A thin-shell pecan disease and pest monitoring system based on unmanned aerial vehicle inspection

Abstract

The present application provides a kind of based on unmanned aerial vehicle inspection Pecan disease and pest monitoring system, compared with prior art, the present application also includes the unmanned aerial vehicle of preset route in Pecan planting area is carried out inspection operation, the camera device of being set on unmanned aerial vehicle to the Pecan fruit tree of unmanned aerial vehicle is inspected is carried out image shooting, receive image and analyze image to determine the pathological condition of Pecan fruit tree judging module, the treatment agent infiltration module of bottom insertion to soil to the treatment agent infiltration below ground for the root of Pecan fruit tree is absorbed, the spraying module of Pecan fruit tree is sprayed, and the conveying module of the infiltration unit and spraying unit is carried out treatment agent directional delivery.The present application system can accurately determine the type of Pecan fruit tree disease and pest, so as to take corresponding prevention and control measures in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fruit tree disease and pest control systems, and more particularly to a thin-shelled pecan disease and pest monitoring system based on unmanned aerial vehicle inspection. BACKGROUND

[0002] Thin-shelled pecan, also known as American pecan and long pecan, is a deciduous tree of the Juglandaceae family. In recent years, the planting area of thin-shelled pecan has gradually increased in China. Pecan nurseries in Anhui, Jiangsu and Zhejiang have entered the fruiting period. Depending on the planting time, the yield of thin-shelled pecan per mu is between 200 and 400 kg. At the same time, the thin-shelled pecan is also more and more serious, and the yellow aphid, root nodule aphid, peach borer, borer and black spot disease seriously harm the leaves, branches and fruits of the thin-shelled pecan. The thin-shelled pecan fruit falls off, the leaves are severely damaged, the fruit yield and quality are affected.

[0003] The experimental team has long been engaged in the relevant technology and has carried out a large number of relevant record data browsing and research, and relies on relevant resources and carries out a large number of relevant experiments. Through a large number of searches, it is found that the existing technologies such as CN114145276B, CN113678811B, CN111296456B and KR100870386B1 exist. A fruit tree disease and pest control device based on Internet of Things technology is disclosed. It includes a plurality of prevention units, a replacement unit, a cloud server, a control terminal, and a cloud server and a control terminal data interaction. The prevention unit includes a liquid storage assembly for trapping pests, a liquid level sensor, a single-chip microcomputer, a first wireless communication module, and a first power module. The single-chip microcomputer controls the first wireless communication module to communicate with the cloud server. The replacement unit includes a mechanical replacement assembly for replacing the liquid storage assembly, a central processing module, a second wireless communication module, and a second power module. The central processing module controls the second wireless communication module to communicate with the cloud server, and the central processing module controls the mechanical replacement assembly to work.

[0004] In order to solve the problems of low efficiency of fruit tree disease and pest control, large demand for manual labor for fruit tree disease and pest control, etc. in the field, the present application is made. SUMMARY

[0005] The present application aims to solve the problems existing in the field at present, and provides a thin-shelled pecan disease and pest monitoring system based on unmanned aerial vehicle inspection.

[0006] In order to overcome the shortcomings of the prior art, the present application adopts the following technical solutions:

[0007] The application discloses a thin-shell pecan disease and pest monitoring system based on unmanned aerial vehicle inspection, which comprises an unmanned aerial vehicle for performing inspection operation in a thin-shell pecan planting area in a preset way, a camera device arranged on the unmanned aerial vehicle and used for taking images of thin-shell pecan fruit trees inspected by the unmanned aerial vehicle, a judgment module used for receiving the images and analyzing the images to judge the disease conditions of the thin-shell pecan fruit trees, a treatment agent infiltration module used for inserting a bottom into soil to infiltrate treatment agents into the ground for the roots of the thin-shell pecan fruit trees to absorb the treatment agents, a spraying module used for spraying the thin-shell pecan fruit trees, and a conveying module used for conveying the treatment agents to the infiltration unit and the spraying unit.

[0008] The judgment module comprises a preprocessing unit used for preprocessing the images of the thin-shell pecan fruit trees taken by the camera device to obtain preprocessed images, an analysis unit used for extracting features related to diseases and pests from the preprocessed images, identifying and classifying the extracted features, judging the types of the diseases and pests of the thin-shell pecan fruit trees in the image acquisition area according to the identification and classification results and preset disease and pest judgment standards, and a comparison database used for pre-storing image features of different disease types of the thin-shell pecan fruit trees, wherein the preprocessing unit is used for sequentially performing image denoising, image enhancement, color space conversion and zooming processing on the images of the thin-shell pecan fruit trees to obtain preprocessed images with preset specifications.

[0009] Further, the comparison database comprises image features of different disease types of the thin-shell pecan fruit trees, disease pattern features of different disease types of the thin-shell pecan fruit trees, treatment agents corresponding to different diseases, and concentrations and dosages of the treatment agents corresponding to different grades of different diseases.

[0010] Further, the treatment agent infiltration module comprises a plurality of infiltration units embedded in the land of the thin-shell pecan orchard, wherein each infiltration unit comprises a preset-depth irrigation groove dug in the land of the thin-shell pecan orchard, an irrigation pipeline vertically inserted and fixed into the irrigation groove, a liquid storage tank in communication with the top of the irrigation pipeline, an inlet pipeline in communication with the side tank wall of the liquid storage tank, a first electric control valve arranged in the inlet pipeline to control the communication between the inlet pipeline and the liquid storage tank, a pressure regulating pump in communication with the liquid storage tank and used for providing negative pressure to the liquid storage tank, liquid outlet holes uniformly arranged on the side pipe wall of the irrigation pipeline, and a liquid level sensor used for monitoring the liquid level of the treatment agent in the liquid storage tank, and the liquid storage tank is supported at a preset height of the land of the thin-shell pecan orchard by a support seat.

[0011] Further, the spraying module comprises a plurality of spraying units arranged in sequence on the infiltration unit, a buffer liquid dish fixed above each liquid storage tank, a support rod supporting and fixing the buffer liquid dish above the liquid storage tank at a predetermined distance, a placing box arranged adjacent to one side of the buffer liquid dish, a first branch pipe in communication with the bottom of the buffer liquid dish, a second branch pipe in communication with the top of the buffer liquid dish, a spraying pipe of a glue-like structure with one end in communication with the first branch pipe, a fixed pipe with one end in communication with the other end of the spraying pipe, an atomizing nozzle in communication with the other end of the fixed pipe, a ring plate horizontally sleeved to the outer pipe wall of the fixed pipe, two mounting seats symmetrically fixed to the dish top wall of the buffer liquid dish, a lifting driving mechanism for driving the mounting seat to lift relative to the buffer liquid dish, and a driving liquid pump for driving the liquid in the buffer liquid to flow out from the first branch pipe, the spraying pipe and the atomizing nozzle in sequence.

[0012] Further, the conveying module comprises a liquid tank for storing treatment medicament, a plurality of first conveying pipes for communicating the liquid tank with the liquid inlet pipeline of each respective infiltration unit, a plurality of first electric control valves for controlling the communication condition of the first conveying pipes with the liquid tank, a first transmission liquid pump for controlling the quantitative conveying of the treatment medicament in the liquid tank from the first conveying pipes to each buffer liquid dish, a plurality of second conveying pipes for communicating the liquid tank with each respective buffer liquid dish, a plurality of second electric control valves for controlling the communication condition of the second conveying pipes with the liquid tank, and a second transmission liquid pump for controlling the quantitative conveying of the treatment medicament in the liquid tank from the second conveying pipes to each buffer liquid dish.

[0013] The present application has the following advantages:

[0014] 1. The unmanned aerial vehicle can realize efficient inspection of the thin-shelled pecan planting area, and the unmanned aerial vehicle can transmit the photographed thin-shelled pecan tree image to the judgment module for analysis and identification through real-time image transmission, so as to realize real-time acquisition of the disease condition of the fruit tree, and to realize comprehensive monitoring of all fruit trees in the thin-shelled pecan planting area, timely detection of disease and pest conditions, and avoidance of missed detection and omission, thereby greatly saving manpower and time cost.

[0015] 2. The judgment module analyzes the thin-shelled pecan tree image photographed by the unmanned aerial vehicle through image processing, feature extraction, classification and identification, and finally judges the disease and pest condition of the fruit tree, thereby providing accurate information and decision basis for subsequent thin-shelled pecan disease and pest control, and through preprocessing, feature extraction and identification classification of the image photographed by the camera device, the system can accurately judge the disease and pest type of the thin-shelled pecan tree, and help farmers to take corresponding prevention measures in time.

[0016] 3. The present application realizes treatment and prevention of diseases and pests by automatically infiltrating the treatment agent into the ground for fruit trees to absorb, or spraying, thereby reducing the burden of manual operation, reducing the risk of direct contact of personnel with pesticides, reducing harm to the human body and pollution to the environment. BRIEF DESCRIPTION OF DRAWINGS

[0017] The present application can be further understood from the following description in conjunction with the drawings. The components in the drawings are not necessarily drawn to scale, but emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0018] Figure 1 A modular schematic diagram of the present application is provided.

[0019] Figure 2 A modular schematic diagram of the present application is provided.

[0020] Figure 3 A partial structure schematic diagram of the present application is provided.

[0021] Figure 4 A partial structure schematic diagram of the present application is provided.

[0022] BRIEF DESCRIPTION OF DRAWINGS: 1-atomizing nozzle; 2-spraying pipe; 3-second branch pipe; 4-buffer liquid dish; 5-supporting rod; 6-liquid inlet pipeline; 7-liquid storage tank; 8-first branch pipe; 9-placing box; 10-telescopic driving rod; 11-ring plate; 12-fixing pipe; 13-supporting seat; 14-irrigation pipeline; 15-irrigation groove; 16-liquid outlet hole. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application will be further described in detail below in combination with its embodiments; it should be pointed out that the specific embodiments described here are only used to explain the present application, and are not used to limit the case. For those skilled in the art, other systems, methods and / or features of the present embodiment will become apparent after reading the following detailed description. And the terms used to describe the positional relationship in the drawings are only used for illustrative description, and cannot be understood as a limitation of the present patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0024] Embodiment one: in combination with the drawings Figure 1 , the drawings Figure 2 , the drawings Figure 3 and the drawings Figure 4The embodiment constructs a thin-shell pecan disease and pest monitoring system based on unmanned aerial vehicle inspection. The thin-shell pecan disease and pest monitoring system comprises an unmanned aerial vehicle for performing inspection work in a predetermined way in a thin-shell pecan planting area, a camera device arranged on the unmanned aerial vehicle for image shooting of the thin-shell pecan fruit trees inspected by the unmanned aerial vehicle, a judgment module for receiving the images and analyzing the images to determine the disease conditions of the thin-shell pecan fruit trees, a treatment agent infiltration module for inserting the bottom into the soil to infiltrate the treatment agent into the ground for the root of the thin-shell pecan fruit tree to absorb the treatment agent, a spraying module for spraying the thin-shell pecan fruit trees, and a conveying module for conveying the treatment agent to the infiltration unit and the spraying unit. The unmanned aerial vehicle is provided with a positioning sensor for obtaining the position thereof. The camera device matches and records the images shot with the position of the unmanned aerial vehicle,

[0025] The judgment module comprises a preprocessing unit for preprocessing the thin-shell pecan fruit tree images shot by the camera device to obtain preprocessed images, an analysis unit for extracting features related to diseases and pests from the preprocessed images, identifying and classifying the extracted features, and further determining the types of diseases and pests of the thin-shell pecan fruit trees in the image acquisition area according to the identification and classification results and the preset disease and pest determination standard, and a comparison database of image features of different types of diseases of the thin-shell pecan fruit trees, which is obtained by a large number of repeated experiments on different thin-shell pecan disease images based on historical experience of a person skilled in the art. Again, no further description is given,

[0026] The preprocessing unit is used for sequentially performing image denoising, image enhancement, color space conversion and scaling processing on the thin-shell pecan fruit tree images to obtain preprocessed images of a preset specification size, so as to improve the accuracy of the subsequent feature extraction unit and the identification unit,

[0027] The person skilled in the art obtains a large number of training samples of different disease types of thin-shell pecan fruit trees, corresponding leaf disease area patterns of different disease types, and fruit disease area patterns of different disease types based on historical experience in advance. The comparison database comprises disease pattern features of the leaf disease area patterns corresponding to different disease types, disease pattern features of the fruit disease area patterns of different disease types, treatment agents corresponding to different diseases, and concentrations and dosages of treatment agents corresponding to different grades of diseases in the fruit trees, which are obtained by analyzing and processing the training samples based on existing image processing techniques. The disease pattern features of the leaf disease area patterns comprise a gray value range of the disease area in the fruit tree leaves, a shape of the disease area, and an area size ratio of the disease area to the non-disease area. The disease pattern features of the fruit disease area patterns comprise a gray value range of the disease area in the fruit tree fruits, a shape of the disease area, and an area size ratio of the disease area to the non-disease area,

[0028] The first area ratio is taken as the area ratio of the diseased area of ​​the fruit tree leaves to the non-lesioned area, and the second area ratio is taken as the area ratio of the diseased area of ​​the fruit tree fruit to the non-lesioned area. Different types of lesions in the database correspond to different graphic features of the lesion areas of the fruit tree leaves and the lesion areas of the fruit tree fruit, the first area ratio, and the second area ratio. The types of lesions at least include yellow aphid damage, root-knot aphid damage, peach borer damage, longhorn beetle damage, and black spot disease, and different types of lesions are represented by category a, category b, category c, category d, and category e, respectively.

[0029] The present invention can realize efficient inspection operations of thin-shell pecan planting areas through drone inspection. The drone transmits the photographed thin-shell pecan fruit tree images to the judgment module for analysis and identification through real-time image transmission, and can obtain the disease conditions of the fruit trees in real time, and then comprehensively monitor all fruit trees in the thin-shell pecan planting area, timely discover diseases and pests, avoid missed inspections and omissions, and greatly save manpower and time costs.

[0030] Example 2: Combined with the attached Figure 1 , Attachment Figure 2 , Attachment Figure 3 and attached Figure 4 In addition to the contents of the above embodiments, the analysis unit is implemented by the following steps:

[0031] S101: Using existing morphological processing techniques such as edge detection, erosion, and dilation, morphological features are extracted from the image. Further, based on pre-stored fruit tree leaf and fruit fruit graphics, the fruit tree leaf and fruit fruit graphics in the pre-processed image are extracted as the processed image. Meanwhile, the fruit tree leaf area and fruit tree fruit area in the processed image are divided by marking. The fruit tree leaf area Sr1 and the fruit tree fruit area Sr2 in the processed image are respectively calculated by pixel counting.

[0032] S102: By comparing the grayscale value range of the fruit tree leaf lesion area and the grayscale value range of the fruit tree fruit lesion area in the database, the lesion areas in the fruit tree leaf and fruit tree fruit images in the processed image are divided and the lesion types of the corresponding lesion areas are preliminarily predicted.

[0033] S103: further comparing the shapes of the lesion areas on the leaves of fruit trees and the shapes of the lesion areas on the fruits of fruit trees in the database to determine and / or exclude the type of lesion predicted in S102, and after determining the type of lesion predicted in S102, further obtaining a therapeutic agent that matches the corresponding type of lesion.

[0034] S104: For different lesion categories, the first contrast value related to the fruit tree leaf lesion of the same category and the second contrast value related to the fruit tree fruit lesion of the same category are calculated respectively,

[0035] S105: The difference between the first area size ratio and the first contrast value of the same lesion category is obtained as the first difference value, and the difference between the second area size ratio and the second contrast value of the same lesion category is obtained as the second difference value,

[0036] S106: Based on the lesion category, the first difference value of the corresponding lesion category, the second difference value of the corresponding lesion category, the first judgment standard and the second judgment standard, the lesion grades of different lesion categories of fruit tree leaves and the lesion grades of different lesion categories of fruit tree fruits in the processed image are judged, and the maximum lesion grade of the same lesion category in the fruit tree fruit and the fruit tree leaf is taken as the lesion grade of the corresponding lesion category of the fruit tree,

[0037] S107: According to the lesion category of the fruit tree and the lesion grade of the lesion category, the concentration and dose of the treatment drug are further obtained;

[0038] Among them, taking the acquisition steps of the first contrast value and the second contrast value of the a lesion category in S104 as an example:

[0039] S1041: The lesion area in the fruit tree leaf and fruit image in the image is divided and marked by comparing with the fruit tree leaf lesion area gray value range and the fruit tree fruit lesion area gray value range in the contrast database to obtain a marked image. Specifically, represents the unit pixel point of the xth row and yth column of the processed image, and represents the gray value of the unit pixel of the xth column and yth row of the marked image, A is a set of numbers representing the gray value range of the a lesion category of the fruit tree leaf in the database, B is a set of numbers representing the gray value range of the a lesion category of the fruit tree fruit in the database, and the gray value of each pixel point in the analysis image is divided to obtain a marked image:

[0040] ,

[0041] S1042: The image area size with a gray value of 255 in the marked image is obtained by pixel counting as the preliminary a lesion area of the leaf aSpi, the image area size with a gray value of 125 in the marked image is obtained by pixel counting as the preliminary a lesion area of the fruit aSpr, and the image area size with a gray value of 0 in the marked image is obtained by pixel counting as the non-a lesion area aSpt,

[0042] S1043: The first contrast value aRT of the a lesion category is:

[0043] aRT = ,

[0044] S1044: the second contrast value aRW of the lesion category of the a type:

[0045] aRW = ,

[0046] wherein, is a contrast value correction coefficient related to the overall lesion degree, is a priority parameter related to the contrast value correction coefficient, and is obtained by a person skilled in the art based on historical experience, a large number of repeated experiments and optimization training, which will not be repeated here;

[0047] The judgment module of the present application analyzes the thin-shelled pecan fruit tree image shot by the unmanned aerial vehicle through image processing, feature extraction, classification and identification and other technical means, and finally judges the disease and pest situation of the fruit tree, providing accurate information and decision basis for subsequent thin-shelled pecan disease and pest control. Through pre-processing, feature extraction and identification classification of the images shot by the camera device, the system of the present application can accurately judge the type of disease and pest of the thin-shelled pecan fruit tree, helping farmers to take corresponding prevention and control measures in time.

[0048] Example three: combined with the attached Figure 1 , attached Figure 2 , attached Figure 3 and attached Figure 4 , in addition to containing the content of the above examples, it is also characterized in that the treatment agent of the fruit tree is obtained according to the analysis unit, wherein the use methods of different treatment agents are different, part of the treatment agent is used for pest control treatment on the surface of the fruit tree by spraying, and part of the treatment agent is used for nutrient supplement of the fruit tree by irrigating the root of the fruit tree to ensure the normal growth and development of the fruit tree. According to the use method of different treatment agents, the treatment agent is used in a targeted manner through the treatment agent infiltration module and / or the spraying module, thereby improving the defense and treatment efficiency of the thin-shelled pecan pest.

[0049] The therapeutic agent infiltration module comprises a plurality of infiltration units at least partially embedded into the pecan orchard land, wherein each infiltration unit comprises a preset depth irrigation groove previously dug in the pecan orchard land, an irrigation pipe vertically inserted and fixed into the irrigation groove, a liquid storage tank in communication with the top of the irrigation pipe, a liquid inlet pipe in communication with the side tank wall of the liquid storage tank, a first electric control valve arranged in the liquid inlet pipe to control the communication between the liquid inlet pipe and the liquid storage tank, a pressure regulating pump in communication with the liquid storage tank to provide negative pressure to the liquid storage tank, liquid outlet holes uniformly arranged on the side pipe wall of the irrigation pipe, and a liquid level sensor for monitoring the liquid level in the liquid storage tank, and the liquid storage tank is supported by a support seat at a preset height of the pecan orchard land;

[0050] The spraying module comprises a plurality of spraying units arranged in sequence on the infiltration units, a buffer liquid dish fixed above each liquid storage tank, a support rod supporting and fixing the buffer liquid dish above the liquid storage tank at a preset distance, a placing box adjacently arranged on one side of the buffer liquid dish, a first branch pipe in communication with the bottom of the buffer liquid dish, a second branch pipe in communication with the top of the buffer liquid dish, a spraying pipe of a rubber structure with one end in communication with the first branch pipe, a fixed pipe with one end in communication with the other end of the spraying pipe, an atomizing nozzle in communication with the other end of the fixed pipe, a ring plate horizontally sleeved to the outer pipe wall of the fixed pipe, two mounting seats symmetrically fixed to the dish top wall of the buffer liquid dish, a lifting driving mechanism for driving the mounting seats to lift relative to the buffer liquid dish, and a driving liquid pump for driving the liquid in the buffer liquid to flow out from the first branch pipe, the spraying pipe and the atomizing nozzle in sequence;

[0051] The top of the support rod is vertically fixed to the dish bottom wall of the buffer liquid dish, the top of the support rod is vertically fixed to the tank top wall of the liquid storage tank, the fixed pipe is a hard pipe that cannot be bent and deformed, and the spraying pipe is a flexible rubber pipe, wherein the upper end of the placing box is an open structure, the placing box is used to place the spraying pipe, the placing box is provided with a through hole at the bottom, the spraying pipe is partially inserted into the placing box through the through hole, and when the lifting driving mechanism is in a retracted state, part of the spraying pipe is wound and stored in the placing box, and when the lifting driving mechanism is in an extended state, the spraying pipe wound in the placing box is sequentially pulled out from the upper end of the placing box; the lifting driving mechanism comprises at least two telescopic driving rods, the bottoms of which are fixed to the mounting seats, and the top of which is symmetrically fixed to the plate bottom wall of the ring plate;

[0052] The delivery module comprises a liquid tank for storing therapeutic agents, a plurality of first delivery pipes arranged in communication with the liquid tank and the liquid inlet pipes of each of the respective infiltration units, a plurality of first electrically controlled valves for controlling the communication of the first delivery pipes with the liquid tank, a first delivery pump for controlling the quantitative delivery of the therapeutic agents in the liquid tank from the first delivery pipes to the respective liquid storage tanks, a plurality of second delivery pipes arranged in communication with the liquid tank and the respective buffer liquid dishes, a plurality of second electrically controlled valves for controlling the communication of the second delivery pipes with the liquid tank, and a second delivery pump for controlling the quantitative delivery of the therapeutic agents in the liquid tank from the second delivery pipes to the respective buffer liquid dishes, so that the therapeutic agents are delivered through the first delivery pipes and / or the second delivery pipes, and then the therapeutic agents are delivered to the infiltration module and / or the spraying module.

[0053] The present application automatically infiltrates the therapeutic agents into the ground for fruit trees to absorb, or sprays the therapeutic agents, so as to treat and prevent the pests and diseases, reduces the burden of manual operation, reduces the risk of direct contact of the personnel with the pesticides, reduces the harm to the farmers and the pollution to the environment.

[0054] While the present application has been described with reference to various embodiments thereof, it is to be understood that many changes can be made and equivalents can be substituted without departing from the scope of the present application. That is, many modifications, variations, and additions to the practice of the present application can be employed, as will be appreciated or become apparent to those skilled in the art, in conjunction with the teachings of the foregoing description. Although exemplary implementations of the application are described above, various changes and modifications could be made to them without departing from the scope of the application. That is, the methods, systems and devices discussed above are examples. Various configurations can omit, substitute, or add various procedures or components alike. For instance, in alternative configurations, the methods can be performed in an order different from that described, and / or various steps can be added, omitted, and / or combined. Also, features described with respect to certain configurations can be combined in various other configurations, e.g., features from one configuration can be combined with features from a different configuration. Further, the scope of the application should not be limited to the configurations described above, but should be given the broadest possible interpretation within the bounds of the patent law. Also, various elements of the configurations described above can be updated as technology evolves, i.e., many elements are examples and do not limit the scope of the disclosure or claims. And it is understood that those skilled in the art will be able to ascertain many embodiments that would be or, in the future become, the equivalents of the various described configurations.

Claims

1. A thin-shell pecan disease and pest monitoring system based on unmanned aerial vehicle inspection, characterized in that, The thin-shell pecan disease and pest monitoring system comprises a UAV for patrolling a thin-shell pecan planting area in a preset route, a camera device arranged on the UAV for taking images of the thin-shell pecan trees patrolled by the UAV, a judgment module for receiving the images and analyzing the images to determine the disease conditions of the thin-shell pecan trees, a treatment agent infiltration module for inserting the bottom into the soil to infiltrate the treatment agent into the ground for the roots of the thin-shell pecan trees to absorb the treatment agent, a spraying module for spraying the thin-shell pecan trees, and a delivery module for delivering the treatment agent to the infiltration unit and the spraying unit, wherein the treatment agent infiltration module comprises a plurality of infiltration units at least partially embedded into the soil of the thin-shell pecan orchard, the spraying module comprises a plurality of spraying units arranged on the infiltration units in sequence, and the UAV is provided with a positioning sensor for obtaining the position thereof, and the camera device records the taken images and the position of the UAV in matching; The judgment module comprises a preprocessing unit for preprocessing the images of the thin-shell pecan trees taken by the camera device to obtain preprocessed images, an analysis unit for extracting features related to diseases and pests from the preprocessed images, identifying and classifying the extracted features, and further determining the types of diseases and pests of the thin-shell pecan trees in the image acquisition area according to the identification and classification results and preset disease and pest determination criteria, and a comparison database pre-stored with image features of different types of diseases of the thin-shell pecan trees, wherein the preprocessing unit is used for sequentially performing image denoising, image enhancement, color space conversion and scaling processing on the images of the thin-shell pecan trees to obtain preprocessed images of a preset size; The analysis unit realizes the following steps: S101: morphological features in the image are extracted by using existing morphological processing techniques such as edge detection, erosion and dilation, and further, the images of the tree leaves and the tree fruits in the preprocessed image are extracted as processing images according to the pre-stored images of the tree leaves and the tree fruits, and the tree leaf area and the tree fruit area in the processing images are divided by marking, and the size of the tree leaf area Sr1 and the size of the tree fruit area Sr2 in the processing images are calculated by pixel counting; S102: the diseased areas in the images of the tree leaves and the tree fruits in the processing images are divided and the types of the corresponding diseased areas are preliminarily predicted according to the gray value ranges of the diseased areas of the tree leaves and the diseased areas of the tree fruits in the comparison database; S103: the predicted types of the diseased areas in step S102 are further determined and / or excluded according to the shapes of the diseased areas of the tree leaves and the diseased areas of the tree fruits in the comparison database, and the corresponding treatment agent matched with the predicted types of the diseased areas is further obtained after the predicted types of the diseased areas in step S102 are determined; S104: for different types of diseases, a first comparison value related to the diseased areas of the tree leaves of the same type and a second comparison value related to the diseased areas of the tree fruits of the same type are calculated respectively. S105: obtaining a difference value between the first area size ratio and the first contrast value of the same lesion category as a first difference value, and a difference value between the second area size ratio and the second contrast value of the same lesion category as a second difference value; S106: based on the lesion category, the first difference value of the corresponding lesion category, the second difference value of the corresponding lesion category, the first preset judgment standard, and the second judgment standard, to judge the lesion grade of different lesion categories of fruit tree leaves and the lesion grade of different lesion categories of fruit tree fruits in the processed image, and to take the maximum lesion grade of the same lesion category in the fruit tree fruit and the fruit tree leaf as the lesion grade of the corresponding lesion category of the fruit tree; S107: further obtaining the concentration and dose of the treatment agent according to the lesion category of the fruit tree and the lesion grade of the lesion category; Among them, taking the acquisition step of the first contrast value and the second contrast value of the a type lesion category in step S104 as an example: S1041: dividing the diseased area in the image of the fruit tree leaves and the fruit tree fruits in the image by the gray value range of the diseased area of the fruit tree leaves and the gray value range of the diseased area of the fruit tree fruits in the contrast database to obtain a marked image, specifically, taking the diseased area of the fruit tree leaves as an example, the diseased area of the fruit tree leaves in the image is divided into a plurality of sub-regions, and the gray value of each sub-region is calculated to obtain the gray value range of the diseased area of the fruit tree leaves in the image, and the diseased area of the fruit tree fruits in the image is divided into a plurality of sub-regions, and the gray value of each sub-region is calculated to obtain the gray value range of the diseased area of the fruit tree fruits in the image. represents the unit pixel point of the xth row and yth column of the processed image, and represents the gray value of the unit pixel of the xth column and yth row of the marked image, A is the set number representation of the gray value range of the diseased area of the fruit tree leaves of the a type of disease, B is the set number representation of the gray value range of the diseased area of the fruit tree fruits of the a type of disease, and the gray value of each pixel point of the analysis image is divided to obtain the marked image: ， S1042: obtaining the image area size of the gray value 255 in the marked image as the preliminary a type lesion area of the leaf aSpi by pixel counting, obtaining the image area size of the gray value 125 in the marked image as the preliminary a type lesion area of the fruit aSpr by pixel counting, and obtaining the image area size of the gray value 0 in the marked image as the non a type lesion area aSpt by pixel counting, S1043: the first contrast value aRT of the a type lesion category: aRT = aR + aT , S1044: the second contrast value aRW of the a type lesion category: aRW = 0 , wherein, is a contrast value correction factor related to the overall lesion extent, is a priority parameter related to the contrast value correction factor, and are obtained by the person skilled in the art on the basis of historical experience, extensive repeated experimental training and optimization training.

2. The Carya illinoensis pest and disease monitoring system of claim 1, wherein, The contrast database includes the analysis and processing of the training sample based on existing image processing technology to further obtain the corresponding fruit tree leaf lesion area feature of different lesion categories, the lesion area feature of different lesion categories of fruit tree fruit, the treatment agent corresponding to different lesions, and the concentration and dose of the treatment agent corresponding to different grades of different lesions in the fruit tree.

3. The C. glabra disease and pest monitoring system of claim 2, wherein, Each of the infiltration units respectively comprises a preset depth irrigation groove dug in advance in the pecan orchard land, an irrigation pipeline vertically inserted and fixed into the irrigation groove, a liquid storage tank communicated with the top of the irrigation pipeline, a liquid inlet pipeline communicated with the side tank wall of the liquid storage tank, a first electric control valve arranged in the liquid inlet pipeline to control the communication between the liquid inlet pipeline and the liquid storage tank, a pressure regulating pump communicated with the liquid storage tank and used to provide negative pressure to the liquid storage tank, liquid outlet holes uniformly arranged on the side pipe wall of the irrigation pipeline, and a liquid level sensor for monitoring the liquid level of the treatment agent in the liquid storage tank. The liquid storage tank is supported at a preset height of the pecan orchard land by a support seat.

4. The Carya illinoensis pest and disease monitoring system of claim 3, wherein, The spraying module further comprises a buffer liquid dish fixed above each liquid storage tank, a support rod supporting and fixing the buffer liquid dish at a preset distance above the liquid storage tank, a placing box adjacently arranged at one side of the buffer liquid dish, a first branch pipe arranged in communication with the bottom of the buffer liquid dish, a second branch pipe arranged in communication with the top of the buffer liquid dish, a spraying pipe of a glue-like structure arranged in communication with one end of the first branch pipe, a fixed pipe arranged in communication with the other end of the spraying pipe, an atomizing nozzle arranged in communication with the other end of the fixed pipe, a ring plate horizontally sleeved to the outer pipe wall of the fixed pipe, two mounting seats symmetrically fixed to the dish top wall of the buffer liquid dish, a lifting driving mechanism for driving the mounting seat to perform lifting operation relative to the buffer liquid dish, and a driving liquid pump for driving the liquid in the buffer liquid to flow out from the first branch pipe, the spraying pipe and the atomizing nozzle in sequence.

5. The C. glabra disease and pest monitoring system of claim 4, wherein, The conveying module comprises a liquid tank for storing therapeutic agents, a plurality of first conveying pipes arranged in communication with the liquid tank and the liquid inlet pipes of each respective infiltration unit, a plurality of first electric control valves for controlling the communication condition of the first conveying pipes and the liquid tank, a first transmission liquid pump for controlling the quantitative conveying of the therapeutic agents in the liquid tank from the first conveying pipes to each buffer liquid dish, a plurality of second conveying pipes arranged in communication with the liquid tank and each respective buffer liquid dish, a plurality of second electric control valves for controlling the communication condition of the second conveying pipes and the liquid tank, and a second transmission liquid pump for controlling the quantitative conveying of the therapeutic agents in the liquid tank from the second conveying pipes to each buffer liquid dish.

Images