Agricultural planting intelligent management method, equipment and medium

Abstract

The invention discloses an agricultural planting intelligent management method and device and a medium. The method comprises the steps that farmland environment data and crop growth images corresponding to all planting areas in a farmland are collected; for each planting area, identifying the crop growth image in the planting area to extract corresponding crop growth data; according to the crop growth data, predicting a predicted growth trend of each planting area in a future time period, and according to the predicted growth trend, determining planting parameters needing to be compensated for by the planting areas so as to generate a planting scheme corresponding to the planting areas through the planting parameters; dividing the intelligent planting equipment in the farmland according to the matching degree between the planting areas corresponding to the different planting schemes to obtain the intelligent planting equipment of different equipment grades; and controlling the intelligent planting equipment in the planting area to carry out planting operation according to the planting scheme according to the operation frequencies corresponding to different equipment grades, so as to realize intelligent planting of crops.

Description

Technical Field

[0001] The present application relates to the field of agricultural technology, and specifically to an intelligent management method, equipment and medium for agricultural planting. Background Art

[0002] In the current agricultural planting field, the monitoring and management of farmland environment and crop growth usually rely on manual regular observation, which is not only time-consuming and laborious, but also difficult to ensure the accuracy and timeliness of data, and it is difficult to accurately reflect the actual growth status of crops. In addition, the formulation of planting plans lacks intelligence and is often based on experience and judgment, making it difficult to make fine adjustments based on the specific conditions of different planting areas. At the same time, the allocation and operation frequency of intelligent planting equipment also lack scientific basis, resulting in uneven resource allocation and low planting efficiency. Summary of the invention

[0003] In order to solve the above problems, this application proposes an intelligent management method for agricultural planting, including: Based on the Internet of Things devices set up in the farmland, the farmland environment data and crop growth images corresponding to each planting area in the farmland are collected according to the preset collection interval; For each planting area, the crop growth image in the planting area is identified to extract corresponding crop growth data; wherein the crop growth data includes plant height, leaf area and fruit size; According to the crop growth data, predict the predicted growth trend of each planting area in a future period, and according to the predicted growth trend, determine the planting parameters required to be compensated for the planting area, so as to generate a planting plan corresponding to the planting area through the planting parameters; According to the matching degree between the planting areas corresponding to the different planting plans, the intelligent planting equipment in the farmland is divided to obtain intelligent planting equipment of different equipment levels; According to the operating frequencies corresponding to different equipment levels, the intelligent planting equipment in the planting area is controlled to perform planting operations according to the planting plan to achieve intelligent planting of the crops; wherein the equipment level is positively correlated with the operating frequency.

[0004] In one implementation of the present application, before determining the planting parameters required to be compensated for the planting area according to the predicted growth trend, the method specifically includes: For the future period, obtaining the planting time corresponding to the crops in the planting area, and determining the actual growth stage corresponding to the crops according to the planting time; Determining predicted crop growth data corresponding to the crop according to the predicted growth trend, and determining a predicted growth stage corresponding to the crop according to the predicted crop growth data; It is determined whether the actual growth stage matches the predicted growth stage, and if not, it is determined that the planting parameters corresponding to the planting area need to be compensated.

[0005] In one implementation of the present application, the planting parameters required to be compensated for the planting area are determined according to the predicted growth trend, so as to generate a planting plan corresponding to the planting area through the planting parameters, specifically including: Determining expected growth data of crops corresponding to the planting area based on the actual growth stage; Comparing the expected crop growth data with the predicted crop growth data, so as to determine the compensation target corresponding to the planting area according to the comparison result; wherein each compensation target corresponds to a planting parameter; According to the compensation target, a growth requirement parameter corresponding to the compensation target is obtained from a preset crop growth requirement information table; wherein the growth requirement information includes environmental requirement information, fertilization requirement information, and disease prevention and control requirement information; A planting plan corresponding to the planting area is generated according to the growth demand information.

[0006] In one implementation of the present application, the intelligent planting equipment in the farmland is divided according to the matching degree between the planting areas corresponding to different planting schemes to obtain intelligent planting equipment of different equipment levels, specifically including: According to the planting plan, a target planting area where planting operations need to be performed is screened out from the planting area, and for each target planting area, an associated intelligent planting device having a spatial association relationship therewith is determined, as well as an associated target planting area where the associated intelligent planting device is located; According to the planting plan, determining a matching degree between the target planting area and the associated target planting area; When the matching degree is greater than the preset matching degree, corresponding target device levels are assigned to the target planting area and the associated smart planting devices in the associated target planting area; wherein the target device level is greater than the device level corresponding to the smart planting devices in other planting areas.

[0007] In one implementation of the present application, for each target planting area, determining an associated intelligent planting device having a spatial association relationship therewith, and an associated target planting area where the associated intelligent planting device is located, specifically includes: For each target planting area, determining the intelligent planting equipment in the adjacent target planting area adjacent to the target planting area, and the coverage range of the intelligent planting equipment; According to the coverage range, calculating the coverage rate of the intelligent planting equipment in the target planting area; When the coverage rate is greater than the preset coverage rate, the intelligent planting device is used as an associated intelligent planting device that has a spatial association relationship with the target planting area, and the target planting area where the associated intelligent planting device is located is used as an associated target planting area corresponding to the target planting area.

[0008] In an implementation of the present application, determining the matching degree between the target planting area and the associated target planting area according to the planting plan specifically includes: Determining whether the types of crops in the target planting area and the associated target planting area are consistent; If so, according to the planting plan, the compensation targets corresponding to the target planting area and the associated target planting area are obtained, and the similarity between the compensation targets is calculated, and the similarity is used as the matching degree between the target planting area and the associated target planting area.

[0009] In one implementation of the present application, the crop growth image in the planting area is identified to extract corresponding crop growth data, specifically including: Preprocessing the crop growth image in the planting area, and extracting the crop contour corresponding to each crop in the planting area from the preprocessed crop growth image; Traversing the crop outline, extracting the highest reference feature point from the crop outline, and obtaining the corresponding plant height of the crop according to the height between the reference feature point and the ground; The crop growth image is segmented to obtain segmented leaf areas and fruit areas, the number of pixels corresponding to the leaf areas and the fruit areas are determined, and the leaf area and fruit size corresponding to the crop are calculated based on the number of pixels.

[0010] In one implementation of the present application, the method further includes: The average value of the crop growth data corresponding to each crop is calculated, and the crop growth data corresponding to the planting area is obtained according to the average value.

[0011] The present application provides an intelligent agricultural planting management device, the device comprising: at least one processor; and, at least one processor is communicatively connected to a memory; wherein, The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute an intelligent agricultural planting management method as described in any of the above items.

[0012] The embodiment of the present application provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured as follows: An intelligent management method for agricultural planting as described in any of the above items.

[0013] The intelligent agricultural planting management method proposed in this application can bring the following beneficial effects: the automatic collection of farmland environmental data and crop growth images by Internet of Things devices improves the accuracy and timeliness of data and reduces the time and effort of manual observation. At the same time, the use of image recognition technology to extract crop growth data provides a scientific basis for the formulation of planting plans, and realizes the intelligent and refined adjustment of planting plans. According to the matching degree between the planting areas corresponding to different planting plans, the intelligent planting equipment is divided, and the corresponding operating frequency is set according to the equipment level, so as to realize the scientific allocation and efficient utilization of planting resources and avoid the problems of uneven resource allocation and low planting efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] 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: Figure 1 A flowchart of an intelligent agricultural planting management method provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an intelligent agricultural planting management device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0016] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0017] like Figure 1 As shown, an intelligent agricultural planting management method provided in an embodiment of the present application includes: 101: Based on the IoT devices installed in the farmland, the farmland environment data and crop growth images corresponding to each planting area in the farmland are collected according to the preset collection interval.

[0018] In large farmlands, the farmlands need to be divided into multiple planting areas, each of which is planted with corresponding crops. A variety of sensors are deployed in the planting areas according to actual needs, such as temperature sensors, humidity sensors, light sensors, soil moisture sensors, soil pH sensors, etc. These sensors can monitor key parameters in the farmland environment in real time. Cameras or other image acquisition devices need to be installed in each planting area to capture the growth images of crops. Based on the above sensors, a reasonable collection interval is preset according to the growth cycle of crops and the characteristics of farmland environmental changes. For example, during the critical period of crop growth, the collection interval can be shortened to obtain more detailed data; while during relatively stable periods, the collection interval can be appropriately extended to save resources. According to the preset collection interval, the farmland environment data and crop growth images in each planting area are collected in real time, and these data are analyzed in depth, which helps growers to intuitively understand the growth of crops in the farmland, and further take corresponding planting measures to ensure the yield of crops.

[0019] 102: For each planting area, identify the crop growth image in the planting area to extract corresponding crop growth data; wherein the crop growth data includes plant height, leaf area and fruit size.

[0020] Based on image recognition technology, crop growth images are identified and further growth-related crop growth data such as plant height, leaf area and fruit size are extracted. The plant height is calculated by comparing the pixel distance between the top of the plant and the ground in the image, combined with parameters such as the focal length and shooting angle of the camera. The leaf area and fruit area can be obtained by extracting the contour and calculating the enclosed area.

[0021] Specifically, the crop growth image is preprocessed to obtain the preprocessed crop growth image. First, the collected crop growth image is denoised to remove the noise points in the image, and then the image is enhanced to improve the contrast and clarity of the image, so that the crop features are more obvious. If there is color deviation in the image, color correction is required to ensure the authenticity and consistency of the image color. After the crop growth image is preprocessed, the crop contour corresponding to each crop is extracted, the crop contour is traversed, and the highest point in the contour is found as the reference feature point. Generally speaking, the reference feature point is the top of the plant or the tip of the highest leaf. Then, according to the focal length of the camera, the shooting angle and the pixel distance between the reference feature point in the image and the ground, the actual plant height corresponding to the crop is obtained by proportional conversion. This requires the camera to be calibrated in advance to obtain the internal and external parameters of the camera. The crop growth image is segmented into leaf area and fruit area by image segmentation algorithm (such as threshold segmentation, region growing, watershed algorithm, etc.), and the segmented area is further refined to remove noise and artifacts. Determine the number of pixels corresponding to the segmented leaf area and fruit area respectively, and convert the number of pixels into actual area or volume according to the camera calibration parameters and image resolution to obtain the leaf area and fruit size of the crop.

[0022] Repeat the above steps for each crop in the planting area to obtain the plant height, leaf area and fruit size data of each crop. The growth data of each crop is counted and the average values ​​of plant height, leaf area and fruit size are calculated. The final average values ​​can be used as the crop growth data corresponding to the planting area for subsequent analysis and decision-making.

[0023] 103: Based on the crop growth data, predict the growth trend of each planting area in the future period, and determine the planting parameters required to be compensated for the planting area based on the predicted growth trend, so as to generate a planting plan corresponding to the planting area through the planting parameters.

[0024] According to the collected crop growth data, the deep learning model can be used to predict the predicted growth trend of crops in the future period. Based on the prediction results, by comparing the gap between the actual growth trend and the predicted growth trend, the planting parameters that need to be compensated in each planting area can be clearly identified, such as the amount of fertilizer, irrigation, pest control measures, etc. In combination with the predicted growth trend and the planting parameters that need to be compensated, a corresponding planting plan is formulated. During the implementation of the planting plan, it is also necessary to pay close attention to the growth of crops and environmental changes. If necessary, the planting plan can be adjusted to deal with possible abnormal situations in a timely manner.

[0025] In one embodiment, according to the planting records or planting plans of crops, the duration from planting to the current moment of crops in the future period is obtained. According to the growth cycle and growth characteristics of crops, combined with the planting duration, the actual current growth stage of crops is determined. For example, whether it is in the seedling stage, growth stage, flowering stage or fruiting stage, etc. Key growth data such as plant height, leaf area, fruit size, etc. are extracted from the predicted growth trend, and can be used to determine the predicted growth stage based on the predicted growth data of crops. The actual growth stage is compared with the predicted growth stage to determine whether the two are consistent or close. If the actual growth stage does not match the predicted growth stage, that is, there is a significant difference, it means that the growth of crops may be affected by adverse factors, or the planting parameters need to be adjusted. At this time, it is necessary to compensate for the planting parameters corresponding to the planting area.

[0026] When determining the planting parameters that need to be compensated, it is first necessary to determine the expected growth data that the crops should achieve at this stage based on the current actual growth stage of the crops, combined with the growth characteristics and environmental conditions of the crops. Compare the expected growth data of the crops with the predicted growth data of the crops, analyze the differences between the two, and determine the target that needs to be compensated based on the comparison results. Each compensation target usually corresponds to a planting parameter. The purpose of the compensation target is to make the actual growth of the crops closer to the expected growth data by adjusting these planting parameters.

[0027] After determining the compensation target, it is necessary to obtain the corresponding growth requirement parameters from the preset crop growth requirement information table according to the determined compensation target. The crop growth requirement information table contains information on the crops' requirements for the environment (such as temperature, humidity, and light), fertilization (such as fertilizer type, fertilizer amount, and fertilization time), and disease control (such as control agents, control time, and control methods) at different growth stages. Therefore, after obtaining the above growth requirement information, a detailed planting parameter adjustment plan can be formulated based on the growth requirement information and compensation target to generate the final planting plan.

[0028] 104: According to the matching degree between the planting areas corresponding to the different planting plans, the intelligent planting equipment in the farmland is divided to obtain intelligent planting equipment of different equipment levels.

[0029] Since large farmlands are generally planted in different areas, the types of crops in different planting areas may be the same or different. Each planting area will be equipped with a series of intelligent planting equipment at the area boundary or fixed position, such as sowing equipment, irrigation equipment, fertilization equipment, etc. Therefore, on the basis of the spatial correlation between different planting areas, the intelligent planting equipment in these different planting areas can be applied to multiple planting plans at the same time, that is, multiple planting areas can be supplied for corresponding planting operations. Then, for the above-mentioned intelligent planting equipment that can be used in multiple planting areas, in order to highlight its importance and priority in planting operations, it is necessary to assign equipment levels to each intelligent planting equipment that performs planting operations. Through different equipment levels, the operating frequency of the intelligent planting equipment can be further controlled, thereby ensuring the completion of planting operations in each planting area. The efficiency of the planting operation is greatly improved.

[0030] In one embodiment, according to the planting plan, the target planting areas where planting operations need to be performed are screened out from the planting areas, and for each target planting area, the associated intelligent planting equipment that has a spatial association relationship therewith, and the associated target planting area where the associated intelligent planting equipment is located are determined. Since the working direction of the intelligent planting equipment is not fixed when it is working, for example, the irrigation equipment can perform irrigation operations to the target planting areas in multiple directions, and the fertilization equipment can be applied to multiple target planting areas at the same time for fertilization operations, but the above-mentioned intelligent planting equipment is limited by its setting position and is generally only applied to the adjacent planting areas of the planting area where it is located, and when operating in the adjacent adjacent planting areas, the operating range of the intelligent planting equipment may be subject to certain limitations. Therefore, the spatial association relationship between the intelligent planting equipment and the target planting area can be determined based on the operating range of the above-mentioned intelligent planting equipment.

[0031] Specifically, for each target planting area, the intelligent planting equipment in the adjacent target planting area adjacent to the target planting area and the coverage of the intelligent planting equipment are determined. Since the target planting area can exist in the form of grid units on the map, when defining the adjacent target planting area, it is usually identified as the adjacent target planting area in eight directions of the target planting area. The coverage refers to the maximum operating range of the intelligent planting equipment when performing planting operations in different operating directions. After determining the coverage of the intelligent planting equipment, the coverage rate of the intelligent planting equipment in the target planting area can be calculated based on the ratio between the area area of ​​the overlapping area between the coverage area and the target intelligent planting area and the area area of ​​the target intelligent planting area. If the coverage rate is greater than the preset coverage rate, the preset coverage rate is generally 2 / 3, indicating that the intelligent planting equipment in the adjacent planting area can not only complete the planting operation in the planting area where it is located, but also can be applied to the target planting area and can complete a larger range of planting operations. At this time, the intelligent planting equipment can be used as an associated intelligent planting equipment that has a spatial association relationship with the target planting area, and the target planting area where the associated intelligent planting equipment is located is used as the associated target planting area corresponding to the target planting area.

[0032] After the associated planting areas of the target planting area are identified, the matching degree between the target planting area and the associated target planting area is determined according to the planting plan. The matching degree is used to measure the similarity or adaptability between two planting areas. When calculating the matching degree, it is first necessary to determine whether the crop types in the target planting area and the associated target planting area are consistent. If the crop types are consistent, it is necessary to obtain the compensation targets corresponding to the target planting area and the associated target planting area according to the planting plan, and calculate the similarity between the compensation targets. The similarity obtained at this time is the matching degree between the target planting area and the associated target planting area.

[0033] When the matching degree is greater than the preset matching degree, it means that not only the same crops are planted in the target planting area and its associated target planting area, but also the planting operations to be completed are relatively similar. Therefore, when implementing the planting operation, due to the spatial correlation between the areas, in addition to scheduling the intelligent planting operations within the area itself, the associated intelligent planting equipment can also be used to schedule the planting operations in the two planting areas at the same time. It should be noted that the scheduling of associated intelligent planting equipment needs to ensure the singleness of the planting operation within a certain period of time, that is, it can only be applied to one planting area within a period of time. According to the above content, since the work intensity of the associated intelligent planting equipment is relatively high, in order to ensure that it can complete the planting operations in multiple planting areas in a timely manner, it is necessary to appropriately improve its work efficiency. Therefore, when allocating equipment levels, it is necessary to ensure that the target equipment level corresponding to the associated intelligent planting equipment is greater than the equipment level corresponding to the intelligent planting equipment in other planting areas. There is a positive correlation between the equipment level and the operating frequency. That is, the higher the equipment level, the higher the operating frequency. Therefore, by increasing the operating frequency of the associated intelligent planting equipment, the intelligent planting equipment can be effectively applied to multiple planting areas during the implementation of planting operations. This can greatly improve the efficiency of planting operations and shorten the operating time required for planting operations.

[0034] 105: According to the operating frequencies corresponding to different equipment levels, the intelligent planting equipment in the planting area is controlled to perform planting operations according to the planting plan, so as to realize the intelligent planting of crops; wherein the equipment level is positively correlated with the operating frequency.

[0035] After determining the equipment levels of different intelligent planting equipment, the corresponding intelligent planting equipment is controlled to perform planting operations according to the planting plan according to the operating frequencies corresponding to the different equipment levels. This realizes automatic identification and timely intervention of abnormal growth trends when crops grow abnormally. Through the automated operation of intelligent planting equipment, labor costs are reduced, and operating efficiency and quality can be effectively improved, ensuring the healthy growth, high yield and high quality of crops.

[0036] The above are embodiments of the method proposed in this application. Based on the same idea, some embodiments of this application also provide devices and non-volatile computer storage media corresponding to the above methods.

[0037] Figure 2 This is a schematic diagram of the structure of an intelligent agricultural planting management device provided in an embodiment of the present application. Figure 2 As shown, including: at least one processor; and, at least one processor is communicatively connected to a memory; wherein, The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute an intelligent agricultural planting management method as described in any of the above items.

[0038] The embodiment of the present application provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured as follows: An intelligent management method for agricultural planting as described in any of the above items.

[0039] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0040] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0041] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0042] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0043] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0045] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0046] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0047] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0048] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0049] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. An intelligent management method for agricultural planting, characterized in that: The method comprises: Based on the Internet of Things devices set up in the farmland, the farmland environment data and crop growth images corresponding to each planting area in the farmland are collected according to the preset collection interval; For each planting area, the crop growth image in the planting area is identified to extract corresponding crop growth data; wherein the crop growth data includes plant height, leaf area and fruit size; According to the crop growth data, predict the predicted growth trend of each planting area in a future period, and according to the predicted growth trend, determine the planting parameters required to be compensated for the planting area, so as to generate a planting plan corresponding to the planting area through the planting parameters; According to the matching degree between the planting areas corresponding to different planting plans, the intelligent planting equipment in the farmland is divided to obtain intelligent planting equipment of different equipment levels; According to the operating frequencies corresponding to different equipment levels, the intelligent planting equipment in the planting area is controlled to perform planting operations according to the planting plan to achieve intelligent planting of the crops; wherein the equipment level is positively correlated with the operating frequency.

2. The intelligent agricultural planting management method according to claim 1, characterized in that: Before determining the planting parameters required to be compensated for the planting area according to the predicted growth trend, the method specifically includes: For the future period, obtaining the planting time corresponding to the crops in the planting area, and determining the actual growth stage corresponding to the crops according to the planting time; Determining predicted crop growth data corresponding to the crop according to the predicted growth trend, and determining a predicted growth stage corresponding to the crop according to the predicted crop growth data; It is determined whether the actual growth stage matches the predicted growth stage, and if not, it is determined that the planting parameters corresponding to the planting area need to be compensated.

3. The intelligent agricultural planting management method according to claim 2, characterized in that: Determining the planting parameters required to be compensated for the planting area according to the predicted growth trend, so as to generate a planting plan corresponding to the planting area through the planting parameters, specifically including: Determining expected growth data of crops corresponding to the planting area based on the actual growth stage; Comparing the expected crop growth data with the predicted crop growth data, so as to determine the compensation target corresponding to the planting area according to the comparison result; wherein each compensation target corresponds to a planting parameter; According to the compensation target, a growth requirement parameter corresponding to the compensation target is obtained from a preset crop growth requirement information table; wherein the growth requirement information includes environmental requirement information, fertilization requirement information, and disease prevention and control requirement information; A planting plan corresponding to the planting area is generated according to the growth demand information.

4. The method for intelligent agricultural planting management according to claim 1, characterized in that: According to the matching degree between the planting areas corresponding to different planting plans, the intelligent planting equipment in the farmland is divided to obtain intelligent planting equipment of different equipment levels, specifically including: According to the planting plan, a target planting area where planting operations need to be performed is screened out from the planting area, and for each target planting area, an associated intelligent planting device having a spatial association relationship therewith is determined, as well as an associated target planting area where the associated intelligent planting device is located; According to the planting plan, determining a matching degree between the target planting area and the associated target planting area; When the matching degree is greater than the preset matching degree, corresponding target device levels are assigned to the target planting area and the associated smart planting devices in the associated target planting area; wherein the target device level is greater than the device level corresponding to the smart planting devices in other planting areas.

5. The intelligent agricultural planting management method according to claim 4, characterized in that: For each target planting area, determining the associated intelligent planting device having a spatial association relationship therewith and the associated target planting area where the associated intelligent planting device is located, specifically includes: For each target planting area, determining the intelligent planting equipment in the adjacent target planting area adjacent to the target planting area, and the coverage range of the intelligent planting equipment; Calculating the coverage rate of the intelligent planting equipment in the target planting area according to the coverage range; When the coverage rate is greater than the preset coverage rate, the intelligent planting device is used as an associated intelligent planting device that has a spatial association relationship with the target planting area, and the target planting area where the associated intelligent planting device is located is used as an associated target planting area corresponding to the target planting area.

6. The intelligent agricultural planting management method according to claim 4, characterized in that: Determining the matching degree between the target planting area and the associated target planting area according to the planting plan specifically includes: Determining whether the types of crops in the target planting area and the associated target planting area are consistent; If so, according to the planting plan, the compensation targets corresponding to the target planting area and the associated target planting area are obtained, and the similarity between the compensation targets is calculated, and the similarity is used as the matching degree between the target planting area and the associated target planting area.

7. The intelligent agricultural planting management method according to claim 1, characterized in that: Identifying the crop growth image in the planting area to extract corresponding crop growth data specifically includes: Preprocessing the crop growth image in the planting area, and extracting the crop contour corresponding to each crop in the planting area from the preprocessed crop growth image; Traversing the crop contour, extracting the highest reference feature point from the crop contour, and obtaining the corresponding plant height of the crop according to the height between the reference feature point and the ground; The crop growth image is segmented to obtain segmented leaf areas and fruit areas, the number of pixels corresponding to the leaf areas and the fruit areas are determined, and the leaf area and fruit size corresponding to the crop are calculated based on the number of pixels.

8. The intelligent agricultural planting management method according to claim 7, characterized in that: The method further comprises: The average value of the crop growth data corresponding to each crop is calculated, and the crop growth data corresponding to the planting area is obtained according to the average value.

9. An intelligent agricultural planting management device, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an intelligent agricultural planting management method as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to: An intelligent agricultural planting management method as described in any one of claims 1 to 8.

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