Self-adaptive fertilization management method and management system based on peanut growth detection

Abstract

The invention discloses a self-adaptive fertilization management method and management system based on peanut growth detection, and relates to the technical field of agricultural management. The method comprises the following steps: selecting a sample plant of which the growth state reaches the standard as a standard sample plant, and adding the growth states of a plurality of parts of the standard sample plant and the application amount of each type of fertilizer per unit area in the growth stage of the round into a comparison library; formulating the spreading amount of each type of fertilizer in unit area of each standard sample plant in the next round of growth stage for the standard sample plants; dividing the peanut planting area into a plurality of homomorphic growth subareas according to the positions of the sample plants at the detection points and the growth states of the plurality of parts; and screening and comparing the growth states of the multiple parts of the non-standard sample plants with the comparison library to obtain the application amount of each type of fertilizer in unit area suitable for each non-standard sample plant in the next growth stage. The peanut yield and quality are improved on the premise of reducing the management cost.

Description

Technical Field

[0001] The invention belongs to the technical field of agricultural management, and in particular relates to an adaptive fertilization management method and management system based on peanut growth detection. Background Art

[0002] With the advancement of agricultural modernization, precision agriculture technology has gradually become an important means to improve agricultural production efficiency, reduce production costs, and reduce environmental pollution. Among them, fertilization management, as a key link in agricultural production, directly affects the yield and quality of crops. Traditional fertilization methods mainly rely on experience and judgment, lack scientific basis, and easily lead to excessive or insufficient fertilization, which not only causes waste of resources, but also may have a negative impact on the soil and environment.

[0003] As an important oil crop and cash crop, peanuts have a phased demand for nutrients during their growth process. The demand for nutrients such as nitrogen, phosphorus, and potassium varies at different growth stages and is affected by multiple factors such as soil conditions and climate. Summary of the invention

[0004] The object of the present invention is to provide an adaptive fertilization management method and management system based on peanut growth detection, which can perform adaptive and precise fertilization based on the actual growth conditions of substandard peanut plants, thereby improving peanut yield and quality while reducing management costs.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention provides an adaptive fertilization management method based on peanut growth detection, comprising: The peanut planting area is evenly divided into multiple planting grids, and detection points are selected in each planting grid; At the end of each growth stage of the peanut plant, the growth status of multiple parts of the sample plant at the detection point is obtained; Select sample plants whose growth status meets the standard as qualified sample plants, and add the growth status of multiple parts of the qualified sample plants and the application amount of each type of fertilizer per unit area in this round of growth stage to the comparison library; The application amount of each type of fertilizer per unit area of ​​each qualified sample plant in the next round of growth stage is determined for the qualified sample plants; Dividing the peanut planting area into a plurality of isomorphic growth zones according to the position of the sample plant at the detection point and the growth status of multiple parts; The growth status of multiple parts of the non-standard sample plants are screened and compared with the comparison library to obtain the application amount of each type of fertilizer per unit area suitable for each non-standard sample plant in the next growth stage.

[0006] The present invention also discloses an adaptive fertilization management method based on peanut growth detection, comprising: Obtain the location of each sample plant and the detection point where it is located, as well as the coverage of the planting grid where each detection point is located; Receive the application amount of each type of fertilizer per unit area for the qualified sample plants in the next round of growth, and the application amount of each type of fertilizer per unit area for each non-qualified sample plant in the next round of growth; For each planting grid coverage area where the qualified sample plants and non-qualified sample plants are located, fertilizer is applied according to the corresponding application amount of each type of fertilizer per unit area.

[0007] The present invention also discloses an adaptive fertilization management system based on peanut growth detection, comprising: A detection unit, used to detect the growth status of multiple parts of the sample plant; A fertilization management unit is used to evenly divide the peanut planting area into multiple planting grids and select detection points in each planting grid; At the end of each growth stage of the peanut plant, the growth status of multiple parts of the sample plant at the detection point is obtained; Select sample plants whose growth status meets the standard as qualified sample plants, and add the growth status of multiple parts of the qualified sample plants and the application amount of each type of fertilizer per unit area in this round of growth stage to the comparison library; The application amount of each type of fertilizer per unit area of ​​each qualified sample plant in the next round of growth stage is determined for the qualified sample plants; Dividing the peanut planting area into a plurality of isomorphic growth zones according to the position of the sample plant at the detection point and the growth status of multiple parts; Screening and comparing the growth status of multiple parts of the non-standard sample plants with the comparison library to obtain the application amount of each type of fertilizer per unit area suitable for each non-standard sample plant in the next round of growth stage; The field management unit is used to obtain the location of each sample plant and the detection point where it is located, as well as the coverage of the planting grid where each detection point is located; Receive the application amount of each type of fertilizer per unit area for the qualified sample plants in the next round of growth, and the application amount of each type of fertilizer per unit area for each non-qualified sample plant in the next round of growth; For each planting grid coverage area where the qualified sample plants and non-qualified sample plants are located, fertilizer is applied according to the corresponding application amount of each type of fertilizer per unit area.

[0008] The present invention compares the growth states of numerous non-standard sample plants through a fertilization management unit, finds the closest standard sample plants from the comparison library, and adopts its fertilizer application plan in the next growth stage. In this process, the present invention automatically adapts to the growth states of the non-standard sample plants, and completes the formulation of the fertilizer application plan for the next growth cycle. During the whole process, staff only need to perform manual intervention on a small number of standard sample plants, thereby improving peanut yield and quality while reducing manual management costs.

[0009] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0011] Figure 1 A schematic diagram of functional units and information flows of an adaptive fertilization management system based on peanut growth detection in one embodiment of the present invention; Figure 2 A schematic diagram of a step flow of a fertilization management unit according to an embodiment of the present invention; Figure 3 A schematic diagram of a step flow of a field management unit according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the step flow of step S5 in one embodiment of the present invention; Figure 5 This is a schematic diagram of a step flow chart of step S51 in an embodiment of the present invention; Figure 6 This is a schematic diagram of the step flow of step S6 in one embodiment of the present invention; In the accompanying drawings, the components represented by the reference numerals are listed as follows: 1-Detection unit, 2-Fertilization management unit, 3-Field management unit. DETAILED DESCRIPTION

[0012] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0013] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0014] See also Figures 1 to 3 As shown, the present invention provides an adaptive fertilization management system based on peanut growth detection, which is divided into a detection unit 1, a fertilization management unit 2 and a field management unit 3 from the functional unit. The detection unit 1 in the system can be a virtual functional unit, which is operated by a field management personnel. Specifically, step S01 can be executed to detect the growth status of multiple parts of the sample plant. The growth status of the plant part can be any multiple of plant height, average stem diameter, stem root tip difference, average leaf area, number of leaves, average leaf color depth, average leaf thickness, flower bud differentiation degree, number of flower buds and average flower bud diameter.

[0015] After the detection unit 1 completes the peanut detection in the planting area, the fertilization management unit 2 can then execute step S1 to evenly divide the peanut planting area into multiple planting grids, and select a detection point in each planting grid. The planting network can be a square plot structure, and the detection point can be the center point of the planting network. The fertilization management unit can then receive the detection results of the detection unit 1. Since peanuts need targeted fertilization at different growth stages, step S2 can then be executed to obtain the growth status of multiple parts of the sample plant at the detection point at the end of each growth stage of the peanut plant.

[0016] Since peanut plants need to use fertilizers of different proportions and concentrations in different growth states, it is necessary for staff to adjust the fertilization plan for the growing peanut plants. However, due to the huge number of peanut plants in the planting area, field work managers cannot make adjustments one by one, which not only consumes a lot of time, but also is unbearable in terms of economic cost. This scheme formulates the next stage fertilization plan for a very small number of peanut plants with good growth, which can reduce the workload of formulating fertilization plans. Specifically, step S3 can be performed first to select sample plants with qualified growth status as qualified sample plants, and the growth status of multiple parts of the qualified sample plants and the application amount of each type of fertilizer per unit area in this round of growth stage are added to the comparison library. The fertilizer types include nitrogen fertilizer, phosphate fertilizer, potash fertilizer, calcium fertilizer, organic fertilizer, borax, ammonium molybdate and zinc sulfate. Next, step S4 can be performed to formulate the application amount of each type of fertilizer per unit area for each qualified sample plant in the next round of growth stage for the qualified sample plants. Since in this process, only a small number of qualified sample plants need to be fertilized in the next growth phase, the workload of staff is greatly reduced.

[0017] See also Figure 2 and 4 As shown, since the staff only formulates fertilization plans for a small number of qualified sample plants, adaptive comparison is required for other sample plants to find suitable fertilization calculations, that is, step S5 can be executed next to divide the peanut planting area into several isomorphic growth zones according to the location of the sample plants at the detection point and the growth status of multiple parts. Specifically, step S51 can be executed first to divide multiple sample plants with consistent growth status into the same plant combination according to the difference in the growth status of each part of each sample plant.

[0018] See also Figure 5 As shown, in the process of grouping plant combinations, it is necessary to fully consider the difference in growth parameters of different parts of each sample plant, that is, to quantify the difference in plant growth status, and the cumulative value of the difference in growth status parameters of each corresponding part of two sample plants can be used as the difference in growth status of the two sample plants. In view of this, when classifying the growth status of sample plants, step S511 can be first executed to select several sample plants that meet the standards and sample plants that do not meet the standards as benchmark sample plants. Next, step S512 can be executed to use the cumulative value of the difference in growth status parameters of each corresponding part of the two sample plants as the difference in growth status of the two sample plants, and calculate the difference in growth status between each benchmark sample plant and other sample plants. Next, step S513 can be executed for each sample plant other than the benchmark sample plant, and the benchmark sample plant with the smallest difference in growth status difference can be divided into the same plant combination.

[0019] Please continue reading Figure 5 As shown, since the growth status of the sample plants in the plant combination obtained by the above classification may not be completely and fully consistent, it is necessary to verify the consistency. Specifically, step S514 can be executed first to determine whether there are sample plants that meet the standards and sample plants that do not meet the standards mixed and classified in the same plant combination in each plant combination after division. If not, it means that the growth status of the sample plants in each plant combination is consistent, so step S515 can be executed next to determine that the growth status of the sample plants in the plant combination obtained by the current division is consistent. If yes, then the plant combination with consistent growth status can be re-divided.

[0020] In the process of re-dividing the plant combinations, it is first necessary to reselect the benchmark sample plants. First, step S516 can be executed to calculate the cumulative value of the difference in growth status between each sample plant and all other sample plants in the plant combination in each plant combination. Next, step S517 can be executed to use the sample plant with the smallest cumulative value of the difference in growth status between each plant combination and all other sample plants in the plant combination as the reselected benchmark sample plant. Next, steps S512 to S514 can be executed to obtain the re-divided plant combination according to the re-selected benchmark sample plant division, and determine whether there is a situation in which qualified sample plants and unqualified sample plants are mixed and classified in the same plant combination in the re-divided plant combination. The above process continuously optimizes the sample plants in the plant combination through continuous iterative updates until a plant combination with consistent growth status is obtained.

[0021] In order to supplement the implementation process of the above steps S511 to S517, the source code of some functional modules is provided, and the explanation is compared in the comment section. In order to avoid the leakage of data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0022] #include <iostream> #include <vector> #include <map> #include <cmath> #include <algorithm> / / Define the growth status structure of the peanut plant struct PlantStatus { double height; / / Plant height double stemDiameter; / / Average stem diameter double stemTipDifference; / / Stem root tip difference double leafArea; / / average leaf area int leafCount; / / Number of leaves double leafColorDepth; / / Average color depth of leaves double leafThickness; / / Average leaf thickness double budDifferentiation; / / Flower bud differentiation degree int budCount; / / number of flower buds double budDiameter; / / Average diameter of flower buds }; / / Define the detection point structure struct DetectionPoint { int gridID; / / grid ID PlantStatus status; / / Plant growth status bool is reaches the standard; / / Whether it reaches the standard }; / / Function: Calculate the convergence rate of two plant states (the cumulative value of the difference) double calculateConvergenceRate(const PlantStatus&a, constPlantStatus&b) { double difference = 0.0; difference += fabs(a.height - b.height); difference += fabs(a.stemDiameter - b.stemDiameter); difference += fabs(a.stemTipDifference - b.stemTipDifference); difference += fabs(a.leafArea - b.leafArea); difference += fabs(a.leafCount - b.leafCount); difference += fabs(a.leafColorDepth - b.leafColorDepth); difference += fabs(a.leafThickness - b.leafThickness); difference += fabs(a.budDifferentiation - b.budDifferentiation); difference += fabs(a.budCount - b.budCount); difference += fabs(a.budDiameter - b.budDiameter); returndifference ; } / / Function: Select the reference sample plant std::vector <detectionpoint>selectBasePlants(const std::vector <detectionpoint>&plants, int baseCount) { std::vector <detectionpoint>basePlants; / / Simple implementation: select the first baseCount plants as the benchmark sample plants for (int i = 0; i <baseCount&&i<plants.size(); ++i) { basePlants.push_back(plants[i]); } return basePlants; } / / Function: Divide plant combinations std::vector <std::vector <detectionpoint>>groupPlants(const std::vector <detectionpoint>&plants, const std::vector <detectionpoint>&basePlants) { std::vector<std::vector <detectionpoint>>groups(basePlants.size()); / / Traverse all sample plants and assign them to the group where the benchmark sample plant with the highest convergence rate is located for (const auto&plant : plants) { doubleminConvergenceRate = -1.0; int bestGroupIndex = -1; / / Calculate the difference between each benchmark sample plant for (size_t i = 0; i <basePlants.size(); ++i) { double rate = calculateConvergenceRate(plant.status, basePlants[i].status); if (rate <minConvergenceRate) { minConvergenceRate = rate; bestGroupIndex = i; } } / / Assign sample plants to corresponding groups if (bestGroupIndex != -1) { groups[bestGroupIndex].push_back(plant); } } return groups; } / / Function: Check whether the plant combination is mixed with qualified and unqualified sample plants bool isMixedGroup(const std::vector <detectionpoint>&group) { bool has_reached_standard = false; bool has not reached the target = false; for (const auto&plant : group) { if (plant.is reaches the standard) has reached the standard = true; else has failed to meet the standard = true; if (has reached the target &&has not reached the target) return true; } return false; } / / Function: Reselect benchmark sample plants and re-divide groups std::vector <std::vector <detectionpoint>>regroupPlants(const std::vector<std::vector <detectionpoint>>&groups) { std::vector<std::vector <detectionpoint>>newGroups; for (const auto&group : groups) { if (isMixedGroup(group)) { / / If the group is mixed, reselect the benchmark sample plants std::vector <double>convergenceSums(group.size(), 0.0); / / Calculate the cumulative difference between each sample plant and other sample plants in the group for (size_t i = 0; i <group.size(); ++i) { for (size_t j = 0; j <group.size(); ++j) { if (i != j) { convergenceSums[i] += calculateConvergenceRate(group[i].status, group[j].status); } } } / / Select the sample plant with the smallest cumulative value of convergence as the new benchmark sample plant auto minIt = std::, min_element(convergenceSums.begin(),convergenceSums.end()); int baseIndex = std::distance(convergenceSums.begin(),minIt); auto newBasePlant = group[baseIndex]; / / Re-divide the groups auto newGroup = groupPlants(group, {newBasePlant}); newGroups.insert(newGroups.end(), newGroup.begin(), newGroup.end()); } else { / / If there is no mixing in the group, just keep it newGroups.push_back(group); } } return newGroups; } int main() { / / Detection point data std::vector <detectionpoint>detectionPoints = { {1, {35.0, 5.0, 2.0, 50.0, 12, 0.8, 0.2, 0.9, 6, 1.0}, true}, {2, {34.0, 5.1, 2.1, 51.0, 13, 0.81, 0.21, 0.91, 7, 1.1}, true}, {3, {28.0, 4.0, 1.5, 40.0, 8, 0.7, 0.15, 0.8, 4, 0.8}, false}, {4, {29.0, 4.1, 1.6, 41.0, 9, 0.71, 0.16, 0.81, 5, 0.9}, false}, {5, {40.0, 6.0, 2.5, 60.0, 15, 0.9, 0.25, 1.0, 8, 1.2}, true} }; / / Step 1: Select the benchmark sample plants int baseCount = 2; / / Select 2 base sample plants auto basePlants = selectBasePlants(detectionPoints, baseCount); / / Step 2: Divide the plant combinations auto groups = groupPlants(detectionPoints, basePlants); / / Step 3: Check and re-divide the mixed groups auto finalGroups = regroupPlants(groups); / / Output the final division result for (size_t i = 0; i <finalGroups.size(); ++i) { std::cout<<"Plant combination"< for (const auto&plant : finalGroups[i]) { std::cout< <plant.gridID<<" "; } ​std::cout< <std::endl; } return 0; } This code implements the function of dividing plant combinations according to growth status differences in the adaptive fertilization management method based on peanut growth detection. First, select benchmark sample plants from the sample plants that meet the standards and those that do not meet the standards, and calculate the difference in growth status between each sample plant and the benchmark sample plant. Then divide each sample plant into the group where the benchmark sample plant with the highest convergence rate is located. Then check whether each group is mixed with sample plants that meet the standards and those that do not meet the standards. If mixed, reselect the benchmark sample plants and re-divide the groups until the growth status of the sample plants in the group is consistent. Finally, output the divided plant combination to provide a basis for subsequent isomorphic growth zoning and precision fertilization.

[0023] Please continue reading Figure 2 and 4 As shown, after the plant combination is obtained by grouping, the position of the sample plants needs to be considered. This is because only the detection points with close positions have similar soil components and are consistent. Therefore, step S52 can be executed next to divide the multiple sample plants with adjacent positions in the plant combination into the same homomorphic plant combination according to the position of each sample plant at the detection point. Finally, step S53 can be executed to treat the range of multiple planting grids where the sample plants contained in the same homomorphic plant combination are located as a homomorphic growth partition. After the division, the peanut plants in the same homomorphic growth partition not only have the same growth state, but also have similar soils for growth.

[0024] See also Figure 2 and 6 As shown, step S6 can be executed next to screen and compare the growth status of multiple parts of the non-standard sample plants with the comparison library to obtain the application amount of each type of fertilizer per unit area suitable for each non-standard sample plant in the next round of growth stage. Specifically, step S61 can be executed first to calculate the difference in growth status between the non-standard sample plant and each standard sample plant in the comparison library for each non-standard sample plant. Next, step S62 can be executed to use the standard sample plant with the smallest difference in growth status between the comparison library and the non-standard sample plant as the standard sample plant. Finally, step S63 can be executed to use the application amount of each type of fertilizer per unit area of ​​the standard sample plant as the application amount of each type of fertilizer per unit area suitable for the non-standard sample plant in the next round of growth stage.

[0025] See also Figures 1 to 3 As shown, the field management unit 3 in this scheme can be a guidance terminal held by a staff member, which is used to carry out targeted fertilization of peanut plants in different positions according to the fertilization plan. Specifically, step S031 can be first executed to obtain the position of each sample plant and the detection point where it is located, as well as the coverage of the planting grid where each detection point is located. Next, step S032 can be executed to receive the application amount of each type of fertilizer per unit area of ​​the qualified sample plants in the next round of growth stage, and the application amount of each type of fertilizer per unit area applicable to each non-qualified sample plant in the next round of growth stage. Finally, step S033 can be executed to fertilize the planting grid coverage of each qualified sample plant and non-qualified sample plant according to the corresponding application amount of each type of fertilizer per unit area.

[0026] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, system, method and computer program product according to multiple embodiments of the present application. In this regard, each square frame in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the square frame can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square frames can actually be executed substantially in parallel, and they can also be executed in reverse order sometimes, depending on the functions involved.

[0027] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.

[0028] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0029] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.< / detectionpoint> < / double> < / detectionpoint> < / detectionpoint> < / detectionpoint> < / detectionpoint> < / detectionpoint> < / detectionpoint> < / detectionpoint> < / detectionpoint> < / detectionpoint> < / detectionpoint> < / detectionpoint> < / algorithm> < / cmath> < / map> < / vector> < / iostream>

Claims

1. An adaptive fertilization management method based on peanut growth detection, characterized in that: include, The peanut planting area is evenly divided into multiple planting grids, and detection points are selected in each planting grid; At the end of each growth stage of the peanut plant, the growth status of multiple parts of the sample plant at the detection point is obtained; Select sample plants whose growth status meets the standard as qualified sample plants, and add the growth status of multiple parts of the qualified sample plants and the application amount of each type of fertilizer per unit area in this round of growth stage to the comparison library; The application amount of each type of fertilizer per unit area of ​​each qualified sample plant in the next round of growth stage is determined for the qualified sample plants; Dividing the peanut planting area into a plurality of isomorphic growth zones according to the position of the sample plant at the detection point and the growth status of multiple parts; The growth status of multiple parts of the non-standard sample plants are screened and compared with the comparison library to obtain the application amount of each type of fertilizer per unit area suitable for each non-standard sample plant in the next growth stage.

2. The method according to claim 1, characterized in that The growth status of the plant parts includes plant height, average stem diameter, difference between stem and root tips, average leaf area, number of leaves, average leaf color depth, average leaf thickness, degree of flower bud differentiation, number of flower buds and / or average diameter of flower buds; Fertilizer types include nitrogen, phosphate, potash, calcium, organic fertilizers, borax, ammonium molybdate, and / or zinc sulfate.

3. The method according to claim 1, characterized in that: The step of dividing the peanut planting area into a plurality of isomorphic growth zones according to the position of the sample plant at the detection point and the growth status of multiple parts includes: According to the difference in the growth state of each part of each sample plant, multiple sample plants with consistent growth states are divided into the same plant combination; In each plant combination, a plurality of sample plants that are adjacent to each other in the plant combination are divided into the same isomorphic plant combination according to the position of each sample plant at the detection point; The range of multiple planting grids where the sample plants included in the same isomorphic plant combination are located is taken as a isomorphic growth partition.

4. The method according to claim 3, characterized in that The step of classifying a plurality of sample plants having consistent growth states into the same plant combination according to the difference in growth state of each part of each sample plant includes: Select several plants from the qualified sample plants and non-qualified sample plants as reference sample plants; Taking the cumulative value of the difference of the growth state parameters of each corresponding part of the two sample plants as the growth state difference degree of the two sample plants, and calculating and obtaining the growth state difference degree between each reference sample plant and other sample plants; For each sample plant other than the reference sample plant, the sample plant having the smallest difference in growth status with the reference sample plant is classified into the same plant combination.

5. The method according to claim 4, characterized in that The step of classifying a plurality of sample plants having consistent growth states into the same plant combination according to the difference in growth state of each part of each sample plant also includes: Determine whether there are sample plants that meet the standards and sample plants that do not meet the standards mixed and classified in the same plant combination after each plant combination has been divided; If not, it is determined that the growth states of the sample plants in the currently divided plant combination are consistent; If so, re-divide to obtain a plant combination with consistent growth status.

6. The method according to claim 5, characterized in that The step of re-dividing to obtain a plant combination with a consistent growth state, include, Reselect benchmark sample plants; A re-divided plant combination is obtained according to the re-selected benchmark sample plant division; Determine whether there are qualified sample plants and non-qualified sample plants mixed and classified in the same plant combination after the re-division.

7. The method according to claim 6, characterized in that The step of reselecting the reference sample plants, include, In each plant combination, the cumulative value of the difference between the growth status of each sample plant and all other sample plants in the plant combination is calculated respectively; The sample plant with the smallest cumulative difference in growth status between each plant combination and all other sample plants in the same plant combination is used as the benchmark sample plant after reselection.

8. The method according to any one of claims 1 to 7, characterized in that: The step of screening and comparing the growth status of multiple parts of the non-standard sample plants with the comparison library to obtain the application amount of each type of fertilizer per unit area suitable for each non-standard sample plant in the next round of growth stage, include, For each non-standard sample plant, calculate and obtain the difference in growth status between the non-standard sample plant and each standard sample plant in the comparison library; The qualified sample plant with the smallest difference in growth status between the sample plant that does not meet the standard and the sample plant that does not meet the standard in the comparison library is used as the target qualified sample plant; The application rate of each type of fertilizer per unit area of ​​the target-compliant sample plants will be used as the application rate of each type of fertilizer per unit area for the non-compliant sample plants in the next growth stage.

9. An adaptive fertilization management method based on peanut growth detection, characterized in that: include, Obtain the location of each sample plant and the detection point where it is located, as well as the coverage of the planting grid where each detection point is located; Receive the application amount of each type of fertilizer per unit area of ​​the qualified sample plants in the next round of growth stage in the adaptive fertilization management method based on peanut growth detection as described in any one of claims 1 to 8, and the application amount of each type of fertilizer per unit area applicable to each non-qualified sample plant in the next round of growth stage; For each planting grid coverage area where the qualified sample plants and non-qualified sample plants are located, fertilizer is applied according to the corresponding application amount of each type of fertilizer per unit area.

10. An adaptive fertilization management system based on peanut growth detection, characterized in that: include, A detection unit, used to detect the growth status of multiple parts of the sample plant; A fertilization management unit is used to evenly divide the peanut planting area into multiple planting grids and select detection points in each planting grid; At the end of each growth stage of the peanut plant, the growth status of multiple parts of the sample plant at the detection point is obtained; Select sample plants whose growth status meets the standard as qualified sample plants, and add the growth status of multiple parts of the qualified sample plants and the application amount of each type of fertilizer per unit area in this round of growth stage to the comparison library; The application amount of each type of fertilizer per unit area of ​​each qualified sample plant in the next round of growth stage is determined for the qualified sample plants; Dividing the peanut planting area into a plurality of isomorphic growth zones according to the position of the sample plant at the detection point and the growth status of multiple parts; Screening and comparing the growth status of multiple parts of the non-standard sample plants with the comparison library to obtain the application amount of each type of fertilizer per unit area suitable for each non-standard sample plant in the next round of growth stage; The field management unit is used to obtain the location of each sample plant and the detection point where it is located, as well as the coverage of the planting grid where each detection point is located; Receive the application amount of each type of fertilizer per unit area for the qualified sample plants in the next round of growth, and the application amount of each type of fertilizer per unit area for each non-qualified sample plant in the next round of growth; For each planting grid coverage area where the qualified sample plants and non-qualified sample plants are located, fertilizer is applied according to the corresponding application amount of each type of fertilizer per unit area.

Images