CN114708506A

CN114708506A - Chinese chestnut disease monitoring system

Info

Publication number: CN114708506A
Application number: CN202210365591.7A
Authority: CN
Inventors: 齐慧霞; 李双民; 温晓蕾; 冯丽娜; 孙伟明
Original assignee: Changli County Vocational And Technical Education Center; Hebei Normal University of Science and Technology
Current assignee: Changli County Vocational And Technical Education Center; Hebei Normal University of Science and Technology
Priority date: 2022-04-07
Filing date: 2022-04-07
Publication date: 2022-07-05
Anticipated expiration: 2042-04-07
Also published as: CN114708506B

Abstract

The embodiment of the application discloses chinese chestnut disease monitored control system belongs to planting technical field, includes: the information acquisition module is used for acquiring a growth environment information set of the chestnut trees in at least one target area in the current year; the data acquisition module is used for acquiring the chestnut tree growth data of chestnut trees of at least one target area in a plurality of historical years; the disease prediction module predicts whether the chestnut trees in each target area have disease risks or not on the basis of a disease prediction model; the image acquisition module is used for acquiring a chestnut tree image of the target area when the disease prediction model predicts that the chestnut tree of the target area has disease risk; the disease determining module determines whether the Chinese chestnut trees in the target area have diseases or not through the disease determining model, and has the advantages of timely treating the diseases and the pests in the growth process of the Chinese chestnuts, increasing the yield of Chinese chestnut fruits, improving the quality of Chinese chestnut wood and improving the economic benefit of the Chinese chestnut planting industry.

Description

Chinese chestnut disease monitoring system

Technical Field

The invention mainly relates to the technical field of planting, in particular to a Chinese chestnut disease monitoring system.

Background

Chinese chestnut is one of main woody plants in China, has long cultivation history and high economic value, and is a special product of exported soil. The kernels are fat and sweet, the nutritional value is rich, the kernels contain protein, fat, starch and more vitamins, the wood is hard and is a good building material, and the barks, the branches and the leaves contain much tanned acid and can be extracted as industrial raw materials. Therefore, more and more farmers choose to plant the chestnut trees to increase the economic income, however, along with the increase of the area of the chestnut trees, the occurrence and the spread of the diseases and the pests pose a serious threat to the development of the chestnut industry, particularly, the chestnut epidemic diseases have already caused disastrous consequences, the occurrence and the development of the diseases and the pests become one of the main factors restricting the further development of the chestnut industry, and the yield of the chestnut fruits is reduced, and the quality of the chestnut wood is deteriorated.

Therefore, a Chinese chestnut disease monitoring system is needed to be provided for timely treating plant diseases and insect pests in the growth process of Chinese chestnuts, increasing the yield of Chinese chestnut fruits, improving the quality of Chinese chestnut wood and improving the economic benefit of Chinese chestnut planting industry.

Disclosure of Invention

One of embodiments of the present specification provides a Chinese chestnut disease monitoring system, including: the system comprises an information acquisition module, a data processing module and a data processing module, wherein the information acquisition module is used for acquiring a growth environment information set of chestnut trees of at least one target area in the current year, and the growth environment information set of the chestnut trees of the at least one target area in the current year comprises the growth environment information of the chestnut trees of the at least one target area in at least one past time point in the current year and the growth environment information of the chestnut trees of the at least one target area in at least one future time point in the current year; the data acquisition module is used for acquiring Chinese chestnut tree growth data of the Chinese chestnut trees of the at least one target area in a plurality of historical years, wherein the Chinese chestnut tree growth data comprise the Chinese chestnut tree historical disease data and the growth environment information of the at least one target area at least one historical time point; the disease prediction module predicts whether the chestnut trees of each target area have disease risks or not based on chestnut tree growth data of the chestnut trees of the at least one target area in a plurality of historical years and a chestnut tree growth environment information set of the chestnut trees of the at least one target area in the current year through a disease prediction model; the image acquisition module is used for acquiring a chestnut tree image of the target area when the disease prediction model predicts that the chestnut tree of the target area has a disease risk; and the disease determining module is used for determining whether the Chinese chestnut trees in the target area have diseases or not according to Chinese chestnut tree growth data of the Chinese chestnut trees in the target area with the disease risk in a plurality of historical years, a Chinese chestnut tree growth environment information set of the Chinese chestnut trees in the target area with the disease risk in the current year and Chinese chestnut tree images of the target area with the disease risk in the disease determining model.

In some embodiments, the growth environment information includes at least one of soil humidity, soil ph, soil composition, rainfall, light exposure time, light exposure intensity, ambient temperature, and ambient humidity.

In some embodiments, the disease prediction model comprises an LSTM model.

In some embodiments, the disease prediction module is further to: for each target area, the disease prediction model determines the similarity between the chestnut tree growth data of the chestnut trees of the target area in each historical year and the chestnut tree growth environment information set of the chestnut trees of the target area in the current year; the disease prediction model determines a target historical year based on the similarity between the chestnut tree growth data of the chestnut trees in each historical year in the target area and the chestnut tree growth environment information set of the chestnut trees in the current year in the target area; and the disease prediction model predicts whether the Chinese chestnut trees in each target area have disease risks or not based on the Chinese chestnut tree growth data corresponding to the target historical years.

In some embodiments, the image acquisition module comprises at least one image acquisition device for acquiring chestnut tree images of the target area; the image acquisition device comprises a first rack, a first walking piece arranged at the bottom of the first rack, an image acquisition piece arranged on the first rack, a first obstacle avoidance piece, a first positioning piece and a first central controller, wherein the first walking piece, the image acquisition piece, the first positioning piece and the first obstacle avoidance piece are all electrically connected with the first central controller; the image acquisition module is further used for sending position information of the target area with the disease risk to the first central controller, and the first central controller is used for controlling the first walking piece to move to the target area with the disease risk based on the position information; the first positioning piece is used for acquiring the position information of the image acquisition device in real time in the process that the first central controller controls the first walking piece to move based on the position information, and the image acquisition piece is used for acquiring a chestnut tree image of the target area when the image acquisition device is located in the target area with the disease risk.

In some embodiments, the rack comprises a first height adjusting member and a first angle adjusting member, the first angle adjusting member is disposed on the first height adjusting member, the image capturing member is mounted on the first angle adjusting member, and the first height adjusting member and the first angle adjusting member are both electrically connected with the first central controller; the first central controller adjusts the height of the first angle adjusting piece through the first height adjusting piece, and the first central controller adjusts the shooting angle of the image collecting piece through the first angle adjusting piece.

In some embodiments, the image obtaining module obtains a chestnut tree image of the target region, including: acquiring at least one pre-image; performing image processing on the at least one pre-image to obtain at least one processed pre-image; for each processed pre-image, judging whether the processed pre-image meets the quality requirement; and if the processed pre-image meets the quality requirement, taking the processed pre-image as the chestnut tree image of the target area.

In some embodiments, the system further comprises: and the disease treatment module is used for treating diseases of the chestnut trees in the target area after the disease determination module determines that the chestnut trees in the target area have diseases.

In some embodiments, the disease management module comprises at least one disease management device; the disease treatment device comprises a second rack, a second walking piece arranged at the bottom of the second rack, a second obstacle avoidance piece arranged on the second rack, a second positioning piece, a second central controller and a medicine spraying piece, wherein the second walking piece, the second positioning piece, the second obstacle avoidance piece and the medicine spraying piece are all electrically connected with the second central controller; the disease determining module is further configured to send position information of the target area with the disease to the second central controller, and the second central controller is configured to control the second walking member to move to the target area with the disease based on the position information; the second positioning part is used for acquiring the position information of the disease treatment device in real time when the second central controller controls the second walking part to move based on the position information; the second central controller is also used for controlling the medicine spraying piece to spray the medicine when the disease treatment device is positioned in the target area with the disease.

In some embodiments, the medication spray piece comprises at least one medication spray device for spraying at least one type of medication; the disease determination model is further used for determining the disease type of the target area with diseases based on the Chinese chestnut tree growth data of the Chinese chestnut trees of the target area with the disease risk in a plurality of historical years, the Chinese chestnut tree growth environment information set of the target area with the disease risk in the current year and the Chinese chestnut tree image of the target area with the disease risk; the second central controller is used for controlling the at least one medicine spraying device to spray the corresponding type of medicine based on the disease type.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

fig. 1 is a schematic view of an application scenario of a Chinese chestnut disease monitoring system according to some embodiments of the present application;

FIG. 2 is an exemplary block diagram of a computing device shown in accordance with some embodiments of the present application;

FIG. 3 is an exemplary block diagram of a system for monitoring chestnut diseases according to some embodiments of the present application;

FIG. 4 is an exemplary flow diagram of an image acquisition module acquiring chestnut tree images of a target area according to some embodiments of the present application.

In the figure, 100, application scenarios; 110. a processing device; 120. a network; 130. a user terminal; 140. a storage device; 150. an image acquisition device; 160. a disease management device; 210. a processor; 220. a read-only memory; 230. a random access memory; 240. a communication port; 250. an input/output interface; 260. and a hard disk.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. It is understood that these exemplary embodiments are given solely to enable those skilled in the relevant art to better understand and implement the present invention, and are not intended to limit the scope of the invention in any way. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements. Although various references are made herein to certain modules or units in a system according to embodiments of the present application, any number of different modules or units may be used and run on a client and/or server. The modules are merely illustrative and different aspects of the systems and methods may use different modules.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is a schematic diagram of an application scenario 100 of a Chinese chestnut disease monitoring system according to some embodiments of the present application.

As shown in fig. 1, the application scenario 100 may include a processing device 110, a network 120, a user terminal 130, a storage device 140, at least one image acquisition device 150, and at least one disease management device 160.

In some embodiments, the processing device 110 may be used to process information and/or data related to chestnut disease monitoring. For example, the processing device 110 may obtain the growth environment information set of the chestnut trees of at least one target area in the current year, obtain the chestnut tree growth data of the chestnut trees of at least one target area in a plurality of historical years, predict whether the chestnut trees of each target area have a disease risk based on the chestnut tree growth data of the chestnut trees of at least one target area in a plurality of historical years and the growth environment information set of the chestnut trees of at least one target area in the current year by the disease prediction model, and determine whether the chestnut trees of the target area have a disease based on the chestnut tree growth data of the chestnut trees of the target area with a disease risk in a plurality of historical years, the growth environment information set of the chestnut trees of the target area with a disease risk in the current year and the chestnut tree image of the target area with a disease risk by the disease determination model. In some embodiments, the processing device 110 may be regional or remote. For example, processing device 110 may access information and/or profiles stored in user terminal 130 and storage device 140 via network 120. In some embodiments, processing device 110 may be directly connected to user terminal 130 and storage device 140 to access information and/or material stored therein. In some embodiments, the processing device 110 may execute on a cloud platform. In some embodiments, the processing device 110 may include a processor 210, and the processor 210 may include one or more sub-processors (e.g., a single core processing device or a multi-core processing device). Merely by way of example, the processor 210 may comprise a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), the like, or any combination thereof.

In some embodiments, processing device 110 may include an information acquisition module, a data acquisition module, a disease prediction module, and a disease determination module.

The information acquisition module can be used for acquiring the growth environment information set of the chestnut tree of at least one target area in the current year.

The data acquisition module can be used for acquiring the chestnut tree growth data of the chestnut trees of at least one target area in a plurality of historical years.

The disease prediction module can predict whether the chestnut trees in each target area have disease risks or not through a disease prediction model based on chestnut tree growth data of the chestnut trees in a plurality of historical years in at least one target area and a chestnut tree growth environment information set of the chestnut trees in the current year in at least one target area.

The disease determining module can determine whether the chestnut trees in the target area have diseases or not through a disease determining model based on chestnut tree growth data of the chestnut trees in the target area with the disease risk in a plurality of historical years, a chestnut tree growth environment information set of the chestnut trees in the target area with the disease risk in the current year and a chestnut tree image of the target area with the disease risk.

For more description of the information obtaining module, the data obtaining module, the disease predicting module, and the disease determining module, reference may be made to fig. 3 and related description thereof, which are not repeated herein.

The network 120 may facilitate the exchange of data and/or information in the application scenario 100. In some embodiments, one or more components in the application scenario 100 (e.g., the processing device 110, the user terminal 130, and the storage device 140) may send data and/or information to other components in the processing device 100 via the network 120. For example, the processing device 110 may transmit the virtual scene to the user terminal 130 through the network 120 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network. For example, network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a wireless area network (WLAN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, the like, or any combination thereof.

The user terminal 130 is a terminal device used by a user. In some embodiments, the user terminal 130 may include one or any combination of a mobile device, a tablet, a laptop, and the like. In some embodiments, the mobile device may include a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, smart glasses, a smart helmet, a smart watch, a smart backpack, a smart handle, and the like, or any combination thereof. In some embodiments, the smart mobile device may include a smart phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a POS device, and the like, or any combination thereof.

In some embodiments, storage device 140 may be connected to network 120 to enable communication with one or more components of processing device 100 (e.g., processing device 110, user terminal 130, etc.). One or more components of processing device 100 (e.g., processing device 110, user terminal 130, etc.) may access data or instructions stored in storage device 140 via network 120. In some embodiments, storage device 140 may be directly connected to or in communication with one or more components in processing device 100 (e.g., processing device 110, user terminal 130). In some embodiments, the storage device 140 may be part of the processing device 110. In some embodiments, the processing device 110 may also be located in the user terminal 130.

The image acquisition apparatus 150 may be a device for acquiring an image. For more description of the image capturing device 150, reference may be made to fig. 3 and the related description thereof, which are not repeated herein.

The disease treatment device 160 may be a device for treating diseases of chestnut trees. For more description of the disease management device 160, reference may be made to fig. 3 and the related description thereof, which are not repeated herein.

It should be noted that the foregoing description is provided for illustrative purposes only, and is not intended to limit the scope of the present application. Many variations and modifications will occur to those skilled in the art in light of the teachings herein. The features, structures, methods, and other features of the exemplary embodiments described herein may be combined in various ways to obtain additional and/or alternative exemplary embodiments. For example, the storage device 140 may be a data storage device comprising a cloud computing platform, such as a public cloud, a private cloud, a community and hybrid cloud, and the like. However, such changes and modifications do not depart from the scope of the present application.

FIG. 2 is an exemplary block diagram of a computing device shown in accordance with some embodiments of the present application.

In some embodiments, processing device 110 may be implemented on computing device 200. For example, processing device 110 may implement and execute the get work tasks disclosed herein on computing device 200.

As shown in fig. 2, computing device 200 may include a processor 210, a read only memory 220, a random access memory 230, a communication port 240, an input/output interface 250, and a hard disk 260.

The processor 210 may execute the computing instructions (program code) and perform the functions of the processing device 100 described herein. The computing instructions may include programs, objects, components, data structures, procedures, modules, and functions (which refer to specific functions described herein). For example, the processor 210 may generate a three-dimensional model of the landscape ecosystem based on the modeling data; and generating a virtual scene of the garden ecological environment based on the three-dimensional model. In some embodiments, processor 210 may include microcontrollers, microprocessors, Reduced Instruction Set Computers (RISC), Application Specific Integrated Circuits (ASIC), application specific instruction set processors (ASIP), Central Processing Units (CPU), Graphics Processing Units (GPU), Physical Processing Units (PPU), microcontroller units, Digital Signal Processors (DSP), Field Programmable Gate Array (FPGA), Advanced RISC Machines (ARM), programmable logic devices, any circuit or processor capable of executing one or more functions, or the like, or any combination thereof. For illustration only, the computing device 200 in fig. 2 depicts only one processor, but it should be noted that the computing device 200 in the present application may also include multiple processors.

The memory (e.g., Read Only Memory (ROM)220, Random Access Memory (RAM)230, hard disk 260, etc.) of the computing device 200 may store data/information obtained from any other component of the application scenario 100. Such as a target recipe retrieved from the storage device 140. Exemplary ROMs may include Mask ROM (MROM), Programmable ROM (PROM), erasable programmable ROM (PEROM), Electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. Exemplary RAM may include Dynamic RAM (DRAM), double-data-rate synchronous dynamic RAM (DDR SDRAM), Static RAM (SRAM), and the like.

The input/output interface 250 may be used to input or output signals, data, or information. In some embodiments, the input/output interface 250 may enable a user to interface with the computing device 200. For example, the user inputs a user recipe selection instruction to the computing device 200 via the input/output interface 250. In some embodiments, input/output interface 250 may include an input device and an output device. Exemplary input devices may include a keyboard, mouse, touch screen, microphone, and the like, or any combination thereof. Exemplary output devices may include a display device, speakers, printer, projector, etc., or any combination thereof. Exemplary display devices may include Liquid Crystal Displays (LCDs), Light Emitting Diode (LED) based displays, flat panel displays, curved displays, television equipment, Cathode Ray Tubes (CRTs), and the like, or any combination thereof. The communication port 240 may be connected to a network for data communication. The connection may be a wired connection, a wireless connection, or a combination of both. The wired connection may include an electrical cable, an optical cable, or a telephone line, among others, or any combination thereof. The wireless connection may include bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobile networks (e.g., 3G, 4G, or 5G, etc.), and the like, or any combination thereof. In some embodiments, the communication port 240 may be a standardized port, such as RS232, RS485, and the like. In some embodiments, the communication port 240 may be a specially designed port.

Computing device 200 depicts only one central processor and/or processor for purposes of illustration only. However, it should be noted that the computing device 200 in the present application may include a plurality of central processing units and/or processors, and thus the operations and/or methods described in the present application implemented by one central processing unit and/or processor may also be implemented by a plurality of central processing units and/or processors, collectively or independently. For example, a central processor and/or processors of computing device 200 may perform steps a and B. In another example, steps a and B may also be performed by two different central processors and/or processors in computing device 200, either in combination or separately (e.g., a first processor performing step a and a second processor performing step B, or both a first and second processor performing steps a and B together).

Fig. 3 is an exemplary block diagram of a system for monitoring chestnut diseases according to some embodiments of the present application.

As shown in fig. 3, a system for monitoring chestnut diseases may include an information acquisition module, a data acquisition module, a disease prediction module, an image acquisition module, and a disease determination module.

In some embodiments, the set of information on the growing environment of the chestnut trees of the at least one target area in the current year includes information on the growing environment of the chestnut trees of the at least one target area in the current year at least one past time point and information on the growing environment of the chestnut trees of the at least one target area in the current year at least one future time point, wherein the target area can be an area where chestnut trees are planted, and it is understood that one target area can be planted with at least one chestnut tree. For example, if the current time is 20 days at 4 months at 2022 years, the growing environment information of at least one past time point in the current year may include the growing environment information of chestnut trees of at least one target region at 1 month at 2022 years, 2 months at 2022 years and 3 months at 2022 years; the growth environment information of at least one future time point in the current year may include predicted growth environment information of chestnut trees of at least one target region in months 4-12 in 2022.

In some embodiments, the information obtaining module may predict growth environment information of the chestnut trees of the at least one target region at least one future time point of the year through the prediction model. The input of the prediction model can be the chestnut tree growth data of the chestnut trees of at least one target area in a plurality of historical years, and the output of the prediction model is the growth environment information of the chestnut trees of at least one target area in at least one future time point in the current year. The prediction model may be an RNN (Current Neural network) model, an LSTM (Long Short-term memory, LSTM) model, or the like.

In some embodiments, the growth environment information may include at least one of soil humidity, soil ph, soil composition, rainfall, light exposure time, light exposure intensity, ambient temperature, and ambient humidity.

In some embodiments, the chestnut tree growth data comprises historical disease data and growth environment information of the chestnut trees of at least one target area at least one historical time point, wherein the historical disease data of the chestnut trees can comprise disease types, treatment schemes, disease duration and the like of the chestnut trees of at least one target area at least one historical time point.

In some embodiments, the disease prediction model may include an LSTM model. The input of the disease prediction model can comprise Chinese chestnut tree growth data of at least one target area in a plurality of historical years and a Chinese chestnut tree growth environment information set of at least one target area in the current year, and the output of the disease prediction model can indicate whether the Chinese chestnut tree of each target area has disease risk.

In some embodiments, the disease prediction module is further to:

for each of the target areas, the target area is,

the disease prediction model determines the similarity between the chestnut tree growth data of the chestnut trees in the target area in each historical year and the chestnut tree growth environment information set of the chestnut trees in the target area in the current year;

the disease prediction model determines the target historical year based on the similarity between the chestnut tree growth data of the chestnut trees in the target area in each historical year and the chestnut tree growth environment information set of the chestnut trees in the target area in the current year;

and the disease prediction model predicts whether the Chinese chestnut trees in each target area have disease risks or not based on the Chinese chestnut tree growth data corresponding to the target historical years.

For example, if the similarity between the growth environment information of the chestnut tree in a certain target area determined by the disease prediction model in 4-12 months in 2022 and the growth environment information of the chestnut tree in a certain historical year (for example, 2010) is greater than a preset similarity threshold (for example, 90%), the disease prediction model may acquire the historical disease data of the chestnut tree in 4 months in 2010 of the chestnut tree in the target area, and if the chestnut tree in the target area has a disease in 4 months in 2010, the disease prediction model may determine that the chestnut tree in the target area has a disease risk in 4 months in the current year; if the chestnut trees in the target area are not damaged in 4 months 2010, the disease prediction model can determine that the chestnut trees in the target area have no disease risk in 4 months in the current year.

The image acquisition module can be used for acquiring a chestnut tree image of the target area when the disease prediction model predicts that the chestnut tree of the target area has disease risk.

In some embodiments, the image acquisition module may comprise at least one image acquisition device 150, the image acquisition device 150 being configured to acquire a chestnut tree image of the target area.

In some embodiments, the image capturing device 150 may include a first frame, a first walking member disposed at the bottom of the first frame, an image capturing member disposed on the first frame, a first obstacle avoidance member, a first positioning member, and a first central controller, and the first walking member, the image capturing member, the first positioning member, and the first obstacle avoidance member are all electrically connected to the first central controller. The image acquisition module is further used for sending the position information of the target area with the disease risk to the first central controller, and the first central controller is used for controlling the first walking piece to move to the target area with the disease risk based on the position information. The first positioning part is used for acquiring the position information of the image acquisition device 150 in real time in the process that the first central controller controls the first walking part to move based on the position information, and the image acquisition part is used for acquiring a chestnut tree image of a target area when the image acquisition device 150 is located in the target area with a disease risk.

In some embodiments, the frame may include a first height adjustment member and a first angle adjustment member, the first angle adjustment member is disposed on the first height adjustment member, the image capturing member is mounted on the first angle adjustment member, and both the first height adjustment member and the first angle adjustment member are electrically connected to the first central controller. The first central controller adjusts the height of the first angle adjusting piece through the first height adjusting piece, and the first central controller adjusts the shooting angle of the image collecting piece through the first angle adjusting piece.

In some embodiments, the central controller can flexibly adjust the height of the first angle adjusting part and the shooting angle of the image acquisition part, so as to acquire chestnut tree images of the target area under different heights and shooting angles.

In some embodiments, with reference to fig. 4, the acquiring the image of the chestnut tree of the target area by the image acquiring module may include:

acquiring at least one pre-image;

performing image processing on at least one pre-image to obtain at least one processed pre-image;

for each of the pre-images after processing,

judging whether the processed pre-image meets the quality requirement or not;

and if the processed pre-image meets the quality requirement, taking the processed pre-image as the chestnut tree image of the target area.

In some embodiments, the pre-processing may also include image denoising, image enhancement, and the like.

The image denoising refers to removing interference information in a pre-image. The interference information in the pre-image may degrade the quality of the pre-image. In some embodiments, the image acquisition module may implement image denoising through a median filter, a machine learning model, or the like.

Image enhancement refers to adding missing information in a pre-image. Missing information in the pre-image can cause image blurring. In some embodiments, the image acquisition module may implement image enhancement through a smoothing filter, a median filter, or the like.

In some embodiments, the pre-processing may also include other operations (e.g., image segmentation, etc.).

In some embodiments, after a pre-image is obtained, the image obtaining module may further determine whether the obtained pre-image meets the quality requirement, and delete the pre-image if the obtained pre-image does not meet the quality requirement.

In some embodiments, the quality of the image may be characterized by a quality characteristic of the image. In some embodiments, the quality characteristics of the image may be noise characteristics, gray scale distribution, global gray scale, resolution, contrast, and the like.

Noise is interference information in an image, and noise in a pre-image not only reduces the quality of a first image and affects the visual effect of the image, but also affects the efficiency of subsequent processing such as image recognition. The noise feature is used for describing noise information of the image, and is a numerical expression of information related to noise in the image. In some embodiments, the noise characteristics may include noise distribution, noise global strength, noise level, noise rate, and the like.

The gray distribution characteristics reflect the distribution of the gray values of the pixels in the image. The gradation distribution characteristics can be obtained by processing the image. For example, the mean value or the standard deviation of the gradation values in the image may be used as the gradation distribution characteristic.

Global grayscale refers to the average or weighted average of the grayscale values of all pixels in an image. The larger the global grayscale value, the darker the image, and the smaller the global grayscale value, the brighter the image.

Resolution refers to the amount of information stored in an image. In some embodiments, the resolution may be characterized by the number of pixels contained in a unit area of the image. It will be appreciated that the higher the resolution, the sharper the image.

Contrast refers to the measurement of different brightness levels in an image, representing the magnitude of the image's gray scale contrast. In some embodiments, the contrast ratio may be obtained by using the formulas of weber contrast ratio, root mean square contrast ratio, Michelson contrast ratio, and the like.

In some embodiments, the image acquisition module may analyze the quality characteristics of the pre-image to determine whether the quality of the pre-image meets the requirements. For example, if the resolution of the pre-image is less than 1024x768, the pre-image does not meet the quality requirement. For example, if the global gray scale of the pre-image is smaller than the preset gray scale, the pre-image does not meet the quality requirement.

In some embodiments, before the pre-image is identified, the image acquisition module judges whether the pre-image meets the quality requirement, so that the identification of an invalid pre-image is avoided, and the accuracy and efficiency of image identification are improved.

The disease determining module can determine whether the Chinese chestnut trees in the target area have diseases or not through the disease determining model based on Chinese chestnut tree growth data of Chinese chestnut trees in a plurality of historical years in the target area with disease risk, a Chinese chestnut tree growth environment information set of the Chinese chestnut trees in the target area with disease risk in the current year and Chinese chestnut tree images in the target area with disease risk.

The input of the disease determination model can comprise Chinese chestnut tree growth data of a target area with disease risk in a plurality of historical years, a Chinese chestnut tree growth environment information set of the target area with disease risk in the current year and a Chinese chestnut tree image of the target area with disease risk, and the output can comprise the step of determining whether the Chinese chestnut tree of the target area has disease. The prediction model may be an RNN (Current Neural network) model, an LSTM (Long Short-Term Memory, LSTM) model, or the like.

In some embodiments, the Chinese chestnut disease monitoring system may further include a disease treatment module, and the disease treatment module may be configured to perform disease treatment on the Chinese chestnut trees in the target area after the disease determination module determines that the Chinese chestnut trees in the target area have diseases.

In some embodiments, the disease management module may include at least one disease management device 160. Disease treatment device 160 includes the second frame, sets up the second running member in second frame bottom, sets up the second in the second frame and keeps away barrier piece, second setting element, second central controller and medicine and spray the piece, and second running member, second setting element, second keep away barrier piece and medicine and spray the piece and all be connected with second central controller electricity. The disease determining module is further used for sending the position information of the target area with the disease to the second central controller, and the second central controller is used for controlling the second walking part to move to the target area with the disease based on the position information. The second positioning element is used for acquiring the position information of the disease treatment device 160 in real time in the process that the second central controller controls the second walking element to move based on the position information. The second central controller is also used to control the medicine spraying member to spray the medicine when the disease treating device 160 is located at the target area where the disease exists.

In some embodiments, the medication spray element may comprise at least one medication spray device for spraying at least one type of medication. The disease determination model is also used for determining the disease type of the target area with diseases based on the Chinese chestnut growth data of the Chinese chestnut trees of the target area with diseases at risk in multiple historical years, the growth environment information set of the Chinese chestnut trees of the target area with diseases at risk in the current year and the Chinese chestnut tree images of the target area with diseases at risk, wherein the disease type can be fruit pests (such as Chinese chestnut elephant, chestnut-snowflake elephant, scirpus mollissima, two-spotted Chinese chestnut elephant, oak elephant, chestnut leaf moth, Qianyou moth, peach moth and the like), buds, leaf pests (such as moth-eating flower wheat moth, chestnut window moth, Chongyang wood spot moth, apple palm boat moth, oak boat moth, yellow two-star moth, wild jujube tree moth, big straw moth, white purse moth and the like), branch and trunk pests (such as chestnut, chestnut chain gall, black chain moth, white scale, black silk worm, etc.) Fruit diseases (e.g., anthracnose of chestnut, kernel blotch disease, etc.), bud and leaf diseases (e.g., bacterial blight of chestnut, leaf spot of chestnut, powdery mildew of chestnut, anthracnose of chestnut, leaf blight of chestnut, etc.), branch and stem diseases (e.g., bacterial blight of chestnut, rot of chestnut, dead branch of chestnut, plaster of chestnut, etc.), root diseases (e.g., root rot of chestnut, rhizoctonia solani, etc.).

In some embodiments, the second central controller is for controlling the at least one medication spraying device to spray a corresponding type of medication based on the disease type.

For example, when the disease determination model determines that the disease type of the target area where the disease exists is a damping off disease, the second central controller may control the medicine spraying apparatus for spraying 100 times carbendazim or 500 times arsenic liquid to spray 100 times carbendazim or 500 times arsenic liquid onto the chestnut trees of the target area where the disease exists.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may run entirely on the supervisor computer, as a stand-alone software package, partly on the supervisor computer, partly on a remote computer or entirely on the remote computer or server. In the latter case, the remote computer may be connected to the proctoring person computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or connected to an external computer (e.g., through the internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the present disclosure.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A Chinese chestnut disease monitoring system is characterized by comprising:

the system comprises an information acquisition module, a data processing module and a data processing module, wherein the information acquisition module is used for acquiring a growth environment information set of chestnut trees of at least one target area in the current year, and the growth environment information set of the chestnut trees of the at least one target area in the current year comprises the growth environment information of the chestnut trees of the at least one target area in at least one past time point in the current year and the growth environment information of the chestnut trees of the at least one target area in at least one future time point in the current year;

the data acquisition module is used for acquiring the chestnut tree growth data of the chestnut trees of the at least one target area in a plurality of historical years, wherein the chestnut tree growth data comprises the historical disease data and the growth environment information of the chestnut trees of the at least one target area at least one historical time point;

the disease prediction module predicts whether the chestnut trees of each target area have disease risks or not based on chestnut tree growth data of the chestnut trees of the at least one target area in a plurality of historical years and a chestnut tree growth environment information set of the chestnut trees of the at least one target area in the current year through a disease prediction model;

the image acquisition module is used for acquiring a chestnut tree image of the target area when the disease prediction model predicts that the chestnut tree of the target area has disease risk;

and the disease determining module is used for determining whether the chestnut trees in the target area have diseases or not according to the chestnut tree growth data of the chestnut trees in the target area with the disease risk in a plurality of historical years, the chestnut tree growth environment information set of the chestnut trees in the target area with the disease risk in the current year and the chestnut tree image of the target area with the disease risk by the disease determining model.

2. A system for monitoring chestnut disease according to claim 1, characterized in that the growth environment information includes at least one of soil humidity, soil ph, soil composition, rainfall, light exposure time, light exposure intensity, ambient temperature, ambient humidity.

3. A chestnut disease monitoring system as claimed in claim 1, wherein the disease prediction model includes an LSTM model.

4. A system for monitoring diseases in chestnuts, as claimed in claim 3, wherein said disease prediction module is further configured to:

for each of said target areas, a target region is selected,

the disease prediction model determines a target historical year based on the similarity between the chestnut tree growth data of the chestnut trees in each historical year in the target area and the chestnut tree growth environment information set of the chestnut trees in the current year in the target area;

5. A chestnut disease monitoring system according to claim 4, characterized in that the image acquisition module includes at least one image acquisition device for acquiring images of chestnut trees in the target area;

the image acquisition device comprises a first rack, a first walking piece arranged at the bottom of the first rack, an image acquisition piece arranged on the first rack, a first obstacle avoidance piece, a first positioning piece and a first central controller, wherein the first walking piece, the image acquisition piece, the first positioning piece and the first obstacle avoidance piece are all electrically connected with the first central controller;

the image acquisition module is further used for sending position information of the target area with the disease risk to the first central controller, and the first central controller is used for controlling the first walking piece to move to the target area with the disease risk based on the position information;

the first positioning piece is used for acquiring the position information of the image acquisition device in real time in the process that the first central controller controls the first walking piece to move based on the position information, and the image acquisition piece is used for acquiring a chestnut tree image of the target area when the image acquisition device is located in the target area with the disease risk.

6. A Chinese chestnut disease monitoring system as claimed in claim 5, wherein the rack includes a first height adjustment member and a first angle adjustment member, the first angle adjustment member is disposed on the first height adjustment member, the image capturing member is mounted on the first angle adjustment member, and the first height adjustment member and the first angle adjustment member are both electrically connected to the first central controller;

the first central controller adjusts the height of the first angle adjusting piece through the first height adjusting piece, and the first central controller adjusts the shooting angle of the image collecting piece through the first angle adjusting piece.

7. A system for monitoring chestnut diseases according to claim 1, wherein the image acquisition module acquires images of chestnut trees in the target area, and the system comprises:

acquiring at least one pre-image;

performing image processing on the at least one pre-image to obtain at least one processed pre-image;

for each of the pre-images after the processing,

judging whether the processed pre-image meets the quality requirement;

8. A system for monitoring diseases in chestnuts according to any one of claims 1 to 7, further comprising:

and the disease treatment module is used for treating the diseases of the chestnut trees in the target area after the disease determination module determines that the chestnut trees in the target area have the diseases.

9. A chestnut disease monitoring system as claimed in claim 8, wherein the disease management module includes at least one disease management device;

the disease treatment device comprises a second rack, a second walking piece arranged at the bottom of the second rack, a second obstacle avoidance piece arranged on the second rack, a second positioning piece, a second central controller and a medicine spraying piece, wherein the second walking piece, the second positioning piece, the second obstacle avoidance piece and the medicine spraying piece are all electrically connected with the second central controller;

the disease determining module is further configured to send position information of the target area with the disease to the second central controller, and the second central controller is configured to control the second walking member to move to the target area with the disease based on the position information;

the second positioning part is used for acquiring the position information of the disease treatment device in real time when the second central controller controls the second walking part to move based on the position information;

the second central controller is also used for controlling the medicine spraying piece to spray the medicine when the disease treatment device is positioned in the target area with the disease.

10. A chestnut disease monitoring system as claimed in claim 9, wherein the drug spray unit includes at least one drug spray device for spraying at least one type of drug;

the disease determination model is further used for determining the disease type of the target area with diseases based on the Chinese chestnut tree growth data of the Chinese chestnut trees of the target area with the disease risk in a plurality of historical years, the Chinese chestnut tree growth environment information set of the target area with the disease risk in the current year and the Chinese chestnut tree image of the target area with the disease risk;

the second central controller is used for controlling the at least one medicine spraying device to spray the corresponding type of medicine based on the disease type.