Nut digital fruit tree planting management system

Abstract

The invention, which relates to the technical field of fruit tree management, discloses a nut digital fruit tree planting management system comprising an environment monitoring module, an irrigation and fertilization module, a fruit health assessment module and an intervention adjustment module. According to the technical scheme, irrigation and fertilization better meet the actual requirements of fruit tree growth by calculating specific numerical values, healthy growth of fruit trees is promoted, and the yield and quality of an orchard are improved; the fruit rate and the pest density value of fruit trees are obtained by using an image recognition technology, a health assessment calculation model is constructed in combination with the height information of the fruit trees, and an accurate health assessment value is generated for each fruit tree; according to the evaluation result, the system can automatically judge whether the health degree of the fruit trees reaches the standard or not, performs fixed-point fertigation and insecticidal treatment on the fruit trees which do not reach the standard, performs fixed-point fertigation after preliminary comprehensive fertigation operation, and has a targeted treatment mode to ensure the effectiveness of fruit tree planting management.

Description

Technical Field

[0001] The invention relates to the technical field of fruit tree management, and in particular to a digital nut fruit tree planting management system. Background Art

[0002] Fruit tree management refers to the scientific and systematic cultivation and maintenance of fruit trees to increase the yield and quality of the fruit while maintaining the health and long-term productivity of the fruit trees; it covers multiple aspects such as fruit tree selection, planting, pruning, fertilization, pest and disease control, etc. In fruit tree management, first of all, it is necessary to select suitable fruit tree varieties based on climatic conditions, soil type and other factors; then, through reasonable planting techniques and density, ensure that the fruit trees can make full use of sunlight, water and nutrients; pruning is an important part of fruit tree management. Through pruning, the shape of the fruit trees can be adjusted and the growth and development of the fruit can be promoted; at the same time, scientific fertilization and pest and disease control are also the key to improving fruit yield and quality; in summary, fruit tree management is a comprehensive process that requires comprehensive consideration of multiple factors and the realization of high-quality, high-yield and sustainable development of fruit trees through scientific management techniques.

[0003] When managing fruit trees, for nut tree planting areas, irrigation and fertilization in traditional fruit tree management are implemented based on experience, which is inaccurate. Even if the initial comprehensive fertilization and irrigation operations can meet the needs of the entire planting area, some nut trees that grow slowly or have insect pests still need targeted fertilization and irrigation treatments. Then, how to accurately and effectively judge whether there are abnormalities in the nut trees or whether their health meets the standards? Manual observation alone is not effective, and it is impossible to further guarantee the effectiveness of fruit tree planting and management. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a digital nut fruit tree planting management system, which solves the problems raised in the background technology.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A digital fruit tree planting management system for nuts, the system comprising:

[0009] The environmental monitoring module regularly monitors environmental data in the target fruit tree planting area, obtains the actual area of ​​the target fruit tree planting area, and samples the average height and chlorophyll content of the fruit trees in the target fruit tree planting area;

[0010] The irrigation and fertilization module builds an estimation calculation model based on environmental data and actual land area to calculate the irrigation estimate, and performs irrigation treatment according to the irrigation estimate; at the same time, it builds a weighted analysis formula based on the mean height and the mean chlorophyll content to generate an estimated fertilizer amount per square meter, and performs fertilization treatment according to the estimated fertilizer amount;

[0011] The fruit health assessment module uses image recognition technology to obtain the health data of each fruit tree when the fruit trees in the target fruit tree planting area are at maturity, retrieves the monitored height of the fruit trees, builds a health assessment calculation model, generates the health assessment value corresponding to each fruit tree, executes the analysis strategy, and outputs the analysis results;

[0012] The intervention and adjustment module receives the analysis results. When it is determined that the health assessment value exceeds the standard threshold, no response action is taken; otherwise, irrigation and fertilization treatment is performed on the corresponding fruit trees whose health assessment values ​​do not exceed the standard threshold.

[0013] Furthermore, the environmental data includes at least soil moisture, soil temperature, light intensity, and rainfall;

[0014] The actual area of ​​the target fruit tree planting area is obtained by pre-measurement.

[0015] Furthermore, the process of sampling and collecting the mean height and mean chlorophyll content of fruit trees in the target fruit tree planting area is: randomly select any four fruit trees in the target fruit tree planting area, and measure the height and chlorophyll content of each selected fruit tree, and then calculate the mean to obtain the mean height and mean chlorophyll content.

[0016] Furthermore, the process of constructing the estimation calculation model is as follows:

[0017] Q g =A*(α*(H0-H1)+β*(|T0-T1|)+γ*Lr+δ*(R0-R1)); (1)

[0018] In formula (1), Q g represents the irrigation estimate, A represents the actual area, α, β, γ, and δ are weight coefficients, and their values ​​range from 0 to 1, H0 represents the ideal soil moisture, H1 represents the soil moisture, T0 represents the ideal soil temperature, T1 represents the soil temperature, Lr represents the light intensity, R0 represents the ideal rainfall, and R1 represents the rainfall;

[0019] If the irrigation estimate is ≤0, no irrigation treatment is performed;

[0020] If the irrigation estimate is greater than 0, irrigation will still be carried out according to the irrigation estimate.

[0021] Furthermore, the weighted analysis formula is as follows:

[0022]

[0023] In formula (2), Q f represents the estimated amount of fertilizer per square meter, B represents the basic amount of fertilizer, P represents the height mean, and P max represents the highest value among the selected fruit tree heights in the target fruit tree planting area, C represents the average chlorophyll content, and C max It represents the highest value of chlorophyll content in the selected fruit trees in the target fruit tree planting area. w1 and w2 are weight coefficients, and their value ranges are both 0 to 1.

[0024] Furthermore, the health data of each fruit tree includes at least: fruiting rate and pest density value;

[0025] The process of using image recognition technology to obtain the health data of each fruit tree is as follows:

[0026] Image processing: Use the inspection robot equipped with a high-definition probe to take pictures of each fruit tree in the target fruit tree planting area, and perform denoising on the captured images;

[0027] Fruit recognition and fruit-bearing rate calculation: Use the trained image recognition model to identify fruits in the denoised images, count the identified fruits, and obtain the total number of fruits on the trees;

[0028] The fruiting rate is the ratio of the number of fruits on a fruit tree to the crown area of ​​the fruit tree, and the crown area is obtained by measurement;

[0029] The calculation formula is: Fruiting rate = total number of fruits / crown area of ​​fruit trees;

[0030] Pest identification and pest density calculation: Use the trained image recognition model to identify pests in the preprocessed images, count the identified pests, and obtain the total number of pests on the fruit trees;

[0031] The pest density value is the ratio of the number of pests on the fruit tree to the crown area of ​​the fruit tree;

[0032] The calculation formula is: Pest density value = total number of pests / crown area of ​​fruit trees;

[0033] If the corresponding fruit trees have no insect pests and no fruits, the insect pest density value and fruiting rate are both 0.

[0034] Furthermore, the formula used to build the health assessment calculation model is as follows:

[0035]

[0036] In formula (3), Hgz iIt represents the health assessment value of the corresponding fruit tree, i represents the number of the fruit tree, and i=1, 2, ..., n, n is a positive integer, which is the total number of fruit trees in the target fruit tree planting area, Go represents the fruiting rate, k represents the adjustment coefficient, and k>0, D represents the pest density value, and hn represents the height of the fruit tree.

[0037] Furthermore, the process of executing the analysis strategy is as follows:

[0038] The health assessment value of each fruit tree is compared with the preset standard threshold, and the comparison results are:

[0039] If the health assessment value exceeds the standard threshold, it means that the health level meets the standard;

[0040] Or the health assessment value does not exceed the standard threshold, indicating that the health level does not meet the standard.

[0041] Furthermore, when the irrigation and fertilization treatment is performed at a fixed point, it also includes triggering a secondary determination mechanism to determine whether there is an insect pest density value;

[0042] If it exists and the pest density value is lower than the preset calibration value, the drug addition action will be executed simultaneously when the irrigation and fertilization treatment is performed; if it exists and the pest density value is not lower than the preset calibration value, the drug addition action and the drone start command will be executed simultaneously when the irrigation and fertilization treatment is performed; if it does not exist, no response action will be taken.

[0043] Furthermore, the content of the drug adding action is: adding pesticides to the irrigation water for insecticide treatment; the drone start-up command is: covering the area above the corresponding fruit trees with the pesticides.

[0044] A digital fruit tree planting management method for nuts, the steps of the method are as follows:

[0045] S1. Regularly monitor environmental data in the target fruit tree planting area, obtain the actual area of ​​the target fruit tree planting area, and sample the average height and chlorophyll content of the fruit trees in the target fruit tree planting area;

[0046] S2. Based on environmental data and actual land area, an estimation calculation model is constructed to calculate the irrigation estimate, and irrigation treatment is carried out according to the irrigation estimate; simultaneously, based on the mean height and the mean chlorophyll content, a weighted analysis formula is constructed to generate an estimated fertilizer amount per square meter, and fertilization treatment is carried out according to the estimated fertilizer amount;

[0047] S3. When the fruit trees in the target fruit tree planting area are in the mature stage, the health data of each fruit tree is obtained by using image recognition technology, and the monitored height of the fruit trees is retrieved, a health assessment calculation model is built, and the health assessment value corresponding to each fruit tree is generated, the analysis strategy is executed, and the analysis results are output;

[0048] S4. Receive the analysis result. When it is determined that the health assessment value exceeds the standard threshold, no response action is taken; otherwise, irrigation and fertilization treatment is performed on the corresponding fruit trees whose health assessment values ​​do not exceed the standard threshold.

[0049] (III) Beneficial effects

[0050] The present invention provides a digital fruit tree planting management system for nuts, which has the following beneficial effects:

[0051] (1) This scheme regularly monitors environmental data such as soil moisture, temperature, light intensity and rainfall, and combines it with the actual area of ​​the orchard to build an irrigation estimation model to ensure that the irrigation amount meets the growth needs of fruit trees without wasting water resources. At the same time, through sampling and testing of fruit tree height and chlorophyll content, the weighted analysis formula is used to calculate the estimated amount of fertilizer per square meter, which achieves precise fertilization and solves the problem of irrigation and fertilization based on experience and inaccuracy in traditional fruit tree management. Through data-driven methods, irrigation and fertilization are more in line with the actual needs of fruit tree growth, promoting the healthy growth of fruit trees and improving the yield and quality of the orchard.

[0052] (2) This solution uses image recognition technology to obtain the fruit tree's fruiting rate and pest density values, and combines the fruit tree height information to construct a health assessment calculation model to generate an accurate health assessment value for each fruit tree. Based on the assessment results, the system can automatically determine whether the fruit tree's health meets the standard, and carry out targeted irrigation, fertilization and insecticide treatment for fruit trees that do not meet the standard. After the initial comprehensive irrigation and fertilization operation, targeted irrigation and fertilization are carried out. The targeted treatment method ensures the effectiveness of fruit tree planting management. In particular, the solution designs differentiated intervention strategies for different pest density situations, such as using inspection robots or drones to add pesticides, to ensure the maximization of insecticide effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of system modularization in the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Embodiment 1:

[0056] See also Figure 1This embodiment provides a digital fruit tree planting management system for nuts. This management system aims to achieve the whole process management of nut fruit tree planting through digital technology, from planting planning, daily monitoring, environmental regulation, pest and disease prevention to yield management analysis (which can be added as needed), with special emphasis on accurate identification and intervention of fruit trees with substandard fruiting rates, so as to improve the overall orchard production efficiency and fruit quality;

[0057] The management system includes several functional modules, namely, environmental monitoring module, irrigation and fertilization module, fruit health assessment module and intervention and adjustment module; the system description of each functional module is as follows:

[0058] Environmental monitoring module:

[0059] Regularly monitor environmental data in the target fruit tree planting area, obtain the actual area of ​​the target fruit tree planting area, and sample the average height and chlorophyll content of the fruit trees in the target fruit tree planting area;

[0060] The target fruit tree planting area refers to a park selected in advance and equipped with the management system;

[0061] The term "regular" in regular monitoring refers to data collection within a preset fixed period, but for other data supervision, real-time operations can be performed, which is not detailed here;

[0062] Environmental data include at least soil moisture, soil temperature, light intensity and rainfall;

[0063] Soil moisture: Use a soil moisture meter (such as resistive or capacitive) to directly insert it into the soil for measurement, and real-time soil moisture data can be obtained; or through satellite or drone remote sensing technology, soil moisture information of large planting areas can be obtained. In this embodiment, satellite remote sensing technology can be used for acquisition; Soil temperature: Data is collected through the soil temperature sensor set in the local weather station, and soil temperature data can be automatically recorded and transmitted; Light intensity: Through satellite or drone remote sensing technology, light intensity information of large planting areas can be obtained. This method needs to be calibrated in combination with ground measured data, which will not be elaborated here; Rainfall: Through meteorological radar detection, rainfall conditions in a large range can be obtained, but this method is affected by factors such as terrain and clouds, and needs to be verified in combination with ground measured data, which will not be elaborated here;

[0064] The actual area of ​​the target fruit tree planting area was measured and obtained in advance;

[0065] The process of sampling and collecting the mean height and chlorophyll content of fruit trees in the target fruit tree planting area is as follows:

[0066] Randomly select any four fruit trees in the target fruit tree planting area, measure the height and chlorophyll content of each selected fruit tree, and then calculate the mean value to obtain the mean height and chlorophyll content;

[0067] Laser sensors and chlorophyll meters can be used to measure the required height and chlorophyll content. The Internet of Things technology can be used to transmit the measured data to a remote management system for subsequent analysis and processing.

[0068] Fertigation module:

[0069] Based on environmental data and actual land area, an estimation calculation model is constructed to calculate the irrigation estimation, and the target fruit tree planting area is irrigated in an overall and preliminary manner according to the irrigation estimation; simultaneously, a weighted analysis formula is constructed based on the mean height and the mean chlorophyll content to generate an estimated fertilizer amount per square meter, and the target fruit tree planting area is fertilized in an overall and preliminary manner according to the estimated fertilizer amount;

[0070] It should be noted that in this embodiment, before calculating according to the formula, each data needs to be dimensionless to ensure that each data is in the same dimension, which facilitates the smooth operation and calculation of the formula or model;

[0071] The process of building an estimation calculation model is as follows:

[0072] Q g =A*(α*(H0-H1)+β*(|T0-T1|)+γ*(Lr+δ*(R0-R1)); (1)

[0073] In formula (1), Q g represents the irrigation estimate, represents the irrigation amount of the entire target fruit tree planting area, A represents the actual area, α, β, γ, δ are weight coefficients, and the value range is 0-1, H0 represents the ideal value of soil moisture, H1 represents soil moisture, T0 represents the ideal value of soil temperature, T1 represents soil temperature, Lr represents light intensity, R0 represents the ideal value of rainfall, and R1 represents rainfall;

[0074] Formula (1) estimates the amount of irrigation required for an orchard by considering four environmental factors, namely soil moisture, temperature, light intensity and rainfall, as well as the area of ​​the orchard. Each environmental factor has a corresponding influence coefficient, which is used to adjust its contribution to the irrigation amount. For example, if the current soil moisture is lower than the ideal moisture, the formula will increase the estimated irrigation amount accordingly. Similarly, if the recent rainfall is less than the ideal rainfall, the formula will also increase the estimated irrigation amount to compensate for the lack of water. For each ideal value, it is based on the ideal range for fruit tree growth. For example, the ideal range of soil moisture suitable for fruit tree growth is X℃ to X+2℃, and the ideal soil moisture value is the median of this range, that is, X+1℃.

[0075] If the irrigation estimate is ≤0, no irrigation treatment is performed;

[0076] If the irrigation estimate is greater than 0, irrigation will still be carried out according to the irrigation estimate;

[0077] The weighted analysis formula constructed is as follows:

[0078]

[0079] In formula (2), Q f represents the estimated amount of fertilizer per square meter, B represents the basic amount of fertilizer, which is a fixed value preset in advance, and P represents the height mean. max represents the highest value among the selected fruit tree heights in the target fruit tree planting area, C represents the average chlorophyll content, and C max It represents the highest value of chlorophyll content of the selected fruit trees in the target fruit tree planting area. w1 and w2 are weight coefficients, and their value ranges are 0 to 1;

[0080] Formula (2) estimates the amount of fertilizer per square meter by considering the average height and chlorophyll content of the sampled trees, as well as their respective maximum values ​​and weight coefficients. Both tree height and chlorophyll content are important indicators reflecting the growth status of trees, so they are used as the basis for estimating the amount of fertilizer. The basic fertilizer amount B in the formula provides a baseline level of fertilizer, while the weight coefficients w1 and w2 are used to adjust the relative importance of tree height and chlorophyll content to the estimation of fertilizer. For example, if the average height of a tree is lower than its corresponding maximum value, the formula will increase the estimated amount of fertilizer accordingly to promote tree growth. Similarly, if the chlorophyll content is lower than its corresponding maximum value, the formula will also increase the estimated amount of fertilizer to improve the photosynthesis efficiency of the tree.

[0081] The weight coefficient is determined by the coefficient of variation method, which is a method of weighting each indicator according to the degree of variation between the current value of each evaluation indicator and the target value. If the numerical difference of an indicator is large, it can clearly distinguish the evaluated objects, indicating that the indicator has rich discrimination information, and thus the indicator should be given a larger weight. On the contrary, if the numerical difference of each evaluated object on a certain indicator is small, then the ability of this indicator to distinguish the evaluation objects is weak, and thus the indicator should be given a smaller weight. This method directly uses the information contained in each indicator to obtain the weight of the indicator through calculation, so it is objective.

[0082] Through the above technical solutions, comprehensive consideration of environmental factors and fruit tree growth conditions is achieved, and the amount of irrigation and fertilization is accurately estimated, thereby improving the scientificity and efficiency of fruit tree management;

[0083] The solution first builds an irrigation estimation model by regularly monitoring environmental data such as soil moisture, temperature, light intensity and rainfall, combined with the actual area of ​​the orchard, to ensure that the irrigation volume meets the growth needs of fruit trees without wasting water resources; at the same time, by sampling and testing the height and chlorophyll content of fruit trees, the weighted analysis formula is used to calculate the estimated amount of fertilizer per square meter, thus achieving precise fertilization; this technical solution solves the problem of irrigation and fertilization based on experience and inaccuracy in traditional fruit tree management. Through a data-driven approach, irrigation and fertilization are more in line with the actual needs of fruit tree growth, promoting the healthy growth of fruit trees and improving the yield and quality of the orchard;

[0084] In addition, the use of Internet of Things technology to achieve real-time transmission and processing of data facilitates remote management, reduces labor costs, and improves the level of intelligence in orchard management. Overall, this technical solution effectively integrates modern information technology and agricultural science, providing strong technical support for the precise management of fruit trees.

[0085] Fruit Health Assessment Module:

[0086] When the fruit trees in the target fruit tree planting area are in the mature stage, the image recognition technology is used to obtain the health data of each fruit tree, and the monitored height of the fruit trees is retrieved. A health assessment calculation model is built to generate the corresponding health assessment value of each fruit tree, execute the analysis strategy, and output the analysis results;

[0087] Among them, when the fruit trees in the target fruit tree planting area are in the mature stage, it is ensured that each fruit tree has a certain amount of fruit when the module is running, which is convenient for subsequent calculations;

[0088] The health data of each fruit tree includes at least: fruiting rate and pest density value;

[0089] The process of using image recognition technology to obtain the health data of each fruit tree is as follows:

[0090] Image processing: Using inspection robots equipped with high-definition probes, we take multi-angle photos of the fruit trees in the target fruit tree planting area to ensure that the fruits and possible pests of the fruit trees can be clearly displayed in the images; we perform pre-processing operations such as denoising, contrast enhancement, and brightness adjustment on the captured images to improve the accuracy of subsequent image recognition;

[0091] Fruit recognition and fruit-bearing rate calculation: Use a trained image recognition model (such as a convolutional neural network model based on deep learning) to identify fruits in preprocessed images; the image recognition model should be able to accurately distinguish fruits from other parts such as leaves and branches; count the identified fruits to obtain the total number of fruits on the fruit tree; the fruit-bearing rate is defined as the ratio of the number of fruits on the fruit tree to the crown area of ​​the fruit tree, and the crown area is obtained by measurement;

[0092] The calculation formula is: Fruiting rate = total number of fruits / crown area of ​​fruit trees;

[0093] Pest identification and pest density value calculation: The trained image recognition model is also used to identify pests on the preprocessed images. The image recognition model can identify common fruit tree pests and count the identified pests to obtain the total number of pests on the fruit trees. The pest density value is defined as the ratio of the number of pests on the fruit trees to the crown area of ​​the fruit trees.

[0094] The calculation formula is: Pest density value = total number of pests / crown area of ​​fruit trees;

[0095] If the corresponding fruit tree has no insect pests or no fruit, the insect pest density value and fruiting rate are 0;

[0096] The formula used to build the health assessment calculation model is as follows:

[0097]

[0098] In formula (3), Hgz i represents the health assessment value of the corresponding fruit tree, i represents the number of the fruit tree, and i=1, 2, ..., n, n is a positive integer, which is the total number of fruit trees in the target fruit tree planting area, usually a small fruit tree area with dozens of trees, which is easy to manage; Go represents the fruiting rate, k represents the adjustment coefficient, which is used to balance the relative importance of the fruiting rate and the pest density value in the health assessment, and k>0, D represents the pest density value, and hn represents the height of the fruit tree;

[0099] Logical description

[0100] Effect of fruiting rate: Fruiting rate directly reflects the yield potential of fruit trees and is an important positive factor in health assessment. In the formula, Go appears in the numerator, which means that the higher the fruiting rate, the greater the health assessment value. Effect of pest density value: The pest density value D reflects the degree of pest damage to fruit trees and is a negative factor in health assessment. In the formula, D appears in the denominator and is multiplied by the adjustment coefficient k, which means that the greater the pest density value, the smaller the health assessment value. The adjustment coefficient k is introduced to adjust the degree of influence of pest density value on health assessment value, ensuring that it can reasonably reflect the health status of fruit trees under different fruit trees or different pest conditions. Effect of fruit tree height: The height of fruit trees hn reflects the growth status and tree structure of fruit trees to a certain extent, but too high fruit trees may mean management difficulties or unbalanced nutrient distribution. In the formula, the square root of hn appears in the denominator, which means that the higher the height of the fruit tree, the smaller the health assessment value, but the degree of influence gradually weakens (because the square root is used).

[0101] Comprehensive evaluation: By combining the three factors of fruit bearing rate, pest density value and tree height in the form of product, the formula can comprehensively reflect the health status of fruit trees; when the fruit tree fruit bearing rate is high, the pest density value is low and the height is moderate, the health evaluation value will be high, indicating that the fruit tree is in good health; when the fruit tree fruit bearing rate is low, the pest density value is high or the height is too high, the health evaluation value will be low, indicating that the fruit tree is in poor health;

[0102] The process of executing the analysis strategy is as follows:

[0103] The health assessment value of each fruit tree is compared with the preset standard threshold, and the comparison result (i.e., analysis result) is: the health assessment value exceeds the standard threshold, indicating that the health level meets the standard, or the health assessment value does not exceed the standard threshold, indicating that the health level does not meet the standard.

[0104] Intervention Adjustment Module:

[0105] Receive the analysis results. If it is determined that the health assessment value exceeds the standard threshold, no response action will be taken. Otherwise, the corresponding fruit trees whose health assessment value does not exceed the standard threshold will be irrigated and fertilized at a fixed point, and the secondary determination mechanism will be triggered to determine whether there is an insect pest density value.

[0106] If there is, and the pest density value is lower than the preset calibration value, then when the irrigation and fertilization treatment is performed, the drug addition action is performed simultaneously, and the content of the drug addition action is: adding insecticide to the irrigation water for insecticide treatment;

[0107] If there is, and the pest density value is not lower than the preset calibration value, then when performing irrigation and fertilization treatment, the drug addition action and the drone start command are executed simultaneously. The drone start command is: cover the area above the corresponding fruit tree with insecticide;

[0108] If it does not exist, no response action will be taken;

[0109] Among them, the existence of pest density value indicates that the corresponding fruit tree has pests, and pest control is required;

[0110] The action of adding pesticides is usually performed by a patrol robot equipped with a sprayer, so the sprayer performs insecticide treatment on the fruit trees from below. This is more suitable for situations with low pest density, that is, when the pest density is lower than the preset calibration value. For situations with high pest density, the fruit trees need to be fully treated with insecticides, so drones need to be called in for operation.

[0111] Through the above technical solutions, accurate assessment and timely intervention of the health status of fruit trees are achieved, which effectively improves the management level of orchards and the yield quality of fruit trees;

[0112] The solution uses image recognition technology to obtain the fruit-bearing rate and pest density of fruit trees, and combines the height information of fruit trees to build a health assessment calculation model, generating accurate health assessment values ​​for each fruit tree. Based on the assessment results, the system can automatically determine whether the health of the fruit trees meets the standards, and carry out targeted irrigation, fertilization and pest control for fruit trees that do not meet the standards. In particular, the solution designs differentiated intervention strategies for different pest densities, such as using patrol robots or drones to add pesticides, to ensure the maximum pest control effect.

[0113] This technical solution solves the problems of inaccurate assessment of fruit tree health status and untimely intervention measures in traditional orchard management. Through data-driven and intelligent decision-making, it realizes refined and efficient orchard management. At the same time, the implementation of this solution will also help reduce the excessive use of pesticides and fertilizers, reduce production costs, and promote the sustainable development of orchards.

[0114] Embodiment 2:

[0115] Based on Example 1, this embodiment also provides a method for digitalizing nut fruit tree planting management, and the steps of the method are as follows:

[0116] S1. Regularly monitor environmental data in the target fruit tree planting area, obtain the actual area of ​​the target fruit tree planting area, and sample the average height and chlorophyll content of the fruit trees in the target fruit tree planting area;

[0117] S2. Based on environmental data and actual land area, an estimation calculation model is constructed to calculate the irrigation estimate, and irrigation treatment is carried out according to the irrigation estimate; simultaneously, based on the mean height and the mean chlorophyll content, a weighted analysis formula is constructed to generate an estimated fertilizer amount per square meter, and fertilization treatment is carried out according to the estimated fertilizer amount;

[0118] S3. When the fruit trees in the target fruit tree planting area are in the mature stage, the health data of each fruit tree is obtained by using image recognition technology, and the monitored height of the fruit trees is retrieved, a health assessment calculation model is built, and the health assessment value corresponding to each fruit tree is generated, the analysis strategy is executed, and the analysis results are output;

[0119] S4. Receive the analysis result. When it is determined that the health assessment value exceeds the standard threshold, no response action is taken; otherwise, irrigation and fertilization treatment is performed on the corresponding fruit trees whose health assessment values ​​do not exceed the standard threshold.

[0120] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0121] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A digital fruit tree planting management system for nuts, characterized in that: The system includes: The environmental monitoring module regularly monitors environmental data in the target fruit tree planting area, obtains the actual area of ​​the target fruit tree planting area, and samples the average height and chlorophyll content of the fruit trees in the target fruit tree planting area; The irrigation and fertilization module builds an estimation calculation model based on environmental data and actual land area to calculate the irrigation estimate, and performs irrigation treatment according to the irrigation estimate; at the same time, it builds a weighted analysis formula based on the mean height and the mean chlorophyll content to generate an estimated fertilizer amount per square meter, and performs fertilization treatment according to the estimated fertilizer amount; The fruit health assessment module uses image recognition technology to obtain the health data of each fruit tree when the fruit trees in the target fruit tree planting area are at maturity, retrieves the monitored height of the fruit trees, builds a health assessment calculation model, generates the health assessment value corresponding to each fruit tree, executes the analysis strategy, and outputs the analysis results; The intervention and adjustment module receives the analysis results. When it is determined that the health assessment value exceeds the standard threshold, no response action is taken; otherwise, irrigation and fertilization treatment is performed on the corresponding fruit trees whose health assessment values ​​do not exceed the standard threshold.

2. A digital fruit tree planting management system for nuts according to claim 1, characterized in that: Environmental data include at least soil moisture, soil temperature, light intensity and rainfall; The actual area of ​​the target fruit tree planting area is obtained by pre-measurement.

3. A digital fruit tree planting management system for nuts according to claim 1, characterized in that: The process of sampling and collecting the mean height and mean chlorophyll content of fruit trees in the target fruit tree planting area is as follows: randomly select any four fruit trees in the target fruit tree planting area, and measure the height and chlorophyll content of each selected fruit tree, and then calculate the mean to obtain the mean height and mean chlorophyll content.

4. A digital fruit tree planting management system for nuts according to claim 2, characterized in that: The process of building an estimation calculation model is as follows: Q g =A*(α*(H0-H1)+β*(|T0-T1|)+γ*Lr+δ*(R0-R1)); (1) In formula (1), Q g represents the irrigation estimate, A represents the actual area, α, β, γ, and δ are weight coefficients, and their values ​​range from 0 to 1, H0 represents the ideal soil moisture, H1 represents the soil moisture, T0 represents the ideal soil temperature, T1 represents the soil temperature, Lr represents the light intensity, R0 represents the ideal rainfall, and R1 represents the rainfall; If the irrigation estimate is ≤0, no irrigation treatment is performed; If the irrigation estimate is greater than 0, irrigation will still be carried out according to the irrigation estimate.

5. The digital fruit tree planting management system for nuts according to claim 1 is characterized by: The weighted analysis formula constructed is as follows: In formula (2), Q f represents the estimated amount of fertilizer per square meter, B represents the basic amount of fertilizer, P represents the height mean, and P max represents the highest value among the selected fruit tree heights in the target fruit tree planting area, C represents the average chlorophyll content, and C max It represents the highest value of chlorophyll content in the selected fruit trees in the target fruit tree planting area. w1 and w2 are weight coefficients, and their value ranges are both 0 to 1.

6. A digital fruit tree planting management system for nuts according to claim 1, characterized in that: The health data of each fruit tree includes at least: fruiting rate and pest density value; The process of using image recognition technology to obtain the health data of each fruit tree is as follows: Image processing: Use the inspection robot equipped with a high-definition probe to take pictures of each fruit tree in the target fruit tree planting area, and perform denoising on the captured images; Fruit recognition and fruit-bearing rate calculation: Use the trained image recognition model to identify fruits in the denoised images, count the identified fruits, and obtain the total number of fruits on the trees; The fruiting rate is the ratio of the number of fruits on a fruit tree to the crown area of ​​the fruit tree, and the crown area is obtained by measurement; The calculation formula is: Fruiting rate = total number of fruits / crown area of ​​fruit trees; Pest identification and pest density calculation: Use the trained image recognition model to identify pests in the preprocessed images, count the identified pests, and obtain the total number of pests on the fruit trees; The pest density value is the ratio of the number of pests on the fruit tree to the crown area of ​​the fruit tree; The calculation formula is: Pest density value = total number of pests / crown area of ​​fruit trees; If the corresponding fruit trees have no insect pests and no fruits, the insect pest density value and fruiting rate are both 0.

7. A digital fruit tree planting management system for nuts according to claim 6, characterized in that: The formula used to build the health assessment calculation model is as follows: In formula (3), Hgz i It represents the health assessment value of the corresponding fruit tree, i represents the number of the fruit tree, and i=1, 2, ..., n, n is a positive integer, which is the total number of fruit trees in the target fruit tree planting area, Go represents the fruiting rate, k represents the adjustment coefficient, and k>0, D represents the pest density value, and hn represents the height of the fruit tree.

8. A digital nut tree planting management system according to claim 7, characterized in that: The process of executing the analysis strategy is as follows: The health assessment value of each fruit tree is compared with the preset standard threshold, and the comparison results are: If the health assessment value exceeds the standard threshold, it means that the health level meets the standard; Or the health assessment value does not exceed the standard threshold, indicating that the health level does not meet the standard.

9. A digital nut tree planting management system according to claim 1, characterized in that: When the irrigation and fertilization treatment is performed at a fixed point, it also includes triggering a secondary judgment mechanism to determine whether there is a pest density value; If it exists and the pest density value is lower than the preset calibration value, the drug addition action will be executed simultaneously when the irrigation and fertilization treatment is performed; if it exists and the pest density value is not lower than the preset calibration value, the drug addition action and the drone start command will be executed simultaneously when the irrigation and fertilization treatment is performed; if it does not exist, no response action will be taken.

10. A digital nut fruit tree planting management system according to claim 9, characterized in that: The content of the dosing action is: adding pesticides to the irrigation water for insecticide treatment; the drone startup command is: covering the area above the corresponding fruit trees with the pesticides.

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