrangement density of buds in the direction along the direction in which the main branch 54 supporting the branch 58 extends (the left-right direction in FIG. 24). The information on the distribution of buds on the branches determined to be the branches to be left in step S222 can be obtained based on the information on the branches determined to be the branches to be left in step S222 and the information on the number of buds to be left on each of the branches to be left. The information on the number of buds to be left on each of the branches to be left can be obtained based on, for example, user input.

[0500] The branches determined to be the branches to be left in step S290 include the branches determined to be the branches to be removed in step S222. That is, in addition to the branches determined to be the branches to be left in step S222, branches to be additionally left in step S290 can be determined. By changing (re-determining) the branches determined to be the branches to be removed in step S222 to the branches to be left in step S290, the arrangement density of the buds on the branches to be left can be made more uniform throughout the fruit tree.

[0501] An example of the process in step S290 will be described. Specifically, in addition to the branches determined to be the remaining branches in step S222, additional branches to be left are determined as follows. For example, if there is a region where the arrangement density of buds is partially low, among the plurality of branches of the fruit tree, one or more branches grouped into a group located near that region are selected to determine the additional branches to be left. For example, if a plurality of groups include a first group in which branches are not grouped, additional branches to be left are determined from among one or more branches grouped into a group adjacent to the first group. Among one or more branches grouped into a group adjacent to the first group, additional branches to be left may be determined from among the branches extending in the direction of the first group. The first group is, for example, a group corresponding to a short shoot where no branches grow. Also, if a plurality of groups include a second group that has no branches to be left, additional branches to be left are determined from among one or more branches grouped into a group adjacent to the second group. Among one or more branches grouped into a group adjacent to the second group, additional branches to be left may be determined from among the branches extending in the direction of the second group. The second group is, for example, a group in which all of the one or more branches grouped into that group are determined to be branches to be removed.

[0502] Information on the cultivation method of the fruit tree is obtained, and in step S290, for each of the plurality of branches of ...

Claims

1. A method performed using one or more computing devices, comprising: receiving sensor data obtained by one or more sensors, the sensor data including sensor data of one or more branches of a fruit tree; determining, based on the sensor data, for each of the one or more branches whether it is a branch to be removed or a branch to be retained; and a method comprising:

2. The method according to claim 1, wherein the branches determined to be retained include fruiting mother branches.

3. The method according to claim 1 or 2, wherein the branches determined to be retained are the branches among the one or more branches on which the highest quality fruits are expected to be borne in that season.

4. further comprising generating cutting point data for each of the branches determined to be retained, the cutting point data including information indicating the three-dimensional position of the point to be cut; for each of the branches determined to be retained, generating the cutting point data includes generating the cutting point data such that each of the branches determined to be retained has one or more buds after being cut. The method according to claim 1 or 2.

5. further comprising generating cutting point data for each of the branches determined to be removed, the cutting point data including information indicating the three-dimensional position of the point to be cut; for each of the branches determined to be removed, generating the cutting point data includes generating the cutting point data such that each of the branches determined to be removed has no buds after being cut. The method according to claim 1 or 2.

6. The method according to claim 4, further comprising inputting the generated cutting point data into a control device that controls the three-dimensional position of a cutter for cutting the branches of the fruit tree.

7. for each of the one or more branches, obtaining a measurement value related to one or more attributes; determining, based on the measurement value, for each of the one or more branches whether it is a branch to be removed or a branch to be retained; and the method according to claim 1 or 2, further comprising:

8. the one or more attributes are two or more attributes; for each of the one or more branches, obtaining a measurement value related to the one or more attributes includes obtaining measurement values related to the two or more attributes for each of the one or more branches; determining for each of the one or more branches whether it is a branch to be removed or a branch to be retained The method according to claim 7, comprising determining, based on the measured values regarding the two or more attributes, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained.

9. The method according to claim 1 or 2, wherein the fruit tree is a grapevine.

10. Further comprising obtaining information on the vineyard design of the grapevine, Determining, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained, The method according to claim 9, wherein determining, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained includes determining, based on the sensor data and the information on the vineyard design of the grapevine, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained.

11. The fruit tree is a grapevine, Further comprising obtaining information on the vineyard design of the grapevine, The one or more attributes include attributes with different evaluation criteria according to the information on the vineyard design of the grapevine, Determining, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained, The method according to claim 7, wherein determining, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained includes determining, based on the measured values of the one or more attributes and the information on the vineyard design of the grapevine, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained.

12. The one or more branches are two or more branches, Determining, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained, The method according to claim 1 or 2, wherein determining, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained includes determining, for the branches other than the branches determined to be branches to be retained among the two or more branches, that they are branches to be removed.

13. One or more sensors for obtaining sensor data of one or more branches of a fruit tree, A data processing device for determining, based on the sensor data, for each of the one or more branches, whether it is a branch to be removed or a branch to be retained A system comprising.

14. One or more sensors for obtaining sensor data of one or more branches of a fruit tree, Means for executing the steps of the method according to claim 1 or 2 A system having.

15. The data processing device generates cutting point data including information indicating the three-dimensional position of the point to be cut for each of the branches determined to be branches to be removed, Further comprising a cutter for cutting the branches of the fruit tree and a control device for controlling the three-dimensional position of the cutter, The data processing device inputs the generated cutting point data into the control device. The control device controls the three-dimensional position of the cutter based on the cutting point data. The system according to claim 13. **Claim 16** An agricultural machine having the system according to claim 15. **Claim 17** The agricultural machine further includes an arm that supports the cutter, a support that supports the arm, and a drive device that moves the support. The control device controls the three-dimensional position of the cutter by controlling the operation of the arm. The agricultural machine according to claim 16.

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