Intelligent nitrogen fertilizer management method and system for maize and peanut intercropping

Abstract

The application relates to an intelligent nitrogen fertilizer management method, system, computer device, storage medium and computer program product for corn and peanut intercropping. The method comprises the following steps: outputting an inspection path information to control an inspection unmanned aerial vehicle to fly to a starting point of the inspection path and make the inspection unmanned aerial vehicle complete the inspection according to the inspection path information; acquiring a thermal imaging image returned by the inspection unmanned aerial vehicle in the inspection process and splicing the thermal imaging image to acquire a global thermal imaging image; determining a SPAD sampling point position according to a temperature distribution in the thermal imaging image and outputting a sampling point coordinate corresponding to the SPAD sampling point; waiting for and acquiring a SPAD sampling result and one-to-one corresponding the SPAD sampling point position and the SPAD sampling result to acquire SPAD value distribution data; and generating a fertilization plan according to the SPAD value distribution data. The method can determine the required fertilization amount of corn plants corresponding to different sampling results according to the distribution of SPAD values, so that the utilization efficiency of nitrogen fertilizer is further improved, and fine farming is realized.

Description

Technical Field

[0001] This application relates to the field of smart agriculture technology, and in particular to a smart nitrogen fertilizer management method, system, computer equipment, storage medium and computer program product for intercropping corn and peanuts. Background Technology

[0002] Rhizobia in legumes are important symbiotic nitrogen-fixing microorganisms in nature. Their core function is to convert free nitrogen (N2) in the air into ammonia (NH3), which can be directly absorbed and utilized by plants. This process is called biological nitrogen fixation and is a key link in the nitrogen cycle on Earth.

[0003] A Chinese invention patent with publication number CN111869524B discloses a planting method for intercropping wheat, peanuts, and corn, suitable for arid regions. The method includes constructing multiple rows of ridges in the field, each 40 cm wide, with furrows 20 cm wide and 10 cm deep between adjacent ridges. Wheat is planted in October of the first year, with one row sown every 10 cm on the ridge surface, for a total of three rows. Another row of wheat is sown in the middle of the furrows. When the wheat height is below 10 cm, irrigation is provided via drip irrigation. When the wheat height reaches 10-15 cm, the field is irrigated through the furrows. 20-25 days before wheat harvest, the furrows and ridges are leveled, and a row of peanuts is intercropped at each of the two edges of the original furrows. After wheat harvest, a row of corn is planted every 20 cm between the wheat on each ridge surface, for a total of two rows of corn on each ridge surface. This method significantly increases yield compared to traditional intercropping methods.

[0004] However, studies have found that nitrogen fixation is maximized when nitrogen fertilizer is applied at a rate of 100 kg / ha. If traditional fertilization methods are used, it is difficult to maximize the efficiency of nitrogen fertilizer use, and some corn plants still suffer from insufficient nitrogen fertilizer, which is not conducive to achieving precision farming. Summary of the Invention

[0005] Based on this, it is necessary to provide an intelligent nitrogen fertilizer management method, system, computer equipment, computer-readable storage medium, and computer program product for intercropping corn and peanuts, which is conducive to achieving precision farming and improving nitrogen fertilizer use efficiency, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides an intelligent nitrogen fertilizer management method for intercropping corn and peanuts, the method comprising:

[0007] Output inspection path information to control the inspection drone to fly to the starting point of the inspection path and enable the inspection drone to complete the inspection according to the inspection path information;

[0008] Acquire thermal imaging images transmitted back by the inspection drone during the inspection process, and stitch the thermal imaging images together to obtain a global thermal imaging image;

[0009] The location of the SPAD sampling point is determined based on the temperature distribution in the thermal imaging image, and the coordinates of the corresponding SPAD sampling point are output.

[0010] Wait for and obtain the SPAD sampling results, and match the SPAD sampling point locations with the SPAD sampling results one by one to obtain the SPAD value distribution data;

[0011] A fertilization plan is generated based on the SPAD value distribution data.

[0012] In one embodiment, the specific steps of acquiring thermal imaging images transmitted back by the inspection drone during the inspection process and stitching the thermal imaging images together to obtain a global thermal imaging image include:

[0013] Acquire thermal imaging images taken by the inspection drone, position data when the thermal imaging images were taken, and attitude data of the drone when the thermal imaging images were taken;

[0014] The thermal imaging images are arranged and stitched together based on location and attitude data to obtain a global thermal imaging image.

[0015] In one embodiment, the specific steps of acquiring thermal imaging images transmitted back by the inspection drone during the inspection process and stitching the thermal imaging images together to obtain a global thermal imaging image include:

[0016] The thermal imaging images are arranged according to the sampling point location information;

[0017] Using the direction from the first sampling point to the second sampling point contained in the inspection path information as the reference direction, calculate the positive directional deviation between the attitude data corresponding to each thermal imaging image and the reference direction;

[0018] The thermal imaging image is rotated based on the positive deviation value, retaining the higher temperature value in the overlapping part of adjacent thermal imaging images.

[0019] In one embodiment, the specific steps of determining the location of the SPAD sampling point based on the temperature distribution in the thermal imaging image and outputting the coordinates of the corresponding SPAD sampling point include:

[0020] Obtain the highest and lowest temperature values ​​in the global thermal imaging image, and divide the temperature range based on the highest and lowest temperature values;

[0021] The global thermal imaging image is divided into regions based on temperature ranges to obtain the temperature regions corresponding to the temperature ranges.

[0022] Obtain the intersection point of the temperature region boundary and the path information, and use the location information of the intersection point as the location of the SPAD sampling point.

[0023] In one embodiment, the specific steps of waiting for and obtaining SPAD sampling results, and matching the SPAD sampling point locations with the SPAD sampling results to obtain SPAD value distribution data include:

[0024] Wait for and obtain the SPAD sampling results corresponding to the SPAD sampling point locations;

[0025] The SPAD sampling results are marked on the global thermal imaging image to obtain SPAD value distribution data.

[0026] In one embodiment, the specific steps for generating a fertilization plan based on SPAD value distribution data include:

[0027] Obtain the highest value in the SPAD value distribution data, obtain the regular fertilizer application data according to the preset SPAD value-fertilizer application rate correspondence, and use the regular fertilizer application rate data as the regular fertilizer application plan.

[0028] Obtain the lowest value in the SPAD value distribution data. Based on the preset SPAD-fertilizer amount correspondence, obtain the fertilizer amount corresponding to the lowest value in the SPAD value distribution data. Calculate the difference between the obtained fertilizer amount and the regular fertilizer amount data as a supplementary fertilization plan.

[0029] Secondly, this application also provides an intelligent nitrogen fertilizer management system for intercropping corn and peanuts, the system comprising:

[0030] The server module stores and outputs inspection path information corresponding to cultivated land. It also receives thermal imaging images, stitches them together to obtain a global thermal imaging image, and...

[0031] The global thermal imaging image acquires and outputs the location of SPAD sampling points. The server module is also used to receive the SPAD sampling results corresponding to the location of the SPAD sampling points, and generate and output a fertilization plan based on the SPAD sampling results.

[0032] The inspection drone module is used to receive inspection path information, inspect corn in the cultivated land according to the inspection path information, and acquire thermal imaging images.

[0033] The SPAD acquisition module is used to collect SPAD information of corn and output the SPAD sampling results corresponding to the location of the SPAD sampling point.

[0034] Thirdly, this application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0035] Output inspection path information to control the inspection drone to fly to the starting point of the inspection path and enable the inspection drone to complete the inspection according to the inspection path information;

[0036] Acquire thermal imaging images transmitted back by the inspection drone during the inspection process, and stitch the thermal imaging images together to obtain a global thermal imaging image;

[0037] The location of the SPAD sampling point is determined based on the temperature distribution in the thermal imaging image, and the coordinates of the corresponding SPAD sampling point are output.

[0038] Wait for and obtain the SPAD sampling results, and match the SPAD sampling point locations with the SPAD sampling results one by one to obtain the SPAD value distribution data;

[0039] A fertilization plan is generated based on the SPAD value distribution data.

[0040] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0041] Output inspection path information to control the inspection drone to fly to the starting point of the inspection path and enable the inspection drone to complete the inspection according to the inspection path information;

[0042] Acquire thermal imaging images transmitted back by the inspection drone during the inspection process, and stitch the thermal imaging images together to obtain a global thermal imaging image;

[0043] The location of the SPAD sampling point is determined based on the temperature distribution in the thermal imaging image, and the coordinates of the corresponding SPAD sampling point are output.

[0044] Wait for and obtain the SPAD sampling results, and match the SPAD sampling point locations with the SPAD sampling results one by one to obtain the SPAD value distribution data;

[0045] A fertilization plan is generated based on the SPAD value distribution data.

[0046] Fifthly, this application also provides a computer program product comprising a computer program that, when executed by a processor, performs the following steps:

[0047] Output inspection path information to control the inspection drone to fly to the starting point of the inspection path and enable the inspection drone to complete the inspection according to the inspection path information;

[0048] Acquire thermal imaging images transmitted back by the inspection drone during the inspection process, and stitch the thermal imaging images together to obtain a global thermal imaging image;

[0049] The location of the SPAD sampling point is determined based on the temperature distribution in the thermal imaging image, and the coordinates of the corresponding SPAD sampling point are output.

[0050] Wait for and obtain the SPAD sampling results, and match the SPAD sampling point locations with the SPAD sampling results one by one to obtain the SPAD value distribution data;

[0051] A fertilization plan is generated based on the SPAD value distribution data.

[0052] The aforementioned intelligent nitrogen fertilizer management method, system, computer equipment, storage medium, and computer program products for intercropping corn and peanuts first use a patrol drone to detect the canopy temperature of corn plants in the cornfield using thermal imaging technology. After the patrol is completed, a global thermal imaging image is obtained. Based on the temperature distribution in the global thermal imaging image, the location of SPAD sampling is determined, and SPAD values ​​are sampled. Finally, based on the collected SPAD values, different amounts of fertilizer are applied to areas with different SPAD value distributions, thereby achieving efficient utilization of nitrogen fertilizer and precision farming. Attached Figure Description

[0053] Figure 1 This is an application environment diagram of an intelligent nitrogen fertilizer management method for intercropping corn and peanuts in one embodiment;

[0054] Figure 2 This is a flowchart illustrating an intelligent nitrogen fertilizer management method for intercropping corn and peanuts in one embodiment.

[0055] Figure 3 This is a partial panoramic thermal image from one embodiment;

[0056] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] The intelligent nitrogen fertilizer management method for corn-peanut intercropping provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include drones, IoT instruments, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0059] In one embodiment, such as Figure 2 As shown, a smart nitrogen fertilizer management method for intercropping corn and peanuts is provided, which can be applied to... Figure 1 Taking server 104 as an example, the following steps are included:

[0060] Step S100: Output inspection path information to control the inspection drone to fly to the starting point of the inspection path and enable the inspection drone to complete the inspection according to the inspection path information.

[0061] The inspection path information is a one-to-one inspection path corresponding to each farmland stored in the storage space. After being output to the drone, the drone can parse the inspection path information to obtain the inspection start point, end point, and intermediate path connecting the start point and end point when inspecting the corresponding farmland. When the inspection drone performs the inspection task, it will first move to the start point corresponding to the inspection path information according to the received inspection path information, and then move along the intermediate path to the end point to complete the inspection task. During the inspection process, the inspection drone carries a detection device. In this embodiment, the detection device carried by the inspection drone includes a thermal imager. The thermal imager collects thermal imaging images from a top-down perspective and transmits them back to the server.

[0062] Step S200: Acquire the thermal imaging images transmitted back by the inspection drone during the inspection process, and stitch the thermal imaging images together to obtain a global thermal imaging image.

[0063] The thermal imaging images are acquired by a thermal imager mounted on the inspection drone. The principle is to capture the infrared radiation emitted by corn in the farmland and represent different temperatures in the image using different colors. In a preferred embodiment, the inspection path information also includes preset sampling points, which are marked on the path to indicate the locations where the inspection drone will collect thermal imaging images. After the inspection drone flies to the preset sampling point, it adjusts its attitude to align the thermal imager's shooting angle vertically downwards, then collects a thermal image and transmits it back to the server as image data.

[0064] The specific steps of step S200 include:

[0065] Step S210: Acquire the thermal imaging images taken by the inspection drone, the position data when the thermal imaging images were taken, and the attitude data of the drone when the thermal imaging images were taken.

[0066] Among them, the position data is the position information transmitted back to the server by the drone when the thermal imager acquires the thermal image, and the attitude data is the data output by the gyroscope of the inspection drone when the thermal image is acquired, which indicates the current positive direction of the drone.

[0067] Step S220: Arrange and stitch the thermal imaging images according to the position data and attitude data to obtain a global thermal imaging image.

[0068] Since the thermal imaging images transmitted back by the inspection drone correspond one-to-one with the attitude and position data, when stitching global thermal imaging images, the relative position of the thermal imaging image in the global thermal imaging image can be adjusted according to the position information, and the thermal imaging image can be rotated according to the attitude information, so that the stitched global thermal imaging image is closer to the actual situation in the farmland.

[0069] In one specific embodiment, step S220 further includes:

[0070] Step S221: Arrange the thermal imaging images according to the sampling point location information.

[0071] Step S222: Using the direction from the first sampling point to the second sampling point contained in the inspection path information as the reference direction, calculate the positive direction deviation value between the attitude data corresponding to each thermal imaging image and the reference direction.

[0072] Step S223: Rotate the thermal imaging image according to the positive direction deviation value, and retain the higher temperature value in the overlapping part of adjacent thermal imaging images.

[0073] In this embodiment of the application, in order to stitch together multiple thermal imaging images to form a more complete global thermal imaging image, the spacing between sampling points can be reduced, so that there is a partial overlap between adjacent thermal imaging images. Thus, after cutting and stitching the thermal imaging images, a more complete global thermal imaging image can be obtained.

[0074] Through steps S221 to S223, the difference between the orientation direction and the reference direction when each thermal imaging image is taken can be calculated. The thermal imaging image is then rotated according to the orientation deviation value to complete the image stitching. For the overlapping parts between the images, the highest value of the temperature corresponding to each pixel is taken to complete the image stitching.

[0075] Step S300: Determine the location of the SPAD sampling point based on the temperature distribution in the thermal imaging image, and output the coordinates of the sampling point corresponding to the SPAD sampling point.

[0076] The SPAD sampling point location is used to indicate the location information for SPAD sampling and detection of corn. The SPAD value is an indicator that characterizes the relative chlorophyll content of plant leaves, reflecting the "greenness" of the plant. The SPAD value of corn is directly proportional to the enrichment degree of nitrogen-fixing bacteria in its roots. By sampling the SPAD of corn leaves, the enrichment degree of nitrogen-fixing bacteria can be detected, and then the specific amount of fertilizer to be applied for subsequent supplementary fertilization can be calculated. More nitrogen fertilizer can be applied to corn plants with low enrichment of nitrogen-fixing bacteria, thereby increasing the yield per acre of corn and achieving precision farming.

[0077] In one embodiment, step S300 specifically includes the following steps:

[0078] Step S310: Obtain the highest and lowest temperature values ​​in the global thermal imaging image, and divide the temperature range according to the highest and lowest temperature values.

[0079] In one specific embodiment, the temperature range can be divided in the following way:

[0080] Based on the highest and lowest temperature values ​​in the global thermal imaging image, the temperature interval between the highest and lowest temperatures is divided according to a length ratio of 10:7:5:2:1 to obtain five temperature intervals of unequal length, with the interval length decreasing as the temperature increases. For example, if the highest temperature is 31℃ and the lowest temperature is 28.5℃, the five temperature intervals are (30.9, 31], (30.7, 30.9], (30.2, 30.7], (29.5, 30.2], and (28.5, 29.5] (unit: degrees Celsius, ℃).

[0081] In another specific embodiment, before dividing the temperature range, a difference threshold can be set for the temperature difference between the highest and lowest temperatures in the global thermal imaging image. For example, if the temperature difference is less than 1°C, when the temperature difference between the highest and lowest temperatures in the cultivated land is less than the difference threshold, it indicates that the canopy temperature distribution of corn in the current cultivated land is relatively uniform and concentrated. A similar or relatively uniform fertilization plan can be adopted for unified fertilization. Therefore, the subsequent step of dividing the temperature range can be skipped, and SPAD sampling points can be set uniformly directly. A unified fertilization plan can be set according to the sampling results, thereby reducing the information calculation and processing process of the system. If the temperature difference is greater than the preset temperature threshold, the steps of step S300 can be continued to complete the division of the temperature range.

[0082] Step S320: Divide the global thermal imaging image into regions according to the temperature range to obtain the temperature regions corresponding to the temperature ranges.

[0083] Based on the above example, a specific implementation of step S320 is as follows: In the global thermal imaging image, identify the temperature value corresponding to the color of the pixel in the global thermal imaging image, and obtain the pixel points corresponding to 31℃, 30.9℃, 30.7℃, 30.2℃ and 28.5℃. Using the pixel points corresponding to the above temperatures as boundaries, if some of the pixel points serving as boundaries are connected into a region, then retain the adjacent pixel points as the pixel points with higher temperatures as the pixel points constituting the boundaries, and the remaining pixel points are regarded as data located within the temperature range.

[0084] Step S330: Obtain the intersection point of the temperature region boundary and the path information, and use the location information of the intersection point as the location of the SPAD sampling point.

[0085] Based on the above example, a specific implementation of step S320 is as follows: The global thermal imaging image divided into temperature ranges obtained through the above steps is as follows: Figure 3 As shown, the intersection point with the path information is the location of the SPAD sampling point.

[0086] Since the temperature of the corn canopy is negatively correlated with its SPAD value, dividing the area with higher temperature in the panoramic temperature image into more temperature intervals can obtain more sampling points, thereby increasing the sampling density in the higher temperature area. This allows for a more accurate acquisition of the corn SPAD value in the high-temperature area, and thus a more precise amount of supplemental fertilizer.

[0087] Step S400: Wait for and obtain the SPAD sampling results, and match the SPAD sampling point locations with the SPAD sampling results one by one to obtain the SPAD value distribution data.

[0088] In one embodiment, step S400 specifically includes the following steps:

[0089] Step S410: Wait for and obtain the SPAD sampling result corresponding to the SPAD sampling point location.

[0090] The SPAD sampling process involves SPAD detectors mounted on inspection drones or staff members using handheld SPAD detectors to enter the SPAD sampling points in the fields to collect samples and upload the corresponding SPAD sampling results.

[0091] Step S420: Mark the SPAD sampling results in the global thermal imaging image to obtain SPAD value distribution data.

[0092] Specifically, by displaying the SPAD sampling results at the corresponding points in the global thermal imaging image, the SPAD value distribution data can be obtained. In the result display, the SPAD sampling results near the higher temperature location in the global thermal imaging image are more clustered, thus causing the SPAD detection density to concentrate on the canopy temperature (i.e., plants with lower nitrogen enrichment). This allows for a more accurate identification of the areas where corn plants lack nitrogen fertilizer, enabling more precise fertilization of the corn in these areas and achieving precision planting.

[0093] Step S500: Generate a fertilization plan based on the SPAD value distribution data.

[0094] In one embodiment, step S500 specifically includes the following steps:

[0095] Step S510: Obtain the highest value in the SPAD value distribution data, obtain the regular fertilizer application data according to the preset SPAD value-fertilizer application correspondence, and use the regular fertilizer application data as the regular fertilizer application plan.

[0096] Step S520: Take the lowest value in the SPAD value distribution data, obtain the fertilizer amount corresponding to the lowest value in the SPAD value distribution data according to the preset SPAD-fertilizer amount correspondence, and calculate the difference between the obtained fertilizer amount and the regular fertilizer amount data as a supplementary fertilization plan.

[0097] In step S500 (including steps S510 and S520), the SPAD-fertilizer amount correspondence is a function or correspondence between the plant's SPAD value and the required fertilizer amount obtained through statistics and / or calculation. Substituting the plant's SPAD value into this SPAD-fertilizer amount correspondence yields the corresponding required fertilizer amount. Based on this SPAD-fertilizer amount correspondence, the current nitrogen fertilizer amount required by the plant at each sampling point can be obtained. The lowest fertilizer amount lower than the calculated nitrogen fertilizer amount is determined as the regular fertilizer amount. The fertilizer amount for the remaining sampling point areas is supplemented by additional fertilizer amount on top of the regular fertilizer amount, thereby specifying the regular fertilizer plan and the supplementary fertilizer plan. This allows for the application of different nitrogen fertilizer amounts to different plants according to their needs, achieving full utilization of nitrogen fertilizer and refined cultivation.

[0098] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0099] Based on the same inventive concept, this application also provides an intelligent nitrogen fertilizer management system for intercropping corn and peanuts, which implements the intelligent nitrogen fertilizer management method for intercropping corn and peanuts described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the intelligent nitrogen fertilizer management system for intercropping corn and peanuts provided below can be found in the limitations of the intelligent nitrogen fertilizer management method for intercropping corn and peanuts described above, and will not be repeated here.

[0100] In one embodiment, an intelligent nitrogen fertilizer management system for corn-peanut intercropping is provided, comprising: a server module, an inspection drone module, and a SPAD data acquisition module, wherein:

[0101] The server module is used to store and output the inspection path information corresponding to the cultivated land. The server module is also used to receive thermal imaging images, stitch the thermal imaging images to obtain a global thermal imaging image, obtain the SPAD sampling point location based on the global thermal imaging image and output it. The server module is also used to receive the SPAD sampling results corresponding to the SPAD sampling point location, and generate and output the fertilization plan based on the SPAD sampling results.

[0102] The inspection drone module is used to receive inspection path information, inspect corn in the cultivated land according to the inspection path information, and acquire thermal imaging images.

[0103] The SPAD acquisition module is used to collect SPAD information of corn and output the SPAD sampling results corresponding to the location of the SPAD sampling point.

[0104] The modules in the aforementioned intelligent nitrogen fertilizer management system for intercropping corn and peanuts can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0105] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an intelligent nitrogen fertilizer management method for intercropping corn and peanuts. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0106] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0107] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0108] Step S100: Output inspection path information to control the inspection drone to fly to the starting point of the inspection path and enable the inspection drone to complete the inspection according to the inspection path information;

[0109] Step S200: Acquire the thermal imaging images transmitted back by the inspection drone during the inspection process, and stitch the thermal imaging images together to obtain a global thermal imaging image.

[0110] Step S300: Determine the location of the SPAD sampling point based on the temperature distribution in the thermal imaging image, and output the coordinates of the sampling point corresponding to the SPAD sampling point;

[0111] Step S400: Wait for and obtain the SPAD sampling results, and match the SPAD sampling point locations with the SPAD sampling results one by one to obtain the SPAD value distribution data;

[0112] Step S500: Generate a fertilization plan based on the SPAD value distribution data.

[0113] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0114] Step S100: Output inspection path information to control the inspection drone to fly to the starting point of the inspection path and enable the inspection drone to complete the inspection according to the inspection path information;

[0115] Step S200: Acquire the thermal imaging images transmitted back by the inspection drone during the inspection process, and stitch the thermal imaging images together to obtain a global thermal imaging image.

[0116] Step S300: Determine the location of the SPAD sampling point based on the temperature distribution in the thermal imaging image, and output the coordinates of the sampling point corresponding to the SPAD sampling point;

[0117] Step S400: Wait for and obtain the SPAD sampling results, and match the SPAD sampling point locations with the SPAD sampling results one by one to obtain the SPAD value distribution data;

[0118] Step S500: Generate a fertilization plan based on the SPAD value distribution data.

[0119] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0120] Step S100: Output inspection path information to control the inspection drone to fly to the starting point of the inspection path and enable the inspection drone to complete the inspection according to the inspection path information;

[0121] Step S200: Acquire the thermal imaging images transmitted back by the inspection drone during the inspection process, and stitch the thermal imaging images together to obtain a global thermal imaging image.

[0122] Step S300: Determine the location of the SPAD sampling point based on the temperature distribution in the thermal imaging image, and output the coordinates of the sampling point corresponding to the SPAD sampling point;

[0123] Step S400: Wait for and obtain the SPAD sampling results, and match the SPAD sampling point locations with the SPAD sampling results one by one to obtain the SPAD value distribution data;

[0124] Step S500: Generate a fertilization plan based on the SPAD value distribution data.

[0125] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0126] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0127] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A smart nitrogen fertilizer management method for intercropping corn and peanuts, characterized in that, The method includes: Output inspection path information to control the inspection drone to fly to the starting point of the inspection path and enable the inspection drone to complete the inspection according to the inspection path information; Acquire thermal imaging images transmitted back by the inspection drone during the inspection process, and stitch the thermal imaging images together to obtain a global thermal imaging image; The location of the SPAD sampling point is determined based on the temperature distribution in the thermal imaging image, and the coordinates of the corresponding SPAD sampling point are output. Wait for and obtain the SPAD sampling results, and match the SPAD sampling point locations with the SPAD sampling results one by one to obtain SPAD value distribution data; A fertilization plan is generated based on SPAD value distribution data; The specific steps for acquiring thermal imaging images transmitted back by the inspection drone during the inspection process and stitching the thermal imaging images together to obtain a global thermal imaging image include: Acquire the thermal imaging image captured by the inspection drone, the position data when the thermal imaging image was captured, and the attitude data of the drone when the thermal imaging image was captured; The thermal imaging images are arranged and stitched together based on the location data and attitude data to obtain a global thermal imaging image; The thermal imaging images are arranged according to the sampling point location information; Using the direction from the first sampling point to the second sampling point contained in the inspection path information as the reference direction, calculate the positive directional deviation value between the attitude data corresponding to each thermal imaging image and the reference direction; The thermal imaging image is rotated based on the positive direction deviation value, retaining the higher temperature value in the overlapping part of adjacent thermal imaging images; The specific steps for determining the location of SPAD sampling points based on the temperature distribution in the thermal imaging image and outputting the coordinates of the corresponding SPAD sampling points include: Obtain the highest and lowest temperature values ​​in the global thermal imaging image, and divide the temperature range according to the highest and lowest temperature values; The global thermal imaging image is divided into regions based on temperature ranges to obtain the temperature regions corresponding to the temperature ranges. Obtain the intersection point of the temperature region boundary and the path information, and use the location information of the intersection point as the location of the SPAD sampling point.

2. The intelligent nitrogen fertilizer management method for intercropping corn and peanuts according to claim 1, characterized in that, The specific steps for waiting for and acquiring SPAD sampling results, and matching the SPAD sampling point locations with the SPAD sampling results to obtain SPAD value distribution data include: Wait for and obtain the SPAD sampling result corresponding to the location of the SPAD sampling point; The SPAD sampling results are marked in the global thermal imaging image to obtain SPAD value distribution data.

3. The intelligent nitrogen fertilizer management method for intercropping corn and peanuts according to claim 2, characterized in that, The specific steps for generating a fertilization plan based on SPAD value distribution data include: Obtain the highest value in the SPAD value distribution data, obtain the regular fertilizer application data according to the preset SPAD value-fertilizer application correspondence, and use the regular fertilizer application data as the regular fertilizer application plan. Obtain the lowest value in the SPAD value distribution data, and according to the preset SPAD value-fertilizer amount correspondence, obtain the fertilizer amount corresponding to the lowest value in the SPAD value distribution data, and calculate the difference between the obtained fertilizer amount and the regular fertilizer amount data as a supplementary fertilization plan.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

Images