Method, device and equipment for monitoring microclimate in crop canopies and predicting diseases and insect pests, and storage medium
By simultaneously acquiring microclimate and pest and disease data within the crop canopy and constructing a prediction model, the problem of inaccurate prediction of pests and diseases within the canopy was solved, precise pest and disease control was achieved, and agricultural production efficiency was improved.
Patent Information
- Application Number
- CN202510824251.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-19
- Publication Date
- 2025-09-26
AI Technical Summary
Existing technologies are unable to accurately monitor microclimate data within the crop canopy, resulting in inaccurate predictions of pests and diseases, making it difficult for agricultural producers to respond promptly and effectively, causing economic losses.
By synchronously acquiring time series microclimate data and pest and disease occurrence data within the crop canopy, a database is established, and a pest and disease occurrence prediction model and an identification and detection model are constructed to predict the type, degree and probability of pests and diseases within the canopy.
It provides precise pest and disease control measures, reduces economic losses and improves agricultural production efficiency.
Smart Images
Figure CN120706642A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of agricultural plant protection technology, and in particular to a method, device, equipment and storage medium for monitoring microclimate within a crop canopy and predicting the occurrence of pests and diseases. Background Art
[0002] In modern agricultural production and management, effective pest and disease control ensures crop yield and quality. Accurately monitoring the crop growth environment, particularly the microclimate within the canopy, and using this data to timely predict pest and disease trends, can help agricultural producers develop scientifically sound prevention and control strategies, achieve precise pesticide application, reduce economic losses, and mitigate negative impacts on the ecological environment.
[0003] At present, the main equipment commonly used for climate monitoring of crop planting environments is a small weather station. Although such weather stations can obtain data such as temperature, humidity, wind speed and wind direction, the data collected is only climate data around the crop planting area or near the crops, and cannot accurately capture the microclimate information closely related to the occurrence of pests and diseases inside the crop canopy. For example, the temperature, humidity, etc. inside the canopy will differ from the surrounding environment, and these differences have a key impact on the breeding, spread and development of pests and diseases. In addition, the existing technology system is seriously lacking an effective prediction method and system that closely combines microclimate data within the crop canopy to predict the occurrence of pests and diseases. This makes it difficult for agricultural producers to take timely, accurate and effective response measures when facing the threat of pests and diseases inside the crop canopy. Once pests and diseases break out, they will cause huge losses. Summary of the Invention
[0004] The present invention aims to provide a method, device, equipment and storage medium for monitoring the microclimate within the crop canopy and predicting the occurrence of pests and diseases, so as to solve the problem of insufficient technology for monitoring the microclimate within the crop canopy and predicting the occurrence of pests and diseases in actual agricultural production management, and help agricultural producers accurately formulate reasonable and effective pest and disease control measures.
[0005] In a first aspect, the present invention provides a method for monitoring microclimate within a crop canopy and predicting the occurrence of pests and diseases, comprising: Synchronously obtain time series microclimate data and time series pest and disease occurrence data within the crop canopy; Establish a database based on synchronized time-series microclimate data within the crop canopy and time-series pest and disease occurrence data; Construct pest and disease occurrence prediction models and identification and detection models based on the database; Based on the pest and disease occurrence prediction model and identification detection model, the type, degree and probability of pest and disease occurrence in the crop canopy are predicted.
[0006] In a second aspect, the present invention provides a device for monitoring microclimate within a crop canopy and predicting the occurrence of pests and diseases, comprising at least one set of a data acquisition module, a geographic positioning module, a data storage module, a data transmission module, a processing and control module, a power supply module, and an installation and fixing module, wherein: The data acquisition module includes at least one temperature, humidity, wind speed, wind direction, air pressure, light radiation intensity and camera sensor; the temperature, humidity, wind speed, wind direction, air pressure, light radiation intensity sensor is used to obtain the time series microclimate data; the camera sensor is used to obtain time series pest and disease occurrence data; The geolocation module is used to obtain geolocation data for the location of the microclimate monitoring and pest and disease occurrence prediction area within the crop canopy, including year, month, day, hour, minute, second, longitude and latitude, and altitude information tag data; The data storage module is used for storing data, programs and instructions; The data transmission module is used for wired and / or wireless transmission of data, programs and instructions between different devices and storage media; The processing control module is used to control the program execution and data processing of the normal operation of the data acquisition module, geographic positioning module, data storage module, data transmission module, and power supply module; The power supply module is used to ensure the normal operation of the data acquisition module, geographic positioning module, data storage module, data transmission module and processing control module; The installation and fixing module is used for installing the data acquisition module, geographic positioning module, data storage module, data transmission module, processing control module, and power supply module, and fixing the microclimate monitoring and pest and disease occurrence prediction device within the crop canopy at any position inside the crop canopy.
[0007] In a third aspect, the present invention provides a device for monitoring microclimate within a crop canopy and predicting the occurrence of pests and diseases, comprising a memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors can implement the method for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases as described in the first aspect.
[0008] In a fourth aspect, the present invention provides a storage medium storing computer-executable instructions, which, when executed by a computer processor, are used to execute the method for microclimate monitoring and pest and disease occurrence prediction within the crop canopy as described in the first aspect.
[0009] The present invention synchronously acquires time-series microclimate data and pest and disease occurrence data within the crop canopy and establishes a database; constructs a pest and disease occurrence prediction model and an identification detection model based on the historical time-series data in the database to predict the currently collected microclimate data and pest and disease occurrence data, and clarifies the type, degree and probability of pest and disease occurrence within the crop canopy; it can help agricultural production managers formulate timely, accurate and effective prevention and control measures to reduce economic losses. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Figure 1 This is a flow chart of the method for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases provided by the present invention.
[0011] Figure 2 This is a schematic diagram of the structure of the device for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases provided by the present invention.
[0012] Figure 3 This is a schematic diagram of the structure of the equipment for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases provided by the present invention. DETAILED DESCRIPTION
[0013] To further clarify the objectives and technical solutions of the present invention, specific embodiments of the present invention are described below with reference to the accompanying drawings. These embodiments are intended only to illustrate specific implementations of the present invention and are not intended to limit the present invention. Furthermore, for ease of description and explanation, the drawings illustrate only some examples relevant to the present invention, rather than all of its contents. When the following description refers to the drawings, unless otherwise specified, identical numerals in different figures represent identical or similar elements. Before presenting the exemplary embodiments of the present invention in detail, it should be noted that some exemplary embodiments are described as flow charts. Although the flow charts illustrate various operations or steps as sequential processes, the order of implementation of the various operations or steps can be rearranged and performed in parallel or simultaneously. When each operation or step is completed, the aforementioned process may terminate, but may include additional operations or steps not illustrated in the drawings. The aforementioned processes may correspond to implemented methods, functions, subroutines, and the like. Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Furthermore, the drawings are schematic illustrations only and are not necessarily drawn to scale. The block diagrams shown in the drawings represent functional entities only and do not necessarily correspond to physically separate entities. These functional entities may be implemented in software form, or in one or more hardware modules, integrated circuits, network platforms, processor devices, or microcontroller devices. It should also be understood that the term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.
[0014] The method for microclimate monitoring and pest and disease occurrence prediction within the crop canopy provided by the present invention can be applied to crop supervision in various crop planting scenarios such as fields, greenhouses and orchards. It aims to help agricultural production managers timely obtain the occurrence type, degree and probability of pests and diseases within the crop canopy, formulate accurate and effective prevention and control measures, and improve production efficiency.
[0015] Figure 1 A flowchart of a method for monitoring microclimate within a crop canopy and predicting pest and disease occurrences, provided in an embodiment of the present invention, is provided. The method can be implemented by a device for monitoring microclimate within a crop canopy and predicting pest and disease occurrences through hardware and / or software, and can be integrated into the device for monitoring microclimate within a crop canopy and predicting pest and disease occurrences.
[0016] The following describes the method for monitoring the microclimate in the crop canopy and predicting the occurrence of pests and diseases using a device for monitoring the microclimate in the crop canopy and predicting the occurrence of pests and diseases as an example. Figure 1 ,The methods for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases are as follows.
[0017] S1: Synchronously obtain time series microclimate data and time series pest and disease occurrence data within the crop canopy.
[0018] For example, within the crop canopy determined to be sampled, time series temperature, humidity, wind speed, wind direction, air pressure, light radiation intensity values and time series canopy images (photos) with year, month, day, hour, minute, second, longitude, latitude and altitude information labels are synchronously obtained and stored and transmitted.
[0019] For example, inside the canopy of the citrus trees that need to be sampled, the temperature, humidity, wind speed, wind direction, air pressure, light radiation intensity values and images inside the canopy with information labels of year, month, day, hour, minute, second, longitude and latitude, and altitude are collected once every 15 minutes and stored and transmitted.
[0020] S2: Establish a database based on synchronized time-series microclimate data within the crop canopy and time-series pest and disease occurrence data.
[0021] Exemplarily, the time series microclimate data within the crop canopy obtained by storage and / or transmission are cleaned and labeled, and after removing information labels and / or data samples with incomplete data, pest and disease occurrence information labels are added, and the data are arranged in chronological order according to a format in which the information labels and numerical values strictly correspond to each other to form a time series microclimate database; the time series pest and disease occurrence data obtained by storage and / or transmission are cleaned and labeled, and after removing information labels and / or data samples with incomplete data, pest and disease occurrence information labels are added, and the data are arranged in chronological order according to a format in which the information labels and pest and disease occurrence data strictly correspond to each other to form a time series pest and disease occurrence database.
[0022] For example, the time series microclimate data within the citrus tree canopy obtained through storage and / or transmission are cleaned, and after removing information labels and / or data samples with incomplete data, information labels on the type and degree of pests and diseases are added, and the data are arranged in chronological order in a format with strict correspondence between the year, month, day, hour, minute, second, longitude and latitude, altitude, type and degree of pests and diseases, and temperature, humidity, wind speed, wind direction, air pressure, and light radiation intensity values to establish a time series microclimate database within the citrus tree canopy; the internal images of the citrus tree canopy obtained through storage and / or transmission are marked, and the locations of pests and diseases in the images are selected with arbitrary closed polygons, and information labels on the year, month, day, hour, minute, second, longitude and latitude, altitude, type and degree of pests and diseases are added, and the data are arranged in chronological order in a format with strict correspondence between the information labels and the image data within the citrus tree canopy to establish a time series pest and disease occurrence database.
[0023] S3: Construct pest and disease occurrence prediction models and identification and detection models based on the database.
[0024] Exemplarily, based on the time series crop canopy microclimate database and the time series pest and disease occurrence database, the time series microclimate data within the crop canopy and the time series pest and disease occurrence data with pest and disease occurrence information labels are used to create sample sets for machine learning algorithm model training and verification, and the sample sets are used to train, verify and debug the machine learning algorithm model to construct a pest and disease occurrence prediction model and an identification and detection model.
[0025] For example, the time series temperature, humidity, wind speed, wind direction, air pressure, and light radiation intensity data in the citrus tree canopy with information labels of year, month, day, hour, minute, second, longitude and latitude, altitude, and type and degree of pest and disease occurrence in the time series crop microclimate database are made into a time series citrus tree canopy microclimate data sample set that supports the use of LSTM and / or CNN-LSTM algorithm models, and the data sample set is divided into a training sample set and a verification sample set in a ratio of 8:2; the time series citrus tree canopy microclimate data training and verification sample sets are used to train LSTM and / or CNN-LSTM algorithm models to obtain the model with the optimal parameter weight configuration as the pest and disease occurrence model in the citrus tree canopy. Prediction model; the time series pest and disease images in the citrus canopy with information labels of year, month, day, hour, minute, second, longitude and latitude, altitude, type and degree of pest and disease occurrence in the time series pest and disease occurrence database are made into a time series citrus canopy pest and disease occurrence data sample set that supports the use of algorithm models such as YOLO, U-net and / or CNN-Mask, and the data sample set is divided into a training sample set and a validation sample set in a ratio of 8:2; the time series citrus canopy pest and disease occurrence data training and validation sample sets are used to train algorithm models such as YOLO, U-net and / or CNN-Mask to obtain a citrus canopy pest and disease identification and detection model with the optimal parameter weight configuration.
[0026] S4: Predict the type, extent and probability of occurrence of pests and diseases within the crop canopy based on the pest and disease occurrence prediction model and identification and detection model.
[0027] Exemplarily, the constructed pest and disease occurrence prediction model and identification detection model are used to predict the time series crop canopy microclimate data and pest and disease occurrence data currently collected, stored and / or transmitted, and output the pest and disease occurrence type, degree and probability prediction results with year, month, day, hour, minute, second, longitude and latitude, and altitude information labels.
[0028] For example, a citrus canopy pest and disease occurrence prediction model constructed based on the time series microclimate database within the citrus canopy is used to predict the time series microclimate data within the citrus canopy currently collected, stored and / or transmitted, and the probability results of leaf disease occurrence are output with information labels of year, month, day, hour, minute, second, longitude and latitude, and altitude; a citrus canopy pest and disease occurrence identification and detection model constructed based on the time series microclimate database within the citrus canopy is used to detect the time series image data within the citrus canopy currently collected, stored and / or transmitted, and the type, degree and probability identification detection results of pest occurrence are output with information labels of year, month, day, hour, minute, second, longitude and latitude, and altitude.
[0029] Figure 2 The schematic diagram of the structure of a device for monitoring microclimate in the crop canopy and predicting the occurrence of pests and diseases provided by the present invention is shown. Figure 2 The device for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases includes a data acquisition module 21, a geographic positioning module 22, a data storage module 23, a data transmission module 24, a processing and control module 25, a power supply module 26 and an installation and fixing module 27.
[0030] Among them, the data acquisition module 21 is used to collect time series crop canopy microclimate data and pest and disease occurrence data; the geographic positioning module 22 is used to obtain the time and geographic positioning data of the crop canopy microclimate monitoring and pest and disease occurrence prediction area location, including year, month, day, hour, minute, second, longitude and latitude, and altitude information tag data; the data storage module 23 is used to store data, programs and instructions; the data transmission module 24 is used for wired and / or wireless transmission of data, programs and instructions between different devices and storage media; the processing control module 25 is used to control the data acquisition module 21, the geographic positioning module The module 22, the data storage module 23, the data transmission module 24 and the power supply module 26 are used for normal program execution and data processing; the power supply module 26 is used to ensure the normal operation of the data acquisition module 21, the geographic positioning module 22, the data storage module 23, the data transmission module 24 and the processing control module 25; the installation and fixing module 27 is used for the installation of the data acquisition module 21, the geographic positioning module 22, the data storage module 23, the data transmission module 24, the processing control module 25 and the power supply module 26, and the fixation of the microclimate monitoring and pest and disease occurrence prediction device in the crop canopy at any position inside the crop canopy.
[0031] It should be noted that in the embodiment of the above-mentioned device for microclimate monitoring and pest and disease occurrence prediction within the crop canopy, the modules included are divided only according to functional logic and are not limited to the above-mentioned division, as long as the corresponding functions are achieved; in addition, the specific names of the functional modules are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.
[0032] An embodiment of the present invention provides a device for monitoring the microclimate within a crop canopy and predicting the occurrence of pests and diseases. The device can integrate the device for monitoring the microclimate within a crop canopy and predicting the occurrence of pests and diseases provided by an embodiment of the present invention. Figure 3 The schematic diagram of the structure of a device for monitoring microclimate in the crop canopy and predicting the occurrence of pests and diseases provided by the present invention is shown. Figure 3The device for monitoring microclimate within the crop canopy and predicting pest and disease occurrences includes one or more processors 31, an output device 32, an input device 33, and a memory 34. The memory 34 is used to store one or more programs; when the one or more programs are executed by the one or more processors 31, the one or more processors 31 can implement the method for monitoring microclimate within the crop canopy and predicting pest and disease occurrences as provided in the above embodiment. The processor 31, the output device 32, and the input device 33 can be connected via a bus or other means. Figure 3 The bus connection is shown. The crop canopy microclimate monitoring and pest and disease occurrence prediction device can be a user terminal (such as a mobile phone or tablet) or a remote control terminal, capable of interacting with unmanned equipment. After generating prediction results, the device can transmit these results as messages and / or instructions to other execution devices for operation.
[0033] Memory 34, as a readable storage medium for a computing device, can be used to store data, software programs, computer-executable programs, and modules, such as program instructions and module data corresponding to the crop canopy microclimate monitoring and pest and disease occurrence prediction methods provided in any embodiment of the present invention (e.g., data acquisition module 21, geolocation module 22, data storage module 23, data transmission module 24, and processing and control module 25). Memory 34 may primarily include a data storage area and a program storage area. The data storage area can store intermediate data generated during device use and transmitted / received data, while the program storage area can store the operating system and application programs required to implement device functions. Furthermore, memory 34 may include high-speed random access memory and / or non-volatile memory, and may further include memory located remotely from processor 31; such remote memory may be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
[0034] The above-mentioned devices, equipment and computers for monitoring microclimate within crop canopies and predicting the occurrence of pests and diseases can be used to execute the methods for monitoring microclimate within crop canopies and predicting the occurrence of pests and diseases provided in any of the above-mentioned embodiments, and have corresponding functions and beneficial effects.
[0035] An embodiment of the present invention also provides a storage medium storing computer-executable instructions. When the computer-executable instructions are executed by a computer processor, they are used to execute the method for microclimate monitoring and pest and disease occurrence prediction within the crop canopy as provided in the above embodiment. The method includes: synchronously acquiring time series microclimate data and time series pest and disease occurrence data within the crop canopy; establishing a database based on the synchronized time series microclimate data and time series pest and disease occurrence data within the crop canopy; constructing a pest and disease occurrence prediction model and an identification detection model based on the database; and predicting the type, degree and probability of pest and disease occurrence within the crop canopy based on the pest and disease occurrence prediction model and the identification detection model.
[0036] Storage media refers to any of various types of storage devices. The term "storage media" is intended to include installation media, computer system memory, random access memory, non-volatile memory, registers, or other similar functional types of memory elements. Storage media may include other types of memory or a combination thereof. Storage media may store instruction programs, computer programs, and the like that are executed by one or more processors.
[0037] Of course, the computer executable instructions of a storage medium storing computer executable instructions provided by an embodiment of the present invention are not limited to the method provided by the present invention, and can also execute related operations in the method provided by any embodiment of the present invention.
[0038] The apparatus, device, and storage medium for monitoring microclimate within the crop canopy and predicting pest and disease outbreaks provided in the above embodiments can implement the methods for monitoring microclimate within the crop canopy and predicting pest and disease outbreaks provided in any embodiment of the present invention. For technical details not fully described in the above embodiments, please refer to the method descriptions provided in any embodiment of the present invention.
[0039] The above are merely preferred embodiments of the present invention and the technical principles employed. The present invention is not limited to the specific embodiments provided. Any obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the claims.
Claims
1. A method for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases, characterized in that: include: Synchronously obtain time series microclimate data and time series pest and disease occurrence data within the crop canopy; Establish a database based on synchronized time-series microclimate data within the crop canopy and time-series pest and disease occurrence data; Construct pest and disease occurrence prediction models and identification and detection models based on the database; Based on the pest and disease occurrence prediction model and identification detection model, the type, degree and probability of pest and disease occurrence in the crop canopy are predicted.
2. The method for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases according to claim 1, characterized in that: The synchronous acquisition of time series microclimate data and time series pest and disease occurrence data within the crop canopy includes: synchronously acquiring time series microclimate data (temperature, humidity, wind speed, wind direction, air pressure, light radiation intensity value) and time series pest and disease occurrence data (intra-canopy images) with year, month, day, hour, minute, second, longitude, and altitude information tags within the crop canopy determined to be sampled, and storing and transmitting the data; The method of establishing a database based on synchronized time-series microclimate data and time-series pest and disease occurrence data within the crop canopy includes: cleaning and labeling the stored and / or transmitted time-series microclimate data within the crop canopy, removing information labels and / or data samples with incomplete data, adding pest and disease occurrence information labels, and arranging the data in chronological order according to a format in which the information labels and values strictly correspond to each other to form a time-series microclimate database; cleaning and labeling the stored and / or transmitted time-series pest and disease occurrence data, removing information labels and / or data samples with incomplete data, adding pest and disease occurrence information labels, and arranging the data in chronological order according to a format in which the information labels and the pest and disease occurrence data strictly correspond to each other to form a time-series pest and disease occurrence database; The method of constructing a pest and disease occurrence prediction model and an identification and detection model based on a database includes: creating a sample set for training and verifying a machine learning algorithm model using the crop canopy time series microclimate data and the time series pest and disease occurrence data with pest and disease occurrence information labels based on a time series crop canopy microclimate database and a time series pest and disease occurrence database, and using the sample set to train, verify, and debug the machine learning algorithm model to construct the pest and disease occurrence prediction model and the identification and detection model; The method of predicting the type, degree and probability of occurrence of pests and diseases in the crop canopy based on the pest and disease occurrence prediction model and the identification and detection model includes: using the constructed pest and disease occurrence prediction model and the identification and detection model to predict the time series microclimate data and pest and disease occurrence data in the crop canopy that are currently collected, stored and / or transmitted, and outputting the prediction results of the type, degree and probability of occurrence of pests and diseases with information labels of year, month, day, hour, minute, second, longitude and latitude, and altitude.
3. The present invention provides a device for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases, comprising at least one set of a data acquisition module, a geographic positioning module, a data storage module, a data transmission module, a processing and control module, a power supply module, and an installation and fixing module, wherein: The data acquisition module includes at least one temperature, humidity, wind speed, wind direction, air pressure, light radiation intensity and camera sensor; the temperature, humidity, wind speed, wind direction, air pressure, light radiation intensity sensor is used to obtain the time series microclimate data; the camera sensor is used to obtain time series pest and disease occurrence data; The geolocation module is used to obtain geolocation data for the location of the microclimate monitoring and pest and disease occurrence prediction area within the crop canopy, including year, month, day, hour, minute, second, longitude and latitude, and altitude information tag data; The data storage module is used for storing data, programs and instructions; The data transmission module is used for wired and / or wireless transmission of data, programs and instructions between different devices and storage media; The processing control module is used to control the program execution and data processing of the normal operation of the data acquisition module, geographic positioning module, data storage module, data transmission module, and power supply module; The power supply module is used to ensure the normal operation of the data acquisition module, geographic positioning module, data storage module, data transmission module and processing control module; The installation and fixing module is used for installing the data acquisition module, data storage module, data transmission module, processing control module, power supply module and fixing the microclimate monitoring and pest occurrence prediction device in the crop canopy at any position inside the crop canopy.
4. A device for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases, characterized in that: include: memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases as described in any one of claims 1-2.
5. A storage medium storing computer-executable instructions, characterized in that: When executed by a computer processor, the computer executable instructions are used to execute the method for monitoring microclimate within the crop canopy and predicting the occurrence of pests and diseases as described in any one of claims 1-2.