Photovoltaic area pasture disease and pest monitoring method, device and system and readable storage medium

Abstract

The invention provides a photovoltaic region pasture disease and insect pest monitoring method, device and system and a readable storage medium, and belongs to the technical field of disease and insect pest monitoring. The method comprises the following steps: acquiring an infrared image and an environment temperature value of pasture in a photovoltaic region; determining an infrared radiation intensity discrimination interval based on the environment temperature value; determining an area initial number of areas in an infrared radiation intensity preset interval in the infrared image and an area corresponding to each area; based on the area corresponding to each region, correcting the initial number of the regions to obtain a region discrimination number; and performing graded early warning based on the area discrimination number. The monitoring method provided by the invention is simple and accurate in identification, and can realize timely and accurate monitoring and early warning of pasture diseases and insect pests in the photovoltaic area.

Description

Technical Field

[0001] The present invention relates to the technical field of pest and disease monitoring, and particularly to a method for monitoring pests and diseases of forage grass in a photovoltaic area, a device for monitoring pests and diseases of forage grass in a photovoltaic area, a system for monitoring pests and diseases of forage grass in a photovoltaic area, and a readable storage medium. Background Art

[0002] With the gradual increase in the number of photovoltaic panels arranged, a relatively large number of photovoltaic areas with a wide range have been formed. In order to achieve the sustainable development of resources in the photovoltaic area, crops such as forage grass are usually planted in the photovoltaic area, which not only plays a role in sand prevention and fixation, but also can bring certain economic benefits.

[0003] Forage grass will be affected by pests and diseases during the growth process. When there are pests and diseases in a certain area, if no targeted treatment is carried out in time, it will cause irreparable damage to the entire forage grass. Therefore, it is necessary to monitor pests and diseases in time. However, due to the solar panels blocking the ground surface, it is impossible to monitor pests and diseases by conventional means. Therefore, in the prior art, the method of manual regular inspection is usually adopted to judge the pest and disease status of forage grass, which has problems such as low monitoring efficiency, waste of manpower and material resources, and easy omission of monitoring. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a method, device, system and readable storage medium for monitoring pests and diseases of forage grass in a photovoltaic area, so as to solve the problems of low monitoring efficiency, waste of manpower and material resources, and easy omission of monitoring caused by the method of manual regular inspection to judge the pest and disease status of forage grass.

[0005] In order to achieve the above purpose, the embodiments of the present invention provide a method for monitoring pests and diseases of forage grass in a photovoltaic area, and the method includes:

[0006] Obtain the infrared image and environmental temperature value of the forage grass in the photovoltaic area;

[0007] Based on the environmental temperature value, determine the infrared radiation intensity discrimination interval;

[0008] Determine the initial number of regions in the infrared image that are in the preset infrared radiation intensity interval and the area corresponding to each region;

[0009] Based on the area corresponding to each region, correct the initial number of regions to obtain the discrimination number of regions;

[0010] Based on the discrimination number of regions, conduct hierarchical early warning on pests and diseases of forage grass in the photovoltaic area.

[0011] Optionally, the infrared image is a processed image, and the method further includes: performing denoising processing on the obtained infrared image by using adaptive median filtering and gradient domain guided filtering.

[0012] Optionally, determining an infrared radiation intensity discrimination interval based on the ambient temperature value includes:

[0013] Based on the ambient temperature value, obtaining an infrared radiation intensity discrimination interval by using a preset table or a preset curve; the preset table or the preset curve is used to represent the corresponding relationship between different ambient temperature values and different infrared radiation intensity discrimination intervals.

[0014] Optionally, determining an initial number of regions in the infrared image that are within a preset interval of infrared radiation intensity and the area corresponding to each region includes:

[0015] Inputting the infrared image into an image discrimination model to obtain the initial number of regions in the preset interval of infrared radiation intensity and the area corresponding to each region; the image discrimination model is obtained by training an improved target monitoring network and a deep neural network with a training dataset, and the training dataset includes multiple historical infrared images with different types of pests and diseases that have been annotated.

[0016] Optionally, the improved target monitoring network is obtained in the following manner:

[0017] Based on the YOLOv5 network, replacing the Conv in the original BackBone with GSConv, adding an attention mechanism to the BackBone, inputting the feature layer into the BiFPN structure, adding a Detect module to the downsampling module, and adding a monitoring head at the same time.

[0018] Optionally, based on the area corresponding to each region, correcting the initial number of regions to obtain a discriminated number of regions includes:

[0019] Eliminating regions with an area greater than or equal to a set area threshold, and taking the number of regions with an area less than the set area threshold as the discriminated number of regions.

[0020] Optionally, based on the discriminated number of regions, performing a hierarchical early warning for pests and diseases in the photovoltaic area pasture includes:

[0021] If the discriminated number of regions is less than or equal to a first judgment threshold, output a low-risk early warning;

[0022] If the discriminated number of regions is greater than the first judgment threshold and less than a second judgment threshold, output a medium-risk early warning;

[0023] If the discriminated number of regions is greater than or equal to the second judgment threshold and less than a third judgment threshold, output a high-risk early warning;

[0024] If the discriminated number of regions is greater than or equal to the third judgment threshold, output an ultra-high-risk early warning;

[0025] Among them, the first judgment threshold is less than the second judgment threshold, and the second judgment threshold is less than the third judgment threshold.

[0026] In a second aspect, an embodiment of the present invention further provides a monitoring device for pests and diseases of pasture in a photovoltaic area. The device includes:

[0027] A data acquisition module, configured to acquire an infrared image and an environmental temperature value of the pasture in the photovoltaic area;

[0028] A discrimination interval determination module, configured to determine an infrared radiation intensity discrimination interval based on the environmental temperature value;

[0029] An output module, configured to determine an initial number of regions in the infrared image that are in a preset interval of infrared radiation intensity and the area corresponding to each region;

[0030] A correction module, configured to correct the initial number of regions based on the area corresponding to each region to obtain a discriminated number of regions;

[0031] An early warning module, configured to perform hierarchical early warning on pests and diseases of the pasture in the photovoltaic area based on the discriminated number of regions.

[0032] In a third aspect, an embodiment of the present invention further provides a monitoring system for pests and diseases of pasture in a photovoltaic area. A plurality of photovoltaic supports are arranged in the photovoltaic area, and photovoltaic panels are arranged on the photovoltaic supports. The system includes:

[0033] A temperature sensor, arranged on the photovoltaic support, for acquiring the environmental temperature value of the photovoltaic area;

[0034] At least one image capturing mechanism, each image capturing mechanism is respectively arranged on the photovoltaic support through a pan-tilt head, and the pan-tilt head is used to adjust the shooting angle of the image capturing mechanism;

[0035] The above-mentioned monitoring device for pests and diseases of pasture in a photovoltaic area is connected to the temperature sensor and the image capturing mechanism;

[0036] A display and alarm mechanism, connected to the monitoring device for pests and diseases of pasture in a photovoltaic area, for displaying early warning information and generating an audible and visual alarm.

[0037] In a fourth aspect, an embodiment of the present invention further provides a readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the above-mentioned method for monitoring pests and diseases of pasture in a photovoltaic area.

[0038] According to the ambient temperature value, this technical solution determines the discrimination interval of infrared radiation intensity; then determines the initial number of regions in the infrared image that are within the preset interval of infrared radiation intensity and the area corresponding to each region; and based on the area corresponding to each region, corrects the initial number of regions to obtain the discrimination number of regions; finally, according to the discrimination number of regions, conducts pest and disease grading early warning. The monitoring method is simple, reduces manpower and material resources, and has accurate recognition, enabling timely and accurate monitoring and early warning of pests and diseases in the forage grass in the photovoltaic area, so as to intervene in time and reduce the impact of pests and diseases on the forage grass.

[0039] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0041] Figure 1 is a flowchart of the method for monitoring pests and diseases of forage grass in the photovoltaic area provided by the present invention;

[0042] Figure 2 is a schematic structural diagram of the device for monitoring pests and diseases of forage grass in the photovoltaic area provided by the present invention;

[0043] Figure 3 is a schematic structural diagram of the system for monitoring pests and diseases of forage grass in the photovoltaic area provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following details the specific implementation of the embodiments of the present invention in conjunction with the drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiments of the present invention, and does not limit the embodiments of the present invention.

[0045] In the embodiments of the present invention, unless otherwise stated, the orientation words such as "upper, lower, left, right" generally refer to the orientation or positional relationship based on the drawings, or the orientation or positional relationship in which the invention product is usually placed during use.

[0046] The terms "first", "second", "third", etc. are only used for distinguishing descriptions, and cannot be understood as indicating or implying relative importance.

[0047] The terms "parallel", "perpendicular", etc. do not mean that the components are required to be absolutely parallel or perpendicular, but can be slightly inclined. For example, "parallel" only means that its direction is more parallel relative to "perpendicular", and does not mean that the structure must be completely parallel, but can be slightly inclined.

[0048] Terms such as "horizontal", "vertical", "hanging" do not require the components to be absolutely horizontal, vertical or hanging, but can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0049] In addition, terms such as "substantially" and "basically" are intended to indicate that the relevant content does not require absolute precision, but can have a certain deviation. For example, "substantially equal" does not only mean absolute equality. Since it is difficult to achieve absolute "equality" in the actual production and operation process, there is generally a certain deviation. Therefore, in addition to absolute equality, "substantially equal" also includes the above-mentioned situations with a certain deviation. Taking this as an example, in other cases, unless otherwise specified, terms such as "substantially" and "basically" have similar meanings to the above.

[0050] In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "installed", "connected", "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected, or indirectly connected through an intermediate medium, and can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0051] Figure 1 is a flowchart of the method for monitoring pests and diseases of forage grass in the photovoltaic area provided by the present invention; Figure 2 is a schematic structural diagram of the device for monitoring pests and diseases of forage grass in the photovoltaic area provided by the present invention; Figure 3 is a schematic structural diagram of the system for monitoring pests and diseases of forage grass in the photovoltaic area provided by the present invention.

[0052] As Figure 1 shown, this embodiment provides a method for monitoring pests and diseases of forage grass in the photovoltaic area, and the method includes:

[0053] Step 1, obtain the infrared image and environmental temperature value of the forage grass in the photovoltaic area;

[0054] Step 2, determine the infrared radiation intensity discrimination interval based on the environmental temperature value;

[0055] Step 3, determine the initial number of regions in the infrared image that are in the preset infrared radiation intensity interval and the area corresponding to each region;

[0056] Step 4, correct the initial number of regions based on the area corresponding to each region to obtain the discriminated number of regions;

[0057] Step 5, perform hierarchical early warning on the pests and diseases of the forage grass in the photovoltaic area based on the discriminated number of regions.

[0058] Specifically, in this embodiment, due to the different physiological structures of plants and insects, although most insects are cold-blooded animals, the body temperatures of both insects and forage grasses will change to a certain extent with the change of environmental temperature. As a result, the infrared radiation intensities emitted by insects and forage grasses will be different. Generally speaking, the infrared radiation intensity generated by insects is greater than that of forage grasses. The color of the infrared image of insects is darker, and the infrared image of plants is lighter. Therefore, the insects and forage grasses can be distinguished by the magnitude of the infrared radiation intensity. Therefore, in this solution, in order to achieve accurate identification through the infrared radiation intensity, while obtaining the infrared image of the forage grass in the photovoltaic area, the environmental temperature value is obtained, and based on the environmental temperature value, the infrared radiation intensity discrimination interval at the current environmental temperature is determined; finally, the initial number of regions in the processed image that are in the preset interval of infrared radiation intensity and the area corresponding to each region are determined; and since there may be some reptiles other than insects in the identified regions that cause identification errors, it is necessary to correct the initial number of regions based on the area corresponding to each region to obtain the region discrimination number; finally, based on the region discrimination number, a hierarchical early warning is carried out.

[0059] Through the above method, the monitoring method is simple, reducing manpower and material resources, and the identification is accurate, enabling timely and accurate monitoring and early warning of the pests and diseases of the forage grass in the photovoltaic area, so as to intervene in time and reduce the impact of pests and diseases on the forage grass.

[0060] In another embodiment, performing a hierarchical early warning based on the region discrimination number can also be replaced by: performing a hierarchical early warning on the pests and diseases of the forage grass in the photovoltaic area based on the total area of the regions corresponding to the region discrimination number.

[0061] In another embodiment, a small automatic weather station is installed in the photovoltaic area to monitor climate environmental conditions such as air temperature, humidity, wind speed, wind direction, solar radiation, and rainfall. The small automatic weather station has a remote transmission function and can obtain meteorological information in real time. During the monitoring process, the meteorological information when pests and diseases occur is compared. If the meteorological information also conforms to the climate conditions when insects are likely to occur, it can be judged whether pests and diseases have occurred.

[0062] Further, the infrared image is a processed image, and the method further includes: performing denoising processing on the obtained infrared image by using adaptive median filtering and gradient-domain guided filtering.

[0063] Specifically, in this embodiment, after obtaining the infrared image, in order to ensure the quality of the image and reduce the influence of noise on subsequent identification of insects and the like, the image is denoised. Adaptive median filtering can remove the preset impulse noise in the remote sensing image. At the same time, gradient-domain guided filtering can retain the edge information of the image, achieving both ensuring the quality of the image and having a good denoising effect to ensure the subsequent identification accuracy.

[0064] Further, based on the environmental temperature value, an infrared radiation intensity discrimination interval is determined, including:

[0065] Based on the environmental temperature value, using a preset table or a preset curve, an infrared radiation intensity discrimination interval is obtained; the preset table or the preset curve is used to represent the corresponding relationship between different environmental temperature values and different infrared radiation intensity discrimination intervals.

[0066] Specifically, in this embodiment, since the self-temperatures of insects and forages will both change to a certain extent with the change of the environmental temperature, which will cause changes in the infrared radiation intensities emitted by insects and forages. Therefore, by obtaining the infrared radiation intensities between different insects and different forages at different temperatures and performing data fitting analysis, a preset table or a preset curve is finally obtained, thereby representing the corresponding relationship between different environmental temperature values and different infrared radiation intensity discrimination intervals.

[0067] Further, determining the initial number of regions in the infrared image that are in the preset interval of infrared radiation intensity and the area corresponding to each region includes:

[0068] Inputting the infrared image into an image discrimination model to obtain the initial number of regions in the preset interval of infrared radiation intensity and the area corresponding to each region; the image discrimination model is obtained by training an improved target monitoring network and a deep neural network with a training data set, and the training data set includes multiple historical infrared images containing different types of pests and diseases that have been labeled.

[0069] Specifically, in this embodiment, an image discrimination model is obtained by training an improved target monitoring network and a deep neural network with a training data set; among them, the improved target monitoring network is used to realize the identification and division of regions in the preset interval of infrared radiation intensity. After identifying and dividing the regions in the preset interval of infrared radiation intensity, the deep neural network is then used for counting and calculating the area of each region. Through the combination of the improved target monitoring network and the deep neural network, the initial number of regions in the preset interval of infrared radiation intensity and the area corresponding to each region can be quickly and accurately identified.

[0070] Among them, the training data set contains multiple infrared images of forage grass with different insects (different types of pests and diseases) in different growth cycles. And through field investigations, interviews, literature reviews, etc., relevant information such as the main types of pests and diseases in the local area and the climate environment during the occurrence of pests and diseases is fully understood. Then, pest and disease entity samples are collected, photographed indoors with an infrared imaging device to obtain infrared images, and an infrared image database for each pest and disease is established to form the training data set.

[0071] Furthermore, the improved target monitoring network is obtained through the following method:

[0072] Based on the YOLOv5 network, replace the Conv in the original BackBone with GSConv, add an attention mechanism to the BackBone, input the feature layer into the BiFPN structure, and add a Detect module to the downsampling module, while adding a monitoring head.

[0073] Specifically, in this embodiment, during the process of building the model, the classic single-stage target monitoring network YOLOv5 is optimized. Replace the Conv in the original BackBone with the more lightweight GSConv, which can reduce the model parameters while making the regional feature extraction more effective. At the same time, introduce an attention mechanism into the BackBone to fuse the feature layers of different scales through channel fusion. Input the fused feature layer into the BiFPN structure, and add a Detect module during the subsequent downsampling process, while adding a monitoring head to prevent the infrared target from being lost during the downsampling process, so as to detect insect targets of different sizes and improve the accuracy of monitoring.

[0074] Furthermore, based on the area corresponding to each region, the initial number of regions is corrected to obtain the region discrimination number, including:

[0075] Eliminate the regions with an area greater than or equal to the set area threshold, and use the number of regions with an area less than the set area threshold as the region discrimination number.

[0076] Specifically, in this embodiment, there may be some recognition errors caused by reptiles other than insects in the identified regions. Therefore, the regions with an area greater than or equal to the set area threshold are eliminated, and the number of regions with an area less than the set area threshold is used as the region discrimination number for subsequent monitoring and early warning.

[0077] Furthermore, based on the region discrimination number, a hierarchical early warning for pests and diseases of forage grass in the photovoltaic area is carried out, including:

[0078] If the region discrimination number is less than or equal to the first judgment threshold, a low-risk early warning is output;

[0079] If the number of region discriminations is greater than the first judgment threshold and less than the second judgment threshold, a medium-risk warning is output;

[0080] If the number of region discriminations is greater than or equal to the second judgment threshold and less than the third judgment threshold, a high-risk warning is output;

[0081] If the number of region discriminations is greater than or equal to the third judgment threshold, an ultra-high-risk warning is output;

[0082] Among them, the first judgment threshold is less than the second judgment threshold, and the second judgment threshold is less than the third judgment threshold.

[0083] Specifically, in this embodiment, the more the number of finally obtained region discriminations, the more the number of insects with the infrared radiation intensity in the preset infrared radiation intensity interval on the forage grass. Therefore, corresponding thresholds can be set, and subsequent monitoring and warning can be carried out according to the number of region discriminations. If the number of region discriminations is less than or equal to the first judgment threshold, it means that the number of insects at this time is small, and a low-risk warning is output, and other treatment measures can be temporarily not taken; if the number of region discriminations is greater than the first judgment threshold and less than the second judgment threshold, it means that the number of insects at this time has reached a certain amount and has caused a certain impact on the forage grass, and timely intervention is required, and a medium-risk warning is output to decide whether to take treatment measures such as spraying pesticides according to the actual situation; if the number of region discriminations is greater than or equal to the second judgment threshold and less than the third judgment threshold, it means that the number of insects at this time is large and the impact on the forage grass is very large, and intervention must be carried out, such as spraying pesticides, and a high-risk warning is output; if the number of region discriminations is greater than or equal to the third judgment threshold, an ultra-high-risk warning is output, indicating that the number of insects at this time is very large and the impact on the forage grass is very large, and intervention must be carried out and the dosage of pesticide spraying is increased.

[0084] In another embodiment, the hierarchical warning based on the number of region discriminations can also be replaced by: hierarchical warning based on the total area of the region corresponding to the number of region discriminations. The pest and disease levels are divided into 4 levels according to the severity, namely level 1, level 2, level 3, and level 4, corresponding to low-risk warning, medium-risk warning, high-risk warning, and ultra-high-risk warning respectively. The level division is based on the area of pests and diseases in the infrared image. Among them, when the pest and disease area is less than or equal to 5% (the first area threshold), it is level 1, when it is in the range of 5%-25% (greater than the first area threshold and less than the second area threshold), it is level 2, when it is in the range of 25%-40% (greater than or equal to the second area threshold and less than the third judgment threshold), it is level 3, and when it is greater than or equal to 40% (the third area threshold), it is level 4.

[0085] Such as Figure 2As shown in the figure, this embodiment also provides a monitoring device for pests and diseases of pasture in a photovoltaic area. The device includes:

[0086] A data acquisition module, configured to acquire infrared images and ambient temperature values of pasture in the photovoltaic area;

[0087] A discrimination interval determination module, configured to determine an infrared radiation intensity discrimination interval based on the ambient temperature value;

[0088] An output module, configured to determine the initial number of regions in the infrared image that are within a preset interval of infrared radiation intensity and the area corresponding to each region;

[0089] A correction module, configured to correct the initial number of regions based on the area corresponding to each region to obtain a discriminated number of regions;

[0090] An early warning module, configured to perform hierarchical early warning on pests and diseases of pasture in the photovoltaic area based on the discriminated number of regions.

[0091] Specifically, in this embodiment, the monitoring method of the monitoring device for pests and diseases of pasture in the photovoltaic area is simple, reducing manpower and material resources, and the identification is accurate. It can realize timely and accurate monitoring and early warning of pests and diseases of pasture in the photovoltaic area, so as to intervene in time and reduce the impact of pests and diseases on the pasture.

[0092] As Figure 3 shown in the figure, this embodiment also provides a monitoring system for pests and diseases of pasture in a photovoltaic area. There are multiple photovoltaic supports arranged in the photovoltaic area, and photovoltaic panels are arranged on the photovoltaic supports. The system includes:

[0093] A temperature sensor, arranged on the photovoltaic support, configured to acquire the ambient temperature value of the photovoltaic area;

[0094] At least one image capturing mechanism, each image capturing mechanism is respectively arranged on the photovoltaic support through a pan-tilt head, and the pan-tilt head is used to adjust the shooting angle of the image capturing mechanism;

[0095] The above-mentioned monitoring device for pests and diseases of pasture in the photovoltaic area is connected to the temperature sensor and the image capturing mechanism;

[0096] A display and alarm mechanism, connected to the monitoring device for pests and diseases of pasture in the photovoltaic area, configured to display the early warning information and generate a sound and light alarm.

[0097] Specifically, in this embodiment, both the temperature sensor and the image capturing mechanism can be powered by a photovoltaic panel. Multiple temperature sensors can be set, and the values of the multiple temperature sensors are averaged to obtain the ambient temperature value for the final judgment. The installation position of the image capturing mechanism is below the solar panel, enabling it to fully monitor the growth of vegetation. The image capturing mechanism uses a variable pan-tilt head, and the lens can rotate automatically to perform a 360° scan and take pictures automatically; the lens of the image capturing mechanism is set with 3 photographing positions, and the photographing time interval is 1 hour; after the setting is completed, the lens obtains the infrared images of the vegetation at 3 positions in turn every 1 hour, and the monitoring period is 4 months. According to the obtained infrared images of the vegetation in different growth cycles, a plant infrared image database can be established. The display and alarm mechanism is connected to the forage grass pest and disease monitoring device in the photovoltaic area, including a display screen and an audible and visual alarm. The display screen is used to display the warning information, and the audible and visual alarm is used to generate an audible and visual alarm for prompting. The alarm device displays different colors according to the pest and disease levels. Red corresponds to level 1, orange corresponds to level 2, yellow corresponds to level 3, and blue corresponds to level 4. According to the different colors of the alarm device, the pest and disease levels can be judged in time, which is convenient for quickly formulating pest and disease control plans.

[0098] This embodiment also provides a readable storage medium, on which instructions are stored, and these instructions are used to make a machine execute the above-mentioned forage grass pest and disease monitoring method in the photovoltaic area.

[0099] The optional implementation manners of the embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above implementation manners. Within the technical concept scope of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all belong to the protection scope of the embodiments of the present invention.

[0100] Those skilled in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program is stored in a storage medium, including several instructions for making a single-chip microcomputer, a chip or a processor execute all or part of the steps of the methods described in the embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc that can store program codes.

[0101] The optional embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without conflict. To avoid unnecessary repetition, the embodiments of the present invention will not separately describe various possible combination methods.

[0102] In addition, any combination can be made between various different embodiments of the embodiments of the present invention, as long as it does not violate the idea of the embodiments of the present invention, and it should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for monitoring pasture pests and diseases in photovoltaic areas, characterized in that: The method comprises: Obtain infrared images and ambient temperature values ​​of pasture in photovoltaic areas; Based on the ambient temperature value, determining an infrared radiation intensity discrimination interval; Determine the initial number of regions in the infrared image that are within a preset range of infrared radiation intensity and the area corresponding to each region; Based on the area corresponding to each region, the initial number of regions is corrected to obtain the number of region discriminations; Based on the regional identification quantity, graded warnings are issued for forage pests and diseases in the photovoltaic area.

2. The photovoltaic area pasture pest monitoring method according to claim 1 is characterized in that: The infrared image is a processed image, and the method further comprises: performing denoising processing on the acquired infrared image by using adaptive median filtering and gradient domain guided filtering.

3. The photovoltaic area pasture pest monitoring method according to claim 1 is characterized in that: Based on the ambient temperature value, determining the infrared radiation intensity discrimination interval includes: Based on the ambient temperature value, the infrared radiation intensity discrimination interval is obtained using a preset table or a preset curve; the preset table or the preset curve is used to characterize the corresponding relationship between different ambient temperature values ​​and different infrared radiation intensity discrimination intervals.

4. The photovoltaic area pasture pest monitoring method according to claim 1, characterized in that: Determining the initial number of regions in the infrared image that are within a preset range of infrared radiation intensity and the area corresponding to each region includes: The infrared image is input into an image discrimination model to obtain the initial number of areas in a preset range of infrared radiation intensity and the area corresponding to each area; the image discrimination model is obtained by training an improved target monitoring network and a deep neural network through a training data set, and the training data set includes multiple annotated historical infrared images containing different types of pests and diseases.

5. The photovoltaic area pasture pest monitoring method according to claim 4 is characterized in that: The improved target monitoring network is obtained by: Based on the YOLOv5 network, the Conv in the original BackBone is replaced with GSConv, the attention mechanism is added to BackBone, the feature layer is input into the BiFPN structure, and the Detect module is added to the downsampling module, and a monitoring head is added.

6. The photovoltaic area pasture pest monitoring method according to claim 1, characterized in that: Based on the area corresponding to each region, the initial number of regions is corrected to obtain the number of region discriminations, including: Regions whose areas are greater than or equal to the set area threshold are eliminated, and the number of regions whose areas are less than the set area threshold is used as the region determination number.

7. The photovoltaic area pasture pest monitoring method according to claim 1, characterized in that: Based on the regional identification quantity, graded warnings are given for forage pests and diseases in the photovoltaic area, including: If the area identification number is less than or equal to the first judgment threshold, a low risk warning is output; If the area discrimination quantity is greater than the first judgment threshold and less than the second judgment threshold, a medium risk warning is output; If the area discrimination quantity is greater than or equal to the second judgment threshold and less than the third judgment threshold, a high-risk warning is output; If the area discrimination number is greater than or equal to the third judgment threshold, an ultra-high risk warning is output; The first judgment threshold is smaller than the second judgment threshold, and the second judgment threshold is smaller than the third judgment threshold.

8. A photovoltaic area pasture pest monitoring device, characterized in that: The device comprises: A data acquisition module is used to obtain infrared images and ambient temperature values ​​of pastures in the photovoltaic area; A discrimination interval determination module, used to determine the infrared radiation intensity discrimination interval based on the ambient temperature value; An output module, used to determine the initial number of regions in the infrared image that are within a preset range of infrared radiation intensity and the area corresponding to each region; A correction module, used for correcting the initial number of regions based on the area corresponding to each region to obtain the number of region discriminations; The early warning module is used to provide graded early warning for pasture pests and diseases in the photovoltaic area based on the identification quantity of the area.

9. A photovoltaic area pasture pest monitoring system, wherein a plurality of photovoltaic brackets are arranged in the photovoltaic area, and photovoltaic panels are arranged on the photovoltaic brackets, characterized in that: The system comprises: A temperature sensor is arranged on the photovoltaic support and is used to obtain the ambient temperature value of the photovoltaic area; At least one image capturing mechanism, each image capturing mechanism is respectively arranged on the photovoltaic support through a pan-tilt platform, and the pan-tilt platform is used to adjust the shooting angle of the image capturing mechanism; The photovoltaic area pasture pest monitoring device according to claim 8 is connected to the temperature sensor and the image capture mechanism; The display alarm mechanism is connected to the photovoltaic area pasture pest and disease monitoring device and is used to display the early warning information and generate sound and light alarms.

10. A readable storage medium, characterized in that: The readable storage medium stores instructions for enabling a machine to execute the photovoltaic area pasture pest monitoring method according to any one of claims 1 to 7.

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