Potato green spot monitoring method, system, equipment and medium

Abstract

The invention relates to a potato green spot disease monitoring method, system and device and a medium, and the method comprises the steps: firstly, determining a target area of which the ridging thickness does not meet the standard thickness through obtaining the furrow form of a planting area in advance; a target area with insufficient hilling thickness can be accurately screened out, the monitoring range is reduced, and a high-risk area is focused. Secondly, green color blocks in the target area are recognized, and target green color blocks with the areas of the green color blocks meeting a preset area threshold value are obtained; finally, interference of scattered green color blocks is eliminated through green color block area threshold value screening, and a target green area is locked. And based on the vein features in the target green color block, determining a potato green spot disease monitoring result through a pre-training detection model. Interferents such as potato green spots and fallen leaves are distinguished through the pre-training detection model. And the monitoring accuracy of the potato green spot disease is obviously improved. The problem that a traditional potato green spot disease monitoring method is low in monitoring accuracy in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of plant monitoring technology, and in particular to a method, system, equipment and medium for monitoring potato green spot disease. Background Art

[0002] During potato growth, the tubers (i.e., potatoes) typically need to be well covered with soil to protect them from sunlight. If the soil is not sufficiently covered, the exposed tubers can develop green spots (green spot) in response to sunlight, accompanied by the accumulation of solanine. Solanine is a naturally occurring toxin that is harmful to human health, necessitating monitoring of potatoes for green spot.

[0003] Currently, monitoring of potato green spot disease mainly relies on manual inspections, that is, visually observing the growth of potatoes in the field. However, manual inspections are difficult to accurately identify the greening phenomenon in potatoes.

[0004] Therefore, the traditional potato green spot disease monitoring method has the problem of low monitoring accuracy. Summary of the Invention

[0005] The embodiments of the present application provide a potato green spot disease monitoring method, system, device and medium to at least solve the problem of low monitoring accuracy in traditional potato green spot disease monitoring methods in the related art.

[0006] In a first aspect, an embodiment of the present application provides a method for monitoring potato green spot disease, the method comprising:

[0007] By obtaining the ridge and furrow morphology of the planting area in advance, the target area where the soil thickness does not meet the standard thickness is determined;

[0008] Identifying a green color block in the target area, and obtaining a target green color block whose area meets a preset area threshold;

[0009] Based on the leaf vein features in the target green color block, the potato green spot disease monitoring results are determined through a pre-trained detection model.

[0010] In one embodiment, the step of determining the target area where the soil thickness does not meet the standard thickness by pre-acquiring the ridge and furrow morphology of the planting area includes:

[0011] Obtaining overhead and oblique images of the planting area;

[0012] measuring the furrow spacing based on the top view image;

[0013] Calculating the actual height of the ridge based on the oblique image and the ridge furrow spacing;

[0014] The actual height is compared with a preset threshold, and the area below the preset threshold is marked as a target area.

[0015] In one embodiment, the calculating the actual height of the ridge based on the oblique image includes:

[0016] The actual height is calculated through geometric relationships based on the pixel height, width, shooting angle and ridge spacing of the ridges in the oblique view.

[0017] In one embodiment, identifying the green block in the target area and obtaining the target green block whose area meets a preset area threshold includes:

[0018] Identify pixels in the RGB image of the target area whose G value is greater than the B value and the R value;

[0019] Comparing the area of ​​the green color block formed by the adjacent pixel points with a preset area threshold;

[0020] A target green color block whose area is greater than or equal to the preset area threshold is obtained.

[0021] In one embodiment, determining the potato green spot disease monitoring result by using a pre-trained detection model based on the leaf vein feature in the green block recognition result includes:

[0022] Detecting leaf vein features in the target green block using a pre-trained detection model;

[0023] If the leaf vein feature is detected in the target green color block, the target green color block is eliminated; if the leaf vein feature is not detected in the target green color block, the target green color block is determined to be potato green spot disease.

[0024] In one embodiment, after determining the potato green spot disease monitoring result using the pre-trained detection model, the method further includes:

[0025] The soil temperature and soil particle size information in the target area are obtained, and when the soil temperature and soil particle size exceed a standard preset range, an early warning message is triggered.

[0026] In one embodiment, obtaining soil temperature and soil particle size information in the target area includes:

[0027] Obtain infrared images and RGB images of the surface of the planting area;

[0028] acquiring soil temperature distribution information based on the infrared image;

[0029] Soil particle size information is obtained based on the texture features of the RGB image.

[0030] In a second aspect, an embodiment of the present application provides a potato green spot disease monitoring system, the system comprising a target area module, a target green color block module and a monitoring result module; wherein,

[0031] The target area module is used to determine the target area where the soil thickness does not meet the standard thickness by pre-acquiring the ridge and furrow morphology of the planting area;

[0032] The target green color block module identifies the green color blocks in the target area and obtains the target green color blocks whose areas meet the preset area threshold;

[0033] The monitoring result module is used to determine the potato green spot disease monitoring result through a pre-trained detection model based on the leaf vein features in the target green color block.

[0034] In a third aspect, an embodiment of the present application provides a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, a potato green spot disease monitoring method as described in the first aspect above is implemented.

[0035] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a potato green spot disease monitoring method as described in the first aspect above.

[0036] The potato green spot disease monitoring method, system, device and medium provided in the embodiments of the present application have at least the following technical effects.

[0037] First, by pre-acquiring the ridge and furrow morphology of the planting area, the target area where the soil thickness does not meet the standard thickness is determined. It can accurately screen out the target area with insufficient soil thickness, narrow the monitoring scope and focus on high-risk areas. Secondly, identify the green blocks in the target area, and obtain the target green blocks whose area meets the preset area threshold. The green block area threshold is used to eliminate the interference of scattered green blocks and lock the target green area. Finally, based on the leaf vein characteristics in the target green block, the potato green spot disease monitoring results are determined through the pre-trained detection model. According to the leaf vein characteristics, the pre-trained detection model is used to distinguish potato green spots from interference objects such as fallen leaves. The accuracy of potato green spot disease monitoring is significantly improved. It solves the problem of low monitoring accuracy in traditional potato green spot disease monitoring methods in related technologies.

[0038] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0040] Figure 1 It is a flow chart of potato green spot disease monitoring method;

[0041] Figure 2 is a schematic diagram showing step S101 according to an exemplary embodiment;

[0042] Figure 3 is a system structure block diagram of a potato green spot disease monitoring system according to an exemplary embodiment;

[0043] Figure 4 It is a structural block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0045] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0046] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0047] Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by a person of ordinary skill in the technical field to which this application belongs. The words "one", "a", "the" and the like used in this application do not indicate a limit on quantity and may indicate the singular or plural. The terms "include", "comprise", "have" and any variations thereof used in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units that are inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The word "multiple" used in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0048] In a first aspect, the present invention provides a method for monitoring potato green spot disease. Figure 1 This is a flow chart of a potato green spot disease monitoring method, such as Figure 1 As shown, the method includes the following steps:

[0049] Step S101: determining a target area where the soil thickness does not meet the standard thickness by obtaining the ridge and furrow morphology of the planting area in advance.

[0050] Step S102: Identify the green color blocks in the target area, and obtain the target green color blocks whose areas meet the preset area threshold.

[0051] Step S103: Based on the vein features in the target green color block, the potato green spot disease monitoring result is determined by using a pre-trained detection model.

[0052] In summary, the embodiments of the present application provide a method for monitoring potato green spot disease. This method uses a pre-trained detection model to determine potato green spot disease monitoring results based on the leaf vein characteristics of the target green color block. Based on the leaf vein characteristics, the pre-trained detection model distinguishes potato green spot disease from interfering objects such as fallen leaves. This significantly improves the accuracy of potato green spot disease monitoring and addresses the low monitoring accuracy of conventional potato green spot disease monitoring methods in the related art.

[0053] Figure 2is a schematic diagram of step S101 according to an exemplary embodiment. Figure 2 As shown, step S101, by pre-acquiring the ridge and furrow morphology of the planting area, determines the target area where the soil thickness does not meet the standard thickness. Specifically, it includes the following steps:

[0054] Step S1011, obtaining a top view image and an oblique view image of the planting area;

[0055] Step S1012: measuring the furrow spacing based on the overhead image;

[0056] Step S1013, calculating the actual height of the ridge based on the oblique view image and the ridge-ditch spacing; specifically including: calculating the actual height through geometric relationships based on the pixel height, width, shooting angle and ridge-ditch spacing of the ridge in the oblique view.

[0057] Step S1014: Compare the actual height with a preset threshold, and mark the area below the preset threshold as a target area.

[0058] Alternatively, the target area can be determined by inferring the soil thickness by analyzing the geometric shape of the ridge (e.g., height and width). A top view of the planting area and oblique views of the planting area (e.g., 45°, 60°, and 30° viewing angles) are obtained in a natural environment with sufficient sunlight. Specifically:

[0059] Step 1: Data collection, as shown in Table 1 Data collection elements.

[0060] Table 1: Data collection elements table

[0061]

[0062] Shooting specifications may include: overhead shooting: shooting vertically facing the ridge; oblique shooting: shooting along the ridge at a 45° angle (for example); shooting time: 9-11 am (when the shadow direction is stable).

[0063] Step 2: Image Processing

[0064] Top-view analysis (ridge width measurement): Automatically identify the ridge and furrow areas in the top-view using existing software (such as AgriScan) or models, and measure the distance between adjacent furrows based on the top-view (typically 70-80 cm).

[0065] The conversion of oblique view (measuring ridge height) is expressed by the following formula:

[0066]

[0067] Where: the ridge spacing measured from the top is 75 cm, the width displayed at the same position in the oblique view is 120 pixels, the height displayed in the oblique view is 90 pixels, θ = 45°, and the actual height H = (75×90) / 120×sin45°≈39.8 cm.

[0068] The actual height obtained by the above calculation is compared with the preset threshold, and the area where the actual height is lower than the preset threshold is taken as the target area.

[0069] Step S101 achieves an accurate assessment of the thickness of the soil in the potato planting area through multi-angle image acquisition (top view and oblique view) combined with geometric calculations. Specifically, the distance between ridges and furrows is accurately measured through the top view, and the actual ridge height is calculated in combination with the stereoscopic perspective and geometric relationship of the oblique view, which can eliminate the measurement error of a single perspective; the reliability of the measurement results is ensured by shooting angles, time periods and the use of standardized calculations; and finally, by comparing with the preset threshold, high-risk areas with insufficient soil can be quickly identified. It effectively overcomes the defects of strong subjectivity and insufficient quantification of traditional manual visual inspection methods, provides a reliable target area positioning basis for the subsequent accurate identification of green spot disease, and significantly improves the accuracy and efficiency of overall monitoring.

[0070] In one embodiment, step S102, identifying a green block in the target area and obtaining a target green block whose area meets a preset area threshold, specifically includes:

[0071] Identify the pixels in the RGB image of the target area where the G value is greater than the B value and R value;

[0072] Compare the area of ​​the green block formed by adjacent pixels with the preset area threshold;

[0073] Get the target green color block whose green color block area is greater than or equal to the preset area threshold.

[0074] Alternatively, a small monitoring vehicle equipped with a camera can first be used to travel between ridges in different areas and capture RGB images of the target surface area. The acquisition frequency can be set individually, for example, to ensure approximately 5% overlap between adjacent frames, or to adjust the sampling frequency based on the vehicle's speed.

[0075] Next, the green blocks are identified based on the pixel's RGB values. For each pixel, if the pixel's G value is greater than both its B and R values, it indicates that the pixel leans toward green. Adjacent pixels leaning toward green form a green block. The area of ​​the green block formed by adjacent pixels is compared with a preset area threshold. If the green block's area is greater than or equal to the preset area threshold, that area is identified as the target green block.

[0076] Step S102: First, a green pixel extraction algorithm based on a G-value threshold (G>B and G>R) is used to effectively capture the green features of the RGB image of the target surface area. Next, an area screening algorithm (the area of ​​the green block formed by adjacent green pixels ≥ a preset area threshold) is used to eliminate the interference of sporadic green noise and ensure that the detected green areas are meaningful.

[0077] In one embodiment, step S103, determining the potato green spot disease monitoring result using a pre-trained detection model based on the vein features in the target green block, specifically includes:

[0078] Use the pre-trained detection model to detect the vein features in the target green block;

[0079] If the vein feature is detected in the target green color block, the target green color block is eliminated; if the vein feature is not detected in the target green color block, the target green color block is determined to be potato green spot disease.

[0080] Optionally, a pre-trained detection model is used to identify vein features within the identified target green block. If vein features are identified, the target green block is discarded, and the potato within the target green block does not have green skin features. If vein features are not identified, the target green block is treated as a green spot, indicating that the potato has green skin features. This indicates that the potato has exhibited greening, and is therefore identified as potato green spot disease. After the diagnosis of potato green spot disease is confirmed, further manual inspection or remedial measures can be prompted.

[0081] It's important to note that the pre-trained detection model can be a deep learning-based image classification model (such as a convolutional neural network (CNN) or a visual transformer). Its input is the RGB image region of the target green patch, and its output is a binary classification result of whether the target green patch contains leaf vein features. The model is trained on a large number of annotated leaf images and potato green spot images, learning to distinguish between leaf vein texture features (such as the regular network of veins) and the uniform green surface characteristics of potato green spots, thereby accurately filtering out interference.

[0082] Step S103: By incorporating a pre-trained leaf vein feature detection model to identify the target green patch, high-precision identification of potato green spot disease is achieved. When leaf vein features are detected, interference objects such as fallen leaves are automatically eliminated, retaining only the true potato green spot. This improves the accuracy of potato green spot monitoring.

[0083] In one embodiment, after determining the potato green spot disease monitoring results using a pre-trained detection model, the potato green spot disease monitoring method further includes obtaining soil temperature and soil particle size information in a target area, including obtaining infrared and RGB images of the surface of the planting area; obtaining soil temperature distribution information based on the infrared images; and obtaining soil particle size information based on the texture features of the RGB images. When the soil temperature and soil particle size exceed a preset standard range, an early warning message is triggered.

[0084] Optionally, areas within the target area with thinner soil and a high risk of tuber exposure are identified for focused screening and monitoring. The monitoring vehicle is also equipped with an infrared camera that simultaneously captures infrared images along with the RGB images. The infrared images are used to obtain the soil surface temperature distribution, and the soil particle size information is combined with the RGB images. Combining the soil particle size information with the soil surface temperature distribution, an early warning message is triggered when the soil temperature exceeds the preset standard temperature range, or when the soil particle size exceeds the preset standard particle size range, to alert the area that requires focused manual screening.

[0085] After completing the identification of leaf vein characteristics and determining the existing green spot disease, high-risk areas where drying is easy and the soil is thin, resulting in tuber exposure, can be discovered in advance through soil temperature, triggering early warning information to remind that this area requires key manual screening.

[0086] In summary, the embodiments of the present application provide a method for monitoring potato green spot disease. First, by pre-acquiring the ridge and furrow morphology of the planting area, the target area where the soil thickness does not meet the standard thickness is determined. The target area with insufficient soil thickness can be accurately screened out, the monitoring range can be narrowed down, and the high-risk area can be focused. Secondly, the green blocks in the target area are identified, and the target green blocks whose area meets the preset area threshold are obtained. Finally, the interference of scattered green blocks is eliminated through the green block area threshold screening, and the target green area is locked. Based on the leaf vein features in the target green block, the potato green spot disease monitoring results are determined by a pre-trained detection model. According to the leaf vein features, the pre-trained detection model is used to distinguish potato green spots from interferences such as fallen leaves. The accuracy of potato green spot disease monitoring is significantly improved. The problem of low monitoring accuracy in the traditional potato green spot disease monitoring method in the related art is solved.

[0087] In a second aspect, an embodiment of the present application provides a potato green spot disease monitoring system. Figure 3 FIG. 1 is a system structure diagram of a potato green spot disease monitoring system according to an exemplary embodiment. Figure 3 As shown, the system includes a target area module 310, a target green block module 320 and a monitoring result module 330; wherein,

[0088] The target area module 310 is used to determine the target area where the soil thickness does not meet the standard thickness by pre-acquiring the ridge and furrow morphology of the planting area;

[0089] The target green color block module 320 identifies the green color blocks in the target area and obtains the target green color blocks whose areas meet the preset area threshold;

[0090] The monitoring result module 330 is used to determine the potato green spot disease monitoring result through a pre-trained detection model based on the vein features in the target green color block.

[0091] In summary, the potato green spot disease monitoring system provided in this application significantly improves the accuracy of potato green spot disease monitoring through the target area module 310, the target green block module 320, and the monitoring result module 330. This solves the problem of low monitoring accuracy in traditional potato green spot disease monitoring methods in related technologies.

[0092] It should be noted that the potato green spot disease monitoring device provided in this embodiment is used to implement the aforementioned embodiments, and details already described will not be repeated. As used above, the terms "module," "unit," "subunit," etc. may refer to a combination of software and / or hardware that implements a predetermined function. While the devices described in the above embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0093] In a third aspect, an embodiment of the present application provides an electronic device, Figure 4 FIG is a block diagram of an electronic device according to an exemplary embodiment. Figure 4 As shown, the electronic device may include a processor 41 and a memory 42 storing computer program instructions.

[0094] Specifically, the processor 41 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0095] Among them, the memory 42 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 42 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 42 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 42 may be inside or outside the data processing device. In a specific embodiment, the memory 42 is a non-volatile memory. In a specific embodiment, the memory 42 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable Programmable Read-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0096] The memory 42 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 41 .

[0097] The processor 41 reads and executes computer program instructions stored in the memory 42 to implement any one of the potato green spot monitoring methods in the above embodiments.

[0098] In one embodiment, a potato green spot disease monitoring device may further include a communication interface 43 and a bus 40. Figure 4 As shown, the processor 41 , the memory 42 , and the communication interface 43 are connected via a bus 40 and communicate with each other.

[0099] The communication interface 43 is used to enable communication between the various modules, devices, units, and / or devices in the embodiments of the present application. The communication port 43 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0100] The bus 40 includes hardware, software, or both, and couples components of a potato green spot disease monitoring device to each other. The bus 40 includes, but is not limited to, at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 40 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of the above. Bus 40 may include one or more buses, where appropriate. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

[0101] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a potato green spot monitoring method provided in the first aspect is implemented.

[0102] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0103] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of a potato green spot disease monitoring method provided in the first aspect.

[0104] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0105] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The above embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for monitoring potato green spot disease, characterized in that: The method comprises: By obtaining the ridge and furrow morphology of the planting area in advance, the target area where the soil thickness does not meet the standard thickness is determined; Identifying a green color block in the target area, and obtaining a target green color block whose area meets a preset area threshold; Based on the leaf vein features in the target green color block, the potato green spot disease monitoring results are determined through a pre-trained detection model.

2. A potato green spot disease monitoring method according to claim 1, characterized in that: The identifying of the green color block in the target area and obtaining the target green color block whose area meets a preset area threshold includes: Identify pixels in the RGB image of the target area whose G value is greater than the B value and the R value; Comparing the area of ​​the green color block formed by the adjacent pixel points with a preset area threshold; A target green color block whose area is greater than or equal to the preset area threshold is obtained.

3. A potato green spot disease monitoring method according to claim 2, characterized in that: The method of determining the potato green spot disease monitoring result by using a pre-trained detection model based on the leaf vein feature in the green color block recognition result includes: Detecting leaf vein features in the target green block using a pre-trained detection model; If the leaf vein feature is detected in the target green color block, the target green color block is eliminated; if the leaf vein feature is not detected in the target green color block, the target green color block is determined to be potato green spot disease.

4. A potato green spot disease monitoring method according to claim 1, characterized in that: The method of obtaining the ridge and furrow morphology of the planting area in advance and determining the target area where the soil thickness does not meet the standard thickness includes: Obtaining overhead and oblique images of the planting area; measuring the furrow spacing based on the top view image; Calculating the actual height of the ridge based on the oblique image and the ridge furrow spacing; The actual height is compared with a preset threshold, and the area below the preset threshold is marked as a target area.

5. A potato green spot disease monitoring method according to claim 4, characterized in that: The calculating the actual height of the ridge based on the oblique image comprises: The actual height is calculated through geometric relationships based on the pixel height, width, shooting angle and ridge spacing of the ridges in the oblique view.

6. A potato green spot disease monitoring method according to claim 1, characterized in that: After determining the potato green spot disease monitoring results using the pre-trained detection model, the method further includes: The soil temperature and soil particle size information in the target area are obtained, and when the soil temperature and soil particle size exceed a standard preset range, an early warning message is triggered.

7. A potato green spot disease monitoring method according to claim 1, characterized in that: The obtaining of soil temperature and soil particle size information in the target area includes: Obtain infrared images and RGB images of the surface of the planting area; acquiring soil temperature distribution information based on the infrared image; Soil particle size information is obtained based on the texture features of the RGB image.

8. A potato green spot disease monitoring system, characterized in that: The system includes a target area module, a target green block module and a monitoring result module; wherein, The target area module is used to determine the target area where the soil thickness does not meet the standard thickness by pre-acquiring the ridge and furrow morphology of the planting area; The target green color block module identifies the green color blocks in the target area and obtains the target green color blocks whose areas meet the preset area threshold; The monitoring result module is used to determine the potato green spot disease monitoring result through a pre-trained detection model based on the leaf vein features in the target green color block.

9. An electronic device, characterized in that: The method comprises a memory and a processor, a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, a potato green spot disease monitoring method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a potato green spot disease monitoring method according to any one of claims 1 to 7 is implemented.

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