Measurement system, processing device, method for determining measured quality value of fruit, and computer program

Abstract

A measurement system 1 according to the present disclosure comprises: a first measurement device 2 that measures the quality of a plurality of fruits K; a second measurement device 4 that has a different configuration from the first measurement device 2 and measures the quality of some of the plurality of fruits K; and a management server 8 that determines a measured quality value of a fruit KT of interest among the plurality of fruits K. The management server 8 includes a processing unit 32 that executes processing for determining the measured quality value on the basis of scaling data S indicating the correlation between the output of the first measurement device 2 and the quality value based on the output of the second measurement device 4, and the output of the first measurement device 2 obtained when the quality of the fruit KT of interest is measured.

Description

MEASUREMENT SYSTEM, PROCESSING DEVICE, METHOD FOR DETERMINING MEASURED QUALITY VALUES OF FRUITS, AND COMPUTER PROGRAM

[0001] This disclosure relates to a measurement system, a processing device, a method for determining a measurement quality value of fruit, and a computer program. This application claims priority to Japanese Application No. 2023-215568 filed on December 21, 2023, and incorporates by reference all of the contents of said Japanese application.

[0002] Patent Document 1 discloses a measuring device that irradiates a measured object such as a fruit with light and performs spectroscopic analysis of the transmitted light to obtain quality values ​​of the measured object, such as sugar content and acidity. This measuring device is equipped with a sensor unit that includes a light source and a light receiving unit that receives the transmitted light. The quality value is measured by bringing the sensor unit close to the measured object. The light source of the sensor unit, which is placed close to the measured object, irradiates light onto the measured object. The light receiving unit of the sensor unit receives the transmitted light that has passed through the measured object.

[0003] Japanese Patent Application Laid-Open No. 2020-101409

[0004] The presently disclosed measurement system includes a first measurement device that measures the quality of multiple fruits, a second measurement device that measures the quality of some of the multiple fruits and has a configuration different from that of the first measurement device, and a processing device that calculates a measured quality value of a target fruit from the multiple fruits. The processing device includes a processing unit that executes processing to calculate the measured quality value based on scaling data that indicates a correlation between the output of the first measurement device and a quality value based on the output of the second measurement device, and the output of the first measurement device when measuring the quality of the target fruit.

[0005] FIG. 1 is a diagram showing an example of the overall configuration of a measurement system according to an embodiment. FIG. 2 is a block diagram showing main parts of an agricultural machine. FIG. 3 is a block diagram showing an example of a first measurement device. FIG. 4A is a block diagram showing an example of a second measurement device. FIG. 4B is a cross-sectional view of a second measurement head. FIG. 5 is a block diagram showing an example of the configuration of a management server. FIG. 6 is a flowchart showing an example of quality measurement work for fruit K in a farm field. FIG. 7 is a plan view of the farm field. FIG. 8 is a flowchart showing an example of quality value calculation processing. FIG. 9 is a diagram showing an example of a plurality of first calibration curve data. FIG. 10 is a diagram showing an example of a plurality of second calibration curve data. FIG. 11 is a diagram showing an example of scaling data. FIG. 12 is a diagram showing a mode when a processing unit calculates a measured quality value of a fruit with ID=1. FIG. 13 is a diagram showing an example of scaling data according to a modified example.

[0006] [Problem to be Solved by the Present Disclosure] When measuring quality values ​​using the above-mentioned measuring device, the sensor unit needs to be placed close to the object to be measured. Therefore, while the measuring device can measure the quality values ​​of some of the fruits that bear fruit on many trees planted in a field, it is difficult to comprehensively measure the quality values ​​of fruits in the entire field.

[0007] Here, spectroscopic cameras, also known as hyperspectral cameras or multispectral cameras, can be used to perform spectroscopic analysis similar to that of the above-mentioned measuring device. Spectroscopic cameras perform measurements based on images captured in a specific wavelength band. Therefore, they can easily measure a relatively wide area. This makes it possible to easily and comprehensively measure multiple fruits borne on numerous trees planted in a field. However, compared to the above-mentioned measuring device, spectroscopic cameras are more susceptible to variations in measurement values ​​due to external factors such as time, climate, and location, and the measurement accuracy of spectroscopic cameras can be said to be lower than that of the above-mentioned measuring device.

[0008] Thus, a measurement device with low measurement accuracy is generally more convenient than a measurement device with high measurement accuracy. The inventors of the present application have found a way to easily improve the quality measurement accuracy even when a measurement device with a different configuration and relatively low quality measurement accuracy is used in order to prioritize convenience, and have completed the embodiments disclosed below.

[0009] [Effects of the Present Disclosure] According to the present disclosure, it is possible to easily improve the accuracy of measuring fruit quality.

[0010] First, the contents of the embodiment will be listed and explained.

[0011] (1) The present disclosure provides a measurement system comprising a first measurement device for measuring the quality of a plurality of fruits, a second measurement device configured differently from the first measurement device for measuring the quality of some of the plurality of fruits, and a processing device for calculating a measured quality value of a target fruit from the plurality of fruits. The processing device comprises a processing unit for executing a process for calculating the measured quality value based on scaling data indicating a correlation between an output of the first measurement device and a quality value based on the output of the second measurement device, and the output of the first measurement device when measuring the quality of the target fruit.

[0012] According to the above configuration, a quality value of the second measuring device corresponding to the output of the first measuring device when measuring the quality of the target fruit can be calculated based on the scaling data, and the calculated quality value of the second measuring device can be used as the measured quality value of the target fruit. Therefore, if the measurement accuracy of the second measuring device is higher than that of the first measuring device, the output of the first measuring device can be corrected to correspond to the quality value of the second measuring device, which has higher measurement accuracy than the first measuring device, thereby easily improving the quality measurement accuracy.

[0013] (2) In the measurement system of (1), the correlation indicated by the scaling data is preferably a correlation between a quality value based on the output of the first measurement device and a quality value based on the output of the second measurement device. In this case, the quality value based on the output of the first measurement device can be corrected to a quality value equivalent to that of the second measurement device.

[0014] (3) In the measurement system of (1) or (2) above, it is preferable that the first measurement device includes a first measurement head that measures the plurality of fruits at a first interval, and the second measurement device includes a second measurement head that measures some of the fruits at a second interval that is shorter than the first interval. In this case, the first measurement head is positioned at a first interval that is longer than the second interval, so that it can measure a wider area at one time than the second measurement head, which is highly convenient. Therefore, with the above configuration, it is possible to improve the quality measurement accuracy while increasing convenience by using the first measurement device.

[0015] (4) The measurement system of (3) may further include a moving mechanism that moves the second measurement head to a measurement position where the distance to the portion of fruit is the second distance. In this case, the moving mechanism can move the second measurement head to the measurement position.

[0016] (5) In addition, in any of the measurement systems described in (1) to (4) above, when the first measurement device includes a spectroscopic camera that receives reflected light from the plurality of fruits, the second measurement device may include a light source that irradiates light onto the portion of the fruits and a spectrometer that disperses the light transmitted through the portion of the fruits. In this case, the first measurement device is more convenient because it can measure a wider range at once than the second measurement device. Therefore, according to the above configuration, the use of the first measurement device can improve convenience while also improving the accuracy of quality measurement.

[0017] (6) In the measurement system according to any one of (1) to (5), the processing unit preferably further executes a process of generating the scaling data. In this case, scaling data according to the environment during measurement by the first measurement device can be obtained, thereby further improving the accuracy of quality measurement.

[0018] (7) In the measurement system of (1) or (6), the scaling data is preferably calculated based on a plurality of outputs of the first measurement device obtained when the portion of fruit is measured by both the first measurement device and the second measurement device, and a plurality of reference quality values ​​based on the plurality of outputs of the second measurement device. In this case, the output of the first measurement device and the quality value of the second measurement device are associated with each other depending on the portion of fruit. This further improves the accuracy of the scaling data.

[0019] (8) In the measurement system of (7), the scaling data may be calculated based on the maximum and minimum values ​​of the multiple outputs of the first measurement device and the maximum and minimum values ​​of the multiple reference quality values ​​of the second measurement device. In this case, the output of the first measurement device and the quality value of the second measurement device are correlated based on at least the respective maximum and minimum values. This allows the scaling data to be calculated easily without reducing accuracy.

[0020] (9) In addition, the measurement system of any one of (1) to (8) above may further include an agricultural machine equipped with the first measurement device and the second measurement device. In this case, the quality of fruit can be measured while the agricultural machine is traveling in a field where fruit trees are grown, and the quality of fruit of fruit trees grown in the field can be measured efficiently, for example.

[0021] (10) From another perspective, the present disclosure provides a processing device for calculating a measured quality value of a target fruit among a plurality of fruits, the processing device including a processing unit that executes a process for calculating the measured quality value based on scaling data indicating a correlation between an output of a first measuring device that measures the quality of the plurality of fruits and a quality value based on an output of a second measuring device that has a configuration different from the first measuring device and measures the quality of some of the plurality of fruits, and the output of the first measuring device when measuring the quality of the target fruit.

[0022] (11) From another perspective, the present disclosure provides a method for determining a measured quality value of a target fruit among a plurality of fruits, the method including a step of determining the measured quality value based on scaling data indicating a correlation between an output of a first measurement device that measures the quality of the plurality of fruits and a quality value based on an output of a second measurement device that is configured differently from the first measurement device and measures the quality of some of the plurality of fruits, and the output of the first measurement device when measuring the quality of the target fruit.

[0023] (12) From another perspective, the present disclosure provides a computer program for causing a computer to execute a process for determining a measured quality value of a target fruit among a plurality of fruits, the computer program causing the computer to execute a step of determining the measured quality value based on scaling data indicating a correlation between an output of a first measuring device that measures the quality of the plurality of fruits and a quality value based on an output of a second measuring device that has a configuration different from the first measuring device and measures the quality of some of the plurality of fruits, and the output of the first measuring device when measuring the quality of the target fruit.

[0024] [Details of the embodiment] Preferred embodiments will be described below with reference to the drawings. Note that at least some of the embodiments described below may be combined in any manner.

[0025] [Overall Configuration of the Measurement System] FIG. 1 is a diagram showing an example of the overall configuration of a measurement system according to an embodiment. In FIG. 1, the measurement system 1 has a function of measuring the quality value of fruit of fruit trees cultivated in a field F. In this embodiment, the field F is, for example, a vineyard for cultivating grapes, which are used to make wine. Therefore, a plurality of trees T are cultivated in the field F. The plurality of trees T are fruit trees, i.e., grapevines. The plurality of trees T are cultivated in multiple rows. The measurement system 1 has a function of measuring the quality value of grapes bearing fruit on the trees T. More specifically, the measurement system 1 determines the quality value of the fruit by analysis using near-infrared light. The quality value of the fruit is a value based on various components that represent the quality of the fruit. The quality values ​​include sugar content, acidity, pH, polyphenols, etc.

[0026] The measurement system 1 includes a first measuring device 2, a second measuring device 4, an agricultural machine 6, a management server 8, a management terminal 10, and a manipulator 12. The first measuring device 2 and the second measuring device 4 are mounted on the agricultural machine 6. The manipulator 12 has a function of moving a second measuring head 26 (described later) of the second measuring device 4. The first measuring device 2 and the second measuring device 4 are devices that measure the quality of fruit. The first measuring device 2 and the second measuring device 4 have a function of acquiring spectral information in the near-infrared region contained in reflected light and transmitted light from the fruit. The spectral information in the near-infrared region includes information indicating the quality value of the fruit. Therefore, the first measuring device 2 and the second measuring device 4 output the spectral information in the near-infrared region as a quality measurement result. The first measuring device 2 and the second measuring device 4 will be described in detail later.

[0027] The agricultural machine 6, the management server 8, and the management terminal 10 are connected to each other so that they can communicate with each other via a public network NW such as the Internet. The agricultural machine 6 has a communication function using, for example, a mobile communication system. The agricultural machine 6 is connected to the public network NW via a wireless base station BS of the mobile communication system.

[0028] The management server 8 has a function of performing processing to determine the quality value of the fruit based on the output of the first measuring device 2 and the output of the second measuring device 4. The management terminal 10 is a terminal operated by an operator 14 of the measurement system 1. The management terminal 10 has a function of accepting operations on the measurement system 1 by the operator 14, and a function of outputting the quality values ​​and the like determined by the management server 8 to the operator 14.

[0029] The agricultural machine 6 is, for example, a tractor. The agricultural machine 6 is capable of traveling within a field F. The agricultural machine 6 travels between rows of multiple trees T within the field F and can approach all of the trees T within the field F. The agricultural machine 6 can travel within the field F by being manually driven by an operator, or can travel within the field F by being automatically driven based on control commands from the management server 8 or control commands from the management terminal 10 based on input from an operator 14.

[0030] FIG. 2 is a block diagram showing the main parts of the agricultural machine 6. As shown in FIG. 2, the agricultural machine 6 has a communication device 18 and a vehicle control device 20. The communication device 18 functions as a mobile terminal in a mobile communication system. Therefore, the communication device 18 communicates wirelessly with a radio base station BS. The vehicle control device 20 has a function of controlling each part of the agricultural machine 6. The vehicle control device 20 is connected to a public network NW via the communication device 18. Therefore, the vehicle control device 20 is connected to a management server 8 and a management terminal 10 so that they can communicate with each other. The vehicle control device 20 exchanges necessary information with the management server 8 and the management terminal 10 via the public network NW. When the agricultural machine 6 is capable of autonomous driving, the vehicle control device 20 controls cameras and sensors that grasp the surroundings of the agricultural machine 6, as well as the drive system and steering system of the agricultural machine 6, based on control commands from the management server 8 and the management terminal 10, and performs processing to execute autonomous driving.

[0031] The first measuring device 2 and the second measuring device 4 are connected to a communication device 18 of the agricultural machine 6. The first measuring device 2 and the second measuring device 4 are connected to the public network NW by the communication device 18. Therefore, the first measuring device 2 and the second measuring device 4 are also connected to the management server 8 and the management terminal 10 so that they can communicate with each other. Furthermore, the manipulator 12 is also connected to the communication device 18 and is connected to the management server 8 and the management terminal 10 so that they can communicate with each other. Control commands and the like are given to the first measuring device 2, the second measuring device 4, and the manipulator 12 from the management server 8 and the management terminal 10. Therefore, the first measuring device 2, the second measuring device 4, and the manipulator 12 are controlled by the management server 8 and the management terminal 10. Furthermore, the first measuring device 2, the second measuring device 4, and the manipulator 12 can provide necessary information to the management server 8 and the management terminal 10.

[0032] [Regarding the First Measuring Device 2 and the Second Measuring Device 4] Figure 3 is a block diagram showing an example of the first measuring device 2. As described above, the first measuring device 2 is a device that measures the quality of fruit and outputs spectral information in the near-infrared region as a quality measurement result. The first measuring device 2 receives light reflected from the fruit and outputs spectral information obtained by dispersing the received reflected light. Note that spectral information refers to information indicating the relationship between wavelength and light intensity in the near-infrared region.

[0033] As shown in FIG. 3 , the first measurement device 2 includes a first measurement head 22 and a first control unit 24. The first measurement head 22 includes, for example, a hyperspectral camera. A hyperspectral camera is a spectroscopic camera with multiple detection wavelength bands capable of detecting light intensity. The first measurement head 22 (hyperspectral camera) has multiple detection wavelength bands in the near-infrared region. The first measurement head 22 captures an image of a predetermined imaging region to acquire a two-dimensional image. That is, the first measurement head 22 receives reflected light when sunlight is irradiated onto an object present in the imaging region to acquire a two-dimensional image. The pixels constituting this two-dimensional image have luminance information indicating the light intensity of multiple detection wavelength bands. Therefore, the image acquired by the first measurement head 22 has spectral information in the near-infrared region for each pixel. Hereinafter, the image acquired by the first measurement head 22 will also be referred to as a spectral image. In this way, the first measurement head 22 can acquire spectral information in the near-infrared region for each imaging region.

[0034] The first measuring head 22 is used to capture an image of the grape berries K, which are the object to be measured. The first measuring head 22 is a camera capable of capturing two-dimensional images. Therefore, when capturing an image of the berries K using the first measuring head 22, a predetermined first distance D1 is provided between the first measuring head 22 and the grape berries K. The first distance D1 is, for example, approximately 30 cm ± 5 cm. In this embodiment, the grape berries K refer to a bunch containing multiple fruit kernels k1. When the first distance D1 is provided, the imaging area A of the first measuring head 22 is large enough to capture an image of multiple fruits K scattered among the branches and leaves of a single tree T. Therefore, the spectral image captured by the first measuring head 22 includes multiple fruits K. The first measuring head 22 outputs a spectral image capturing multiple fruits K. In other words, the first measuring head 22 receives reflected light from the multiple fruits K and outputs a spectral image.

[0035] The first measuring head 22 provides the captured spectral image to the first control unit 24. The first control unit 24 is connected to the communication device 18 of the agricultural machine 6. The first control unit 24 has a function of controlling the first measuring head 22 based on control commands and the like provided from the management server 8 or the management terminal 10. The first control unit 24 also has an input unit (not shown) that accepts operational input, and also has a function of controlling the first measuring head 22 based on operational input from the operator. The first control unit 24 also transmits the spectral image provided from the first measuring head 22 to the management server 8. In other words, the spectral image acquired by the first measuring head 22 is provided to the management server 8 as the output (measurement result) of the first measuring device 2.

[0036] 4A is a block diagram showing an example of the second measurement device 4. As described above, the second measurement device 4 is a device for measuring the quality of the fruit K and outputs spectral information in the near-infrared region as a quality measurement result. The second measurement device 4 irradiates the fruit K with light, receives the transmitted light that has passed through the fruit K, and outputs spectral information obtained by separating the transmitted light.

[0037] 4A , the second measuring device 4 includes a second measuring head 26, a light source 27, a spectroscope 28, and a second control unit 30. The second measuring head 26 has a light-projecting unit 26a and a light-receiving unit 26b. The light-projecting unit 26a and the light source 27 are connected by an optical fiber. The light source 27 is, for example, a halogen lamp. Light from the light source 27 is guided to the light-projecting unit 26a of the second measuring head 26 through the optical fiber. The light-projecting unit 26a irradiates the light from the light source 27 toward the fruit K, which is the object to be measured.

[0038] The light receiving unit 26b receives transmitted light that has passed through the fruit K. The transmitted light received by the light receiving unit 26b is generated when light from the light projecting unit 26a is irradiated onto the fruit K. The light receiving unit 26b and the spectroscope 28 are connected by an optical fiber. The transmitted light received by the light receiving unit 26b is guided to the spectroscope 28 through the optical fiber.

[0039] 4B is a cross-sectional view of the second measuring head 26. As shown in FIG. 4B, the second measuring head 26 has a head body 26c. The head body 26c is an annular member formed of resin or the like. The head body 26c is fixed to the tip of the manipulator 12. Therefore, the second measuring head 26 can be moved by the manipulator 12.

[0040] The light-projecting unit 26a is provided on the annular tip surface 26c1 of the head main body 26c. The light-projecting unit 26a is annular. When light from the light source 27 is applied to the light-projecting unit 26a, it is scattered internally and then uniformly emitted from the light-projecting unit 26a. The light from the light-projecting unit 26a is irradiated onto the fruit kernel k1 of the fruit K, producing transmitted light that passes through the fruit kernel k1 and reflected light that is reflected on the surface of the fruit kernel k1. The light-receiving unit 26b is provided in the hole 26c2 of the head main body 26c. The light-receiving unit 26b receives transmitted light that has passed through the hole 26c2. Note that the light-receiving unit 26b mainly receives transmitted light, but also receives reflected light.

[0041] When measuring fruit K using the second measuring device 4, it is necessary to irradiate fruit grain k1 with light using the light projecting unit 26a, and therefore it is necessary to bring the second measuring head 26 close to the fruit grain k1 of the fruit K. When measuring fruit K using the second measuring device 4, the second measuring head 26 is placed in the measurement position shown in FIG. 4B. The measurement position shown in FIG. 4B refers to a position where there is a second distance D2 between the second measuring head 26 and the fruit grain k1. The second distance D2 is shorter than the first distance D1 (see FIG. 3). The second distance D2 is, for example, approximately 0 cm to 1 cm. Note that a state where the second distance D2 is 0 cm refers to a state where the second measuring head 26 is in contact with the fruit grain k1.

[0042] The second measuring head 26 is placed at the measurement position by the manipulator 12. In this way, the manipulator 12 functions as a movement mechanism that moves the second measuring head 26 to the measurement position.

[0043] When the second measurement head 26 is positioned close to the fruit k1 at the measurement position, light from the light-emitting unit 26a is irradiated onto the fruit k1. The light irradiated onto the fruit k1 passes through the fruit k1 as shown by the arrows in Figure 4B, producing transmitted light. The transmitted light passes through the hole 26c2 and is received by the light-receiving unit 26b.

[0044] 4A, the spectroscope 28 has a function of separating transmitted light and a light-receiving function of converting the separated spectrum into a signal. When the spectroscope 28 receives the transmitted light received by the light-receiving unit 26b, the spectroscope 28 outputs spectral information obtained by separating the transmitted light. The spectral information output by the spectroscope 28 is provided to the second control unit 30.

[0045] The second control unit 30 is connected to the communication device 18 of the agricultural machine 6. The second control unit 30 has a function of controlling the light source 27 and the spectroscope 28 based on control commands and the like provided from the management server 8 or the management terminal 10. The second control unit 30 also has an input unit (not shown) that accepts operational input, and also has a function of controlling the light source 27 and the spectroscope 28 based on operational input from the operator.

[0046] Furthermore, the second control unit 30 transmits the spectral information provided by the spectrometer 28 to the management server 8. That is, the spectral information acquired by the spectrometer 28 is provided to the management server 8 as the output (measurement result) of the second measuring device 4.

[0047] As described above, the configuration of the first measuring device 2 and the configuration of the second measuring device 4 differ in terms of the presence or absence of a spectroscopic camera and a light source. Furthermore, the first measuring device 2 measures the quality of multiple fruits K collectively, while the second measuring device 4 measures the quality of a single fruit k1. Here, the measurement accuracy of the quality value indicated by the spectral information output by the second measuring device 4 is higher than the measurement accuracy of the quality value indicated by the spectral image (spectral information) output by the first measuring device 2. Measurement by the second measuring device 4 involves bringing the second measuring head 26 close to the fruit k1 and acquiring spectral information from the transmitted light when irradiating the fruit k1 with light, making it less susceptible to the influence of the surrounding environment. On the other hand, measurement by the first measuring device 2 involves acquiring spectral information from the reflected light from the fruit K, making it more susceptible to the influence of the surrounding environment. As a result, the measurement accuracy of the second measuring device 4 is higher than that of the first measuring device 2.

[0048] [Regarding the Management Server 8] Fig. 5 is a block diagram showing an example configuration of the management server 8. As shown in Fig. 5, the management server 8 (processing device) is a type of information processing device having a processing unit 32, a storage unit 34, and a communication unit 36. The communication unit 36 ​​is a communication interface capable of communicating with external devices via a public network NW. The processing unit 32 is, for example, one of various processors suitable for computer control, such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), or an FPGA (Field Programmable Gate Array).

[0049] The storage unit 34 is, for example, a flash memory, a hard disk, a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The storage unit 34 stores computer programs and necessary information to be executed by the processing unit 32. The processing unit 32 executes computer programs stored in a computer-readable, non-transitory recording medium such as the storage unit 34 to realize various processing functions of the processing unit 32.

[0050] The memory unit 34 also stores a plurality of first calibration curve data C1, a plurality of second calibration curve data C2, and a plurality of scaling data S. The plurality of first calibration curve data C1 is data for calculating a quality value (first quality value) based on the output of the first measurement device 2. The plurality of second calibration curve data C2 is data for calculating a quality value (second quality value) based on the output of the second measurement device 4. The plurality of scaling data S is data indicating the correlation between the first quality value and the second quality value. The plurality of scaling data S is generated in a quality value calculation process 32a executed by the processing unit 32.

[0051] The processing unit 32 has the function of executing a quality value calculation process 32a and a quality value management process 32b. The quality value calculation process 32a is a process for calculating a measured quality value of the target fruit KT based on the scaling data S and the first quality value of the target fruit KT. The target fruit KT is a fruit among the fruits K for which a measured quality value is to be calculated. The measured quality value is a quality value calculated by the quality value calculation process 32a, which is a quality value obtained by correcting the first quality value of the target fruit KT calculated based on the output of the first measuring device 2, and which has higher accuracy than the first quality value.

[0052] The quality value management process 32b is a process for managing the measured quality value obtained by the quality value calculation process 32a. These processes will be described in detail later.

[0053] [Quality Measurement Work of Fruit K in Farm Field F] Fig. 6 is a flowchart showing an example of quality measurement work of fruit K in farm field F. As shown in Fig. 6, in the quality measurement work of fruit K, first, quality measurement is performed using the first measuring device 2 and the second measuring device 4 (step S1 in Fig. 6).

[0054] FIG. 7 is a plan view of a field F. In FIG. 7, two mutually orthogonal directions are defined as the X direction and the Y direction. As shown in FIG. 7, one of the X directions is defined as the X1 direction, and the opposite direction of the X1 direction is defined as the X2 direction. One of the Y directions is defined as the Y1 direction, and the opposite direction of the Y1 direction is defined as the Y2 direction. Quality measurement by the measuring devices 2 and 4 is performed by having the agricultural machine 6 travel within the field F. In the field F shown in FIG. 7, for example, multiple trees T (15 trees in the illustrated example) are cultivated. Five trees T are arranged in the Y direction and three trees T are arranged in the X direction. If the Y direction is defined as the row direction, the trees T are arranged in three rows. Each row consists of five trees T. Each of the 15 trees T cultivated in the field F is assigned an ID as identification information. Integers from 1 to 15 are assigned to the 15 trees T as IDs. The quality measurement work of the fruits K in the field F is performed on all 15 trees T in the field F. Of the 15 trees T, the hatched trees T (trees T with IDs = 2, 4, 8, 12, and 14) are trees T for which the quality measurement of the fruits K is performed by both the first measuring device 2 and the second measuring device 4. The other trees T are trees T for which the quality measurement of the fruits K is performed only by the first measuring device 2. In other words, the first measuring device 2 performs quality measurement of the fruits K on all trees T, and the second measuring device 4 performs quality measurement of the fruits K on some of the trees T in the field F.

[0055] The quality measurement by the first measuring device 2 and the second measuring device 4 in step S1 may be performed by operation of the worker driving the agricultural machine 6, or may be performed by remote control based on a command from the operator 14. When the quality measurement is performed by the worker, the agricultural machine 6 travels by manual operation by the worker, and the first measuring device 2 and the second measuring device 4 perform quality measurement by manual operation by the worker. When the quality measurement is performed by remote control based on a command from the operator 14, the agricultural machine 6 travels by automatic driving, and the first measuring device 2 and the second measuring device 4 perform quality measurement by control based on a command from the operator 14. In this embodiment, a case where the worker driving the agricultural machine 6 performs quality measurement will be described.

[0056] A worker rides the agricultural machine 6 and travels along the side of a row of multiple trees T in the field F along the dashed line in FIG. 7 . Thus, the agricultural machine 6 approaches each tree T in order, starting with the tree T with ID=1. When the worker reaches the side of each tree T, he stops the agricultural machine 6 and performs quality measurement using the first measuring device 2 and the second measuring device 4. For example, as shown in FIG. 7 , when the worker stops the agricultural machine 6 next to the tree T with ID=1 (on the X1 direction), the worker operates the first measuring device 2 to capture an image of multiple fruits K on the tree T with ID=1, including the multiple fruits K in the imaging area. As a result, the first measuring device 2 outputs a spectral image of the multiple fruits K on the tree T with ID=1 and provides it to the management server 8. At this time, the first measuring device 2 adds the ID of the tree T to the output spectral image and transmits it. The first measuring device 2 adds the ID of the tree T to the spectral image in response to the operator's operation input. When the output (spectral image) of the first measuring device 2 is provided to the management server 8, the quality measurement of the fruit K on the tree T with ID=1 is completed.

[0057] After completing the quality measurement of the fruit K on the tree T with ID=1, the worker moves the agricultural machine 6 in the Y2 direction and stops the agricultural machine 6 to the side (X1 direction side) of the tree T with ID=2. Here, the tree T with ID=2 is the tree T for which quality measurement of the fruit K will be performed by both the first measuring device 2 and the second measuring device 4. Therefore, the worker operates the first measuring device 2 to capture an image of the multiple fruits K on the tree T with ID=2, including them in the imaging area. As a result, the first measuring device 2 outputs a spectral image of the multiple fruits K on the tree T with ID=2, and provides the spectral image with the ID of the tree T added to it to the management server 8.

[0058] The worker then operates the second measuring device 4 to perform quality measurement. Using the manipulator 12, the worker brings the second measuring head 26 of the second measuring device 4 close to the tree T. The worker brings the second measuring head 26 close to one of the multiple fruits K on the tree T that has been imaged by the first measuring device 2. The worker then operates the second measuring device 4 to cause the second measuring device 4 to receive light transmitted through the fruit K. The second measuring device 4 then outputs spectral information for the fruit K on the tree T with ID = 2 and provides it to the management server 8. In the following description, the fruit K measured by the second measuring device 4 is also referred to as the reference fruit KR. The second measuring device 4 also adds the ID of the tree T to the output spectral image and transmits it. The second measuring device 4 adds the ID of the tree T to the spectral information in response to a user operation. The second measuring device 4 may also add location information indicating the location of the reference fruit KR to the spectral information in response to a user operation. When the output of the first measuring device 2 and the output of the second measuring device 4 are given to the management server 8, the quality measurement of the tree T with ID=2 is completed.

[0059] As described above, the worker measures the quality of fruits K on multiple trees T in the order of the IDs. The outputs of the first measuring device 2 and the second measuring device 4 (spectral images and spectral information) are stored in the storage unit 34 of the management server 8. Therefore, the storage unit 34 stores spectral images assigned with IDs 1 to 15 and spectral information assigned with IDs 2, 4, 8, 12, and 14.

[0060] As shown in Fig. 6, after the quality measurements by the first measuring device 2 and the second measuring device 4 are completed, the management server 8 then performs a quality value calculation process (step S2 in Fig. 6). Fig. 8 is a flowchart showing an example of the quality value calculation process. In the quality value calculation process, the measured quality values ​​of the fruits K of the 15 trees T are calculated.

[0061] 8, first, the processing unit 32 of the management server 8 calculates a first quality value and a second quality value (step S21 in FIG. 8). The processing unit 32 calculates the first quality value based on the output (spectral image) of the first measurement device 2. The processing unit 32 also calculates the second quality value based on the output (spectral information) of the second measurement device 4.

[0062] The processing unit 32 determines the first quality value and the second quality value by a spectroscopic analysis technique using near-infrared light. Quantitative analysis by spectroscopic analysis using near-infrared light is an analytical method that utilizes the fact that the degree of light absorption in a wavelength band where a specific component significantly absorbs light varies depending on the content of the specific component. The processing unit 32 determines the sugar content, acidity, pH, and polyphenol content as the first quality value and the second quality value. The wavelength bands in the near-infrared region used when analyzing the sugar content, acidity, pH, and polyphenol content are set in advance.

[0063] First, the process of calculating the first quality value will be described. The processing unit 32 obtains spectral images of wavelength bands corresponding to each quality value from the spectral image output by the first measurement device 2. In this embodiment, the processing unit 32 obtains a spectral image of a wavelength band corresponding to sugar content, a spectral image of a wavelength band corresponding to acidity, a spectral image of a wavelength band corresponding to pH, and a spectral image of a wavelength band corresponding to polyphenols. Note that a spectral image is an image in which the brightness of pixels constituting the image represents the light intensity in a predetermined wavelength band.

[0064] The following describes the process performed by the processing unit 32 when determining the sugar content as the first quality value from the spectral image in the wavelength band corresponding to the sugar content. The processing unit 32 identifies image regions of multiple fruit K portions from the spectral image in the wavelength band corresponding to the sugar content. The brightness of pixels included in these multiple image regions indicates the light intensity in the wavelength band corresponding to the sugar content. In other words, the brightness of a pixel indicates the amount of light absorption.

[0065] The processing unit 32 calculates a representative value of the luminance obtained from one spectral image based on the luminance of pixels included in the image regions of the plurality of fruits K. For example, the processing unit 32 may use the average value of the luminance of pixels included in the image regions of the plurality of fruits K as the representative value. Furthermore, a threshold value can be set for the luminance in order to identify the image regions of the plurality of fruits K in the spectral image. In this case, the processing unit 32 can identify the region equal to or greater than the threshold as the image region of the fruit K.

[0066] Furthermore, when the reference fruit KR can be identified from the spectral images of the trees T with IDs 2, 4, 8, 12, and 14 using position information indicating the position of the reference fruit KR, the processing unit 32 may calculate a representative value of brightness using pixels included in an image area that includes only the portion of the reference fruit KR. In this case, the brightness of the reference fruit KR can be calculated.

[0067] Next, the processing unit 32 references the first calibration curve data C1 and calculates the sugar content based on the brightness (representative value). FIG. 9 is a diagram showing an example of multiple first calibration curve data C1. FIG. 9 schematically shows the first calibration curve data C1. As shown in FIG. 9, among the multiple first calibration curve data C11 to C14, the first calibration curve data C11 shows the correlation between the brightness of pixels in the spectral image in a wavelength band corresponding to the sugar content and the sugar content. Although omitted from FIG. 9, the first calibration curve data C12 shows the correlation between the brightness of pixels in the spectral image in a wavelength band corresponding to the acidity and the acidity. The first calibration curve data C13 shows the correlation between the brightness of pixels in the spectral image in a wavelength band corresponding to the pH and the pH. The first calibration curve data C14 shows the correlation between the brightness of pixels in the spectral image in a wavelength band corresponding to the polyphenols and the polyphenol content.

[0068] The multiple first calibration curve data C1 may be any data that can be used to calculate a first quality value from the brightness of the pixels of the spectroscopic image, and may be a table obtained by experiment, simulation, etc., or a linear or nonlinear mathematical formula.

[0069] When determining the sugar content as the first quality value, the processing unit 32 refers to the first sugar content calibration curve data C11 and determines the sugar content corresponding to the brightness determined above. This sugar content is the first quality value.

[0070] The processing unit 32 also determines the acidity, pH, and polyphenol content in addition to the sugar content using a method similar to that for sugar content. As described above, the processing unit 32 determines the first quality values ​​(sugar content, acidity, pH, and polyphenol content) for each of the trees T with IDs 1 to 15 based on the output of the first measurement device 2.

[0071] Next, a process for calculating the second quality value will be described. The processing unit 32 calculates the absorbance of a wavelength band corresponding to each quality value from the spectral information output by the second measurement device 4. The absorbance is a value indicating the degree of collection of light in a predetermined wavelength band when the light from the light source 27 is used as a reference.

[0072] The following describes the process performed by the processing unit 32 when determining the sugar content as the first quality value. The processing unit 32 references the second calibration curve data C2 and determines the sugar content based on the absorbance (representative value).

[0073] FIG. 10 is a diagram showing an example of multiple second calibration curve data C2. FIG. 10 schematically shows the second calibration curve data C2. As shown in FIG. 10, among the multiple second calibration curve data C21 to C24, the second calibration curve data C21 shows the correlation between the sugar content and the absorbance in the wavelength band corresponding to the sugar content. Although omitted from FIG. 10, the second calibration curve data C22 shows the correlation between the acidity and the absorbance in the wavelength band corresponding to the acidity. The second calibration curve data C23 shows the correlation between the pH and the absorbance in the wavelength band corresponding to the pH. The second calibration curve data C24 shows the correlation between the polyphenol content and the absorbance in the wavelength band corresponding to the polyphenols.

[0074] The multiple second calibration curve data C2 may be any data that allows the second quality value to be calculated from the absorbance, and may be a table obtained by experiment, simulation, etc., or a linear or nonlinear mathematical formula.

[0075] When determining the sugar content as the second quality value, the processing unit 32 refers to the second sugar content calibration curve data C21 and determines the sugar content corresponding to the absorbance determined above. This sugar content is the second quality value.

[0076] The processing unit 32 also determines the acidity, pH, and polyphenol content in addition to the sugar content using a method similar to that for sugar content. As described above, the processing unit 32 determines the second quality values ​​(sugar content, acidity, pH, and polyphenol content) for each of the trees T with IDs 2, 4, 8, 12, and 14 based on the output of the second measurement device 4.

[0077] In this way, the processing unit 32 calculates the first quality value for each of the trees T with IDs 1 to 15 and the second quality value for each of the trees T with IDs 2, 4, 8, 12, and 14 (step S21 in FIG. 8). Next, the processing unit 32 generates scaling data S as shown in FIG. 8 (step S22 in FIG. 8).

[0078] The scaling data S is calculated based on the first quality values ​​and the second quality values ​​obtained when some of the fruits K (reference fruits KR) are measured by both the first measuring device 2 and the second measuring device 4. That is, the scaling data S is calculated based on the first quality values ​​for the trees T with IDs = 2, 4, 8, 12, and 14 and the second quality values ​​for the trees T with IDs = 2, 4, 8, 12, and 14. Hereinafter, the first quality values ​​for IDs = 2, 4, 8, 12, and 14 will also be referred to as reference first quality values, and the second quality values ​​for IDs = 2, 4, 8, 12, and 14 will also be referred to as reference second quality values.

[0079] The first quality value for each of the trees T with IDs = 2, 4, 8, 12, and 14 may be the average of the quality values ​​of multiple fruits K including the reference fruit KR. Also, the first quality value for each of the trees T with IDs = 2, 4, 8, 12, and 14 may include only the quality value of the reference fruit KR.

[0080] 11 is a diagram showing an example of scaling data S. As shown in FIG. 11, among the multiple scaling data S1 to S4, scaling data S1 shows the correlation between the sugar content measured by the first measurement device 2 and the sugar content measured by the second measurement device 4. Although omitted from FIG. 11, scaling data S2 shows the correlation between the acidity measured by the first measurement device 2 and the acidity measured by the second measurement device 4. Scaling data S3 shows the correlation between the pH measured by the first measurement device 2 and the pH measured by the second measurement device 4. Scaling data S4 shows the correlation between the polyphenol content measured by the first measurement device 2 and the polyphenol content measured by the second measurement device 4.

[0081] The scaling data S1 includes a line L showing the correlation between the sugar content measured by the first measuring device 2 and the sugar content measured by the second measuring device 4. The horizontal axis of the scaling data S1 is the sugar content measured by the first measuring device 2 (first quality value), and the vertical axis is the sugar content measured by the second measuring device 4 (second measured value). Line L is a straight line passing through points VU and VL. The five points in FIG. 11 are points plotted for a reference fruit KR having a reference first quality value and a reference second quality value. Of the five points, point VU is the point on the reference fruit KR having the maximum sugar content measured by the multiple first measuring devices 2 (multiple reference first quality values) and the maximum sugar content measured by the multiple second measuring devices 4 (multiple reference second quality values). Point VL is a point on the reference fruit KR that has the smallest sugar content (multiple reference first quality values) measured by multiple first measuring devices 2 and the smallest sugar content (multiple reference second quality values) measured by multiple second measuring devices 4.

[0082] In this manner, in this embodiment, the scaling data S1 is calculated based on a plurality of reference first quality values ​​and a plurality of reference second quality values ​​obtained when a plurality of reference fruits KR are measured by both the first measuring device 2 and the second measuring device 4. In this case, the first quality values ​​and the second quality values ​​are associated with each other by the plurality of reference fruits KR. This allows for further improvement in the accuracy of the scaling data S.

[0083] Furthermore, the scaling data S1 is calculated based on the maximum and minimum values ​​of the multiple reference first quality values ​​(outputs of the first measurement device 2) and the maximum and minimum values ​​of the multiple reference second quality values. In this case, the reference first quality value and the reference second quality value are associated with each other based on at least the respective maximum and minimum values. Therefore, the scaling data S1 can be easily calculated without reducing accuracy.

[0084] The other scaling data S2 to S4 are calculated in the same manner as the scaling data S1. The scaling data S may be any data indicating the correlation between the first quality value (output of the first measurement device 2) and the second quality value, and may be a table or a mathematical formula.

[0085] In this way, the processing unit 32 generates the scaling data S (step S22 in FIG. 8). Next, the processing unit 32 calculates a measurement quality value as shown in FIG. 8 (step S23 in FIG. 8).

[0086] The processing unit 32 selects a target fruit KT from the fruits K on the trees T with IDs 1 to 15, and determines a measured quality value of the target fruit KT based on the scaling data S and the first quality value of the target fruit KT. The processing unit 32 selects the target fruit KT in order from the fruits K on the trees T with IDs 1 to 15, and repeatedly determines the measured quality values. In this way, the processing unit 32 determines the measured quality value of each of the fruits K on the trees T with IDs 1 to 15. The processing unit 32 references the scaling data S and determines a second quality value corresponding to the first quality value of the target fruit KT. The processing unit 32 determines the second quality value corresponding to this first quality value as the measured quality value of the target fruit KT.

[0087] For example, a case will be described in which the sugar content is calculated as a measured quality value of fruit K on tree T with ID = 1. FIG. 12 is a diagram showing the manner in which the processing unit 32 calculates the measured quality value of fruit K on tree T with ID = 1. The processing unit 32 selects fruit K on tree T with ID = 1 as target fruit KT. Next, the processing unit 32 references the scaling data S1 and calculates the sugar content (vertical axis in FIG. 12) measured by the second measuring device 4 on diagram L that corresponds to the sugar content (horizontal axis in FIG. 12), which is the first quality value of fruit K (target fruit KT) with ID = 1. The processing unit 32 sets the sugar content measured by the second measuring device 4 that corresponds to the first quality value as the measured quality value. In this way, the vertical axis of the scaling data S1 indicates the sugar content measured by the second measuring device 4 as well as the measured quality value calculated from the first quality value.

[0088] The processing unit 32 also determines the measured quality values ​​other than sugar content, such as acidity, pH, and polyphenol content, using a method similar to that for sugar content. The processing unit 32 repeats the same process for the fruits K on the trees T with IDs 1 to 15, and determines the measured quality values ​​for each of the fruits K on the trees T with IDs 1 to 15. Note that for the fruits K on the trees T with IDs 2, 4, 8, 12, and 14, including the reference fruit KR, the second quality values ​​measured by the second measuring device 4 may be used as the measured quality values ​​as they are.

[0089] In this way, the processing unit 32 calculates the measurement quality value (step S23 in FIG. 8). Next, the processing unit 32 proceeds to step S3 in FIG. 6 and executes quality control processing for managing the measurement quality value (step S3 in FIG. 6).

[0090] According to the above configuration, a second quality value corresponding to the first quality value (output of the first measuring device) of the target fruit KT can be calculated based on the scaling data S, and the calculated second quality value can be used as the measured quality value of the target fruit KT. As a result, the first quality value of the target fruit KT can be corrected to a value equivalent to the quality value of the second measuring device 4, which has higher measurement accuracy than the first measuring device 2, and the quality measurement accuracy can be easily improved.

[0091] Furthermore, in this embodiment, the first measuring head 22 of the first measuring device 2 is disposed at a first distance D1 from the fruit K that is longer than the second distance D2 of the second measuring head 26, and therefore is able to measure a wider area at once than the second measuring head 26, providing greater convenience. Therefore, according to this embodiment, convenience can be improved by measuring the quality of the fruits K throughout the entire field F using the first measuring device 2 while improving the accuracy of quality measurement.

[0092] That is, in this embodiment, the first measuring head 22 includes a hyperspectral camera (spectroscopic camera), and therefore the first measuring device 2 can measure a wider range at once and is more convenient than the second measuring device 4, which includes a light source 27 and a spectroscope 28 that disperses transmitted light. Therefore, according to this embodiment, the convenience of using the first measuring device 2 can be improved while improving the accuracy of quality measurement.

[0093] [Others] The embodiments disclosed herein should be considered illustrative in all respects and not restrictive. For example, in the above embodiment, the scaling data S is calculated based on the reference first quality value and the reference second quality value, and in step S23 of FIG. 8, the measured quality value of fruit K is calculated based on the first quality value output from the first measurement device 2. However, the scaling data S may be calculated based on the luminance of the first measurement device 2 when measuring the reference fruit KR and the reference second quality value, and in step S23 of FIG. 8, the measured quality value of fruit K may be calculated based on the luminance output from the first measurement device 2. In this case, the scaling data S becomes data showing the correlation between the luminance of the first measurement device 2 and the second quality value, as shown in FIG. 13. Furthermore, in this case, the first calibration curve data C1 is unnecessary, thereby reducing the computational load in the processing unit 32.

[0094] In the above embodiment, the first measurement head 22 includes a hyperspectral camera. However, the first measurement head 22 may include a multispectral camera instead of a hyperspectral camera. A multispectral camera is a spectroscopic camera that can detect light intensity in a smaller number of wavelength bands than a hyperspectral camera. A multispectral camera can be used in the same way as a hyperspectral camera as long as it can detect the required wavelength bands.

[0095] In the above embodiment, the diagram L of the scaling data S is a straight line passing through the points VU and VL, and is calculated based on the maximum and minimum values ​​of the plurality of reference first quality values ​​and the maximum and minimum values ​​of the plurality of reference second quality values. However, the present invention is not limited to this. For example, an approximate formula may be calculated using a plurality of points determined by the reference first quality values ​​and the reference second quality values, and this approximate formula may be used as the scaling data S.

[0096] The scope of the present invention is defined by the claims, not by the meaning described above, and is intended to include meanings equivalent to the claims and all modifications within the scope thereof.

[0097] REFERENCE SIGNS LIST 1 Measurement system 2 First measuring device 4 Second measuring device 6 Agricultural machine 8 Management server 10 Management terminal 12 Manipulator 14 Operator 18 Communication device 20 Vehicle control device 22 First measuring head 24 First control unit 26 Second measuring head 26a Light-emitting unit 26b Light-receiving unit 26c Head body 26c1 Annular tip surface 26c2 Hole 27 Light source 28 Spectrometer 30 Second control unit 32 Processing unit 32a Quality value calculation processing 32b Quality value management processing 34 Memory unit 36 ​​Communication device A Imaging area BS Wireless base station C1, C11, C12, C13, C14 First calibration curve data C2, C21, C22, C23, C24 Second calibration curve data D1 First interval D2 Second interval F Field K Fruit KT Target fruit KR Reference fruit L Line diagram NW Public network S, S1, S2, S3, S4 Scaling data T Tree k1 Granular fruit

Claims

1. A measurement system comprising: a first measuring device that measures the quality of a plurality of fruits; a second measuring device that has a configuration different from the first measuring device and measures the quality of some of the plurality of fruits; and a processing device that determines a measured quality value of a target fruit from the plurality of fruits, wherein the processing device has a processing unit that executes processing to determine the measured quality value based on scaling data indicating a correlation between the output of the first measuring device and a quality value based on the output of the second measuring device, and the output of the first measuring device when the quality of the target fruit is measured.

2. The measurement system according to claim 1, wherein the correlation indicated by the scaling data is a correlation between a quality value based on the output of the first measurement device and a quality value based on the output of the second measurement device.

3. A measurement system as described in claim 1 or claim 2, wherein the first measuring device has a first measuring head that measures the plurality of fruits at a first interval, and the second measuring device has a second measuring head that measures the portion of the fruits at a second interval that is shorter than the first interval.

4. The measurement system according to claim 3, further comprising a moving mechanism for moving the second measurement head to a measurement position where the distance to the portion of fruit is the second distance.

5. A measurement system as described in any one of claims 1 to 4, wherein the first measuring device is equipped with a spectroscopic camera that receives reflected light from the plurality of fruits, and the second measuring device is equipped with a light source that irradiates light onto some of the fruits, and a spectroscope that separates the transmitted light from some of the fruits.

6. The measurement system according to any one of claims 1 to 5, wherein the processing unit further executes a process of generating the scaling data.

7. The measurement system described in any one of claims 1 to 6, wherein the scaling data is obtained based on a plurality of outputs of the first measuring device obtained when the portion of fruit is measured by both the first measuring device and the second measuring device, and a plurality of reference quality values ​​based on the plurality of outputs of the second measuring device.

8. The measurement system of claim 7, wherein the scaling data is determined based on a maximum and a minimum value among a plurality of outputs of the first measurement device and a maximum and a minimum value among a plurality of reference quality values ​​of the second measurement device.

9. The measurement system according to any one of claims 1 to 8, further comprising an agricultural machine on which the first measurement device and the second measurement device are mounted.

10. A processing device for determining a measured quality value of a target fruit among a plurality of fruits, the processing device comprising: a processing unit that executes a process for determining the measured quality value based on scaling data indicating a correlation between the output of a first measuring device that measures the quality of the plurality of fruits and a quality value based on the output of a second measuring device that has a configuration different from that of the first measuring device and measures the quality of a portion of the plurality of fruits, and the output of the first measuring device when measuring the quality of the target fruit.

11. A method for determining a measured quality value of a target fruit among a plurality of fruits, the method comprising: determining the measured quality value based on scaling data indicating a correlation between an output of a first measuring device that measures the quality of the plurality of fruits and a quality value based on the output of a second measuring device that has a configuration different from that of the first measuring device and measures the quality of a portion of the plurality of fruits, and the output of the first measuring device when measuring the quality of the target fruit.

12. A computer program for causing a computer to execute a process for determining a measured quality value of a target fruit among a plurality of fruits, the computer program causing the computer to execute a step of determining the measured quality value based on scaling data indicating a correlation between an output of a first measuring device that measures the quality of the plurality of fruits and a quality value based on the output of a second measuring device that has a configuration different from that of the first measuring device and measures the quality of a portion of the plurality of fruits, and the output of the first measuring device when measuring the quality of the target fruit.

Images