A corn production loss reduction method and system based on variable seeding technology

Abstract

The application discloses a corn production loss reduction method and system based on variable seeding technology, and relates to the technical field of intelligent agricultural machinery, and comprises the following steps: acquiring grid soil data of a target field, and decomposing the target field into multiple independent grid units; generating a prescription map containing target seeding amount and target fertilization amount of each grid based on the grid soil data; controlling a variable seeding system to perform variable seeding based on Beidou positioning information and the prescription map, and dynamically correcting actual seeding amount based on soil environmental factors during the seeding process; synchronously performing variable seed and fertilizer collaborative seeding and fertilization based on the prescription map and real-time Beidou coordinates; and storing seeding operation data, combining crop growth dynamic monitoring data to iteratively optimize variable control logic. Through Beidou positioning variable seeding and dynamic seeding amount correction, the application improves seeding accuracy and reduces losses caused by over-seeding and under-seeding; through variable seed and fertilizer collaborative seeding and fertilization, the application improves fertilizer utilization rate and reduces losses caused by quantitative base fertilization, seed burning and nutrient imbalance.

Description

Technical Field

[0001] This invention relates to the field of intelligent agricultural machinery technology, and in particular to a method and system for reducing losses in corn production based on variable seeding technology. Background Technology

[0002] The sowing stage is a core pre-harvest stage for grain production, and seed loss during this stage represents a long-overlooked form of hidden grain waste. Poor seedbed preparation leading to missing or weak seedlings is also a key factor in maize production losses. Currently, field maize sowing generally adopts a uniform fixed seeding rate operation mode, which has multi-dimensional seed loss defects. For example, a uniform seeding rate can result in excessive seed application in low-fertility areas, and traditional mechanical sowing equipment suffers from double-seeding and missed-seeding problems. Furthermore, existing sowing technologies have the following issues: ordinary precision seeders can only achieve uniform and equidistant sowing, and cannot dynamically adjust the seeding rate according to the spatial heterogeneity of farmland fertility, thus the problem of large-scale seed waste caused by soil fertility zoning adaptation still exists. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for reducing losses in maize production based on variable seeding technology. By using BeiDou positioning for variable seeding and dynamic seeding rate correction, the method improves seeding accuracy and reduces losses caused by reseeding or missed seeding. By using variable seed and fertilizer synergistic application, the method improves fertilizer utilization and reduces losses caused by seed burn and nutrient imbalance due to quantitative base fertilizer application.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solution: In a first aspect, embodiments of the present invention provide a method for reducing corn production losses based on variable seeding technology, comprising: Obtain gridded soil fertility data of the target field, which is divided into multiple independent grid units; Based on the gridded soil fertility data, a prescription map containing the target seeding rate and target fertilizer application rate for each grid is generated; Based on BeiDou positioning information and the prescription map, the control variable seeding system performs variable seeding and dynamically corrects the actual seeding rate based on soil environmental factors during the seeding process. Based on the prescription map and BeiDou real-time coordinates, variable seed and fertilizer co-application is carried out simultaneously. Store sowing operation data and iteratively optimize variable control logic by combining crop growth dynamic monitoring data.

[0005] As a further implementation method, the target field is prepared with simultaneous seedbed preparation, including: A combined operation of seedling strip preparation and deep loosening and fertilization was adopted to clean up the straw and stubble in the seed strip, forming a strip-shaped clean area; The soil after the seedling strip is cleared is compacted by using an independent enclosed tillage bin and heavy roller compaction. Straw is left on the ground outside the seedling strip to retain moisture.

[0006] As a further implementation, obtaining the gridded soil fertility data of the target field includes: Spectral data of the previous crop at different growth stages were collected using drone remote sensing scanning. Based on the spectral data, with soil organic matter as the core indicator, spectral geofertility indicators are assigned to each grid cell to form a continuous geofertility spatial layer. Based on the continuous soil fertility spatial layer, the optimal seeding rate and fertilization rate are calculated by grid, and the prescription map is verified by superimposing the GPS grid boundary.

[0007] As a further implementation, the controlled variable seeding system performs variable seeding, including: The location of the seeder is obtained in real time through the Beidou high-precision positioning unit, and the grid partitions in the prescription map are matched based on the location. Single-row seeding is performed using independent electric-driven seeding execution units for each row, and the seeding status is monitored by a grain sensor.

[0008] As a further implementation, the dynamic correction of the actual seeding rate based on soil environmental factors includes: By using an adaptive loss reduction and regulation model, the actual seeding rate is dynamically corrected by integrating three environmental factors: soil fertility, field moisture content, and straw mulch thickness, thus balancing seed saving and seedling survival rate. Through the in-situ instantaneous reseeding compensation mechanism, the seed falling status is continuously monitored during the sowing process, and the missed sowing is identified and the adjacent seeding volume is increased to replant seedlings locally.

[0009] As a further implementation, the simultaneous application of variable seed and fertilizer includes: Read the prescription map and, based on the real-time coordinates of BeiDou, simultaneously retrieve two sets of parameters for the current grid: the target seeding rate and the target fertilizer application rate. Based on the target seeding rate, seeding and fertilization are controlled, thereby achieving soil fertility-matched variable seed and fertilizer co-sowing.

[0010] As a further implementation, the storage of sowing operation data and the iterative optimization of variable control logic in conjunction with crop growth dynamic monitoring data include: Collect multi-source annual data, which includes sowing data and crop growth dynamic monitoring data; Based on the sowing data and crop growth dynamic monitoring data, grid data matching and correction are performed; The soil fertility model and variable control logic are iteratively optimized based on the corrected data for use in the next planting season.

[0011] Secondly, embodiments of the present invention also provide a corn production loss reduction system based on variable seeding technology, comprising: A gridded soil fertility database construction module is used to acquire gridded soil fertility data of a target field, which is divided into multiple independent grid units; The prescription map generation module is used to determine the target seeding rate and target fertilizer application rate for each grid cell based on the gridded soil fertility data, and generate a prescription map. The variable seeding module is used to control the variable seeding system to perform variable seeding based on BeiDou positioning information and the prescription map; The dynamic seeding rate correction module is used to dynamically correct the actual seeding rate based on soil environmental factors during the seeding process. The variable seed and fertilizer co-application module is used to synchronously apply variable seed and fertilizer co-application based on the prescription map and Beidou real-time coordinates. The data iteration and optimization module is used to store sowing operation data and iteratively optimize variable control logic.

[0012] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the corn production loss reduction method based on variable seeding technology.

[0013] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the corn production loss reduction method based on variable seeding technology.

[0014] The beneficial effects of this invention are as follows: This invention sets seeding rates based on soil fertility differences to achieve precise seed input and reduce the loss of good seeds due to unreasonable seeding rates; it efficiently prepares the seedbed by clearing stubble along the seedbed, improves the consistency of sowing depth, and thus improves the uniformity of seedling emergence, reducing seedling loss and weak seedlings caused by large amounts of straw in the seedbed, thereby reducing the loss of yield per acre in the later stages; it improves fertilizer utilization by using variable seed and fertilizer synergistic application, reducing the loss of seed burn and nutrient imbalance caused by quantitative base fertilizer; and it continuously optimizes the seeding rate decision model through sowing quality assessment and dynamic updates of prescription maps, so that the seed utilization rate of each season's sowing gradually increases. Attached Figure Description

[0015] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0016] Figure 1This is a flowchart of a corn production loss reduction method according to one or more embodiments of the present invention; Figure 2 This is a schematic diagram of a field gridded soil fertility zoning plan according to one or more embodiments of the present invention; Figure 3 This is a schematic diagram of the variable seeding technology according to one or more embodiments of the present invention; Figure 4 This is a schematic diagram of the variable seeding equipment structure according to one or more embodiments of the present invention.

[0017] Among them, 1. Tractor, 2. Precision seeder; 201. Seedling strip clearing device; 202. Seed bed forming device; 203. Air suction seed metering device. Detailed Implementation

[0018] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] Example 1: This embodiment provides a method for reducing corn production losses based on variable seeding technology, including: The target field is divided into multiple independent grid cells to obtain gridded soil fertility data. Based on gridded soil fertility data, a prescription map containing the target seeding rate and target fertilizer application rate for each grid is generated; Based on BeiDou positioning information and the prescription map, the control variable seeding system performs variable seeding and dynamically corrects the actual seeding rate based on soil environmental factors during the seeding process. Based on prescription maps and real-time BeiDou coordinates, variable seed and fertilizer co-application is carried out simultaneously. Store sowing operation data and iteratively optimize variable control logic by combining crop growth dynamic monitoring data.

[0020] Specifically, such as Figure 1 As shown, the method includes the following steps: Step 1: Obtain gridded soil fertility data for the target field.

[0021] The target field was divided into several independent grid units to complete the basic spatial framework decomposition of the field. Spectral data of the previous crop at different growth stages were collected using UAV remote sensing scanning, as shown in Table 1. Based on the acquired spectral data, soil organic matter was used as the core evaluation index to assign spectral soil fertility indices to each grid unit, and these were stitched together to form a continuous soil fertility spatial layer covering the entire target field.

[0022] Table 1. Correspondence between spectral indices and soil fertility conditions

[0023] In addition to soil organic matter, the spectral soil fertility index can also be combined with auxiliary parameters such as soil moisture content, soil layer thickness, and soil texture to achieve digital characterization of soil fertility differences in different locations within the field, providing basic data support for the differentiated calculation of subsequent seeding and fertilization rates.

[0024] Step 2: Based on, for example Figure 2 The gridded soil fertility data shown generates a prescription map containing the target seeding rate and target fertilizer application rate for each grid.

[0025] Read the continuous soil fertility spatial layer obtained in step 1, classify the soil fertility level according to the soil fertility index corresponding to the grid, and calculate the optimal seeding rate and target fertilization rate for each grid unit.

[0026] It should be noted that the specific process of generating prescription maps based on gridded soil fertility data is existing technology and will not be elaborated here.

[0027] Furthermore, soil fertility levels are estimated through spectral indices to determine basic yield targets. The total amount of fertilizer is determined based on the target yield, and the seeding rate is determined in conjunction with soil fertility, target population, and seed quality: the higher the soil fertility, the higher the upper limit of the target yield, but the amount of base fertilizer and seeding rate are appropriately reduced in proportion to yield to reduce losses caused by excessive seeding and fertilization; the lower the soil fertility, the seeding rate should be appropriately increased in proportion to yield to protect the population, with emphasis on supplemental fertilization to increase efficiency in low-yield fields.

[0028] Therefore, the seeding rate and fertilizer application rate are determined based on past experimental data and planting experience to match the soil fertility level.

[0029] After the parameter calculation and assignment are completed, GPS grid boundary information is superimposed to carry out prescription map verification, check the matching of grid boundary with the actual geographical boundary of the field, correct grid data with boundary misalignment and parameter abnormalities, and generate operation prescription map that can be identified and analyzed by variable seeding equipment.

[0030] Variable seeding equipment such as Figure 4 As shown, it mainly includes a tractor 1 and a precision seeder 2 attached to the rear of the tractor 1. The precision seeder 2 includes a seedbed clearing device 201, a seedbed forming device 202, an air suction seed metering device 203, etc. The specific structure of the variable seeding equipment is existing technology and will not be described in detail here.

[0031] Step 3: Based on the BeiDou positioning information and the prescription map, the variable seeding system is used to perform variable seeding.

[0032] The seeding machine is equipped with a Beidou high-precision positioning unit, which can obtain the machine's Beidou positioning information in real time during operation, match the real-time position with the grid partition in the prescription map, and automatically switch the corresponding benchmark seeding rate according to the grid it is in.

[0033] The variable seeding system features independent servo motors in each seeding channel, enabling independent speed adjustment for seeding operations. Each seeding channel is equipped with a grain sensor to monitor the falling grains in real time. The system employs a dual-feedback closed-loop control logic, collecting the actual output speed of the servo motors and the actual number of grains seeded from the grain sensors. This comprehensive compensation for seeding errors caused by ground slippage and motor response lag ensures that the actual seeding effect matches the target seeding rate set on the prescription map.

[0034] Before seeding operations, the seedbed preparation process is completed: a combined operation of seedling strip preparation and deep loosening and fertilization is performed. This involves clearing straw and stubble from the seedling strip area to create a clean, strip-shaped sowing environment. Then, seedbed shaping and adaptive compaction are performed to compact the soil in the seedling strip, while straw is retained on the surface outside the seedling strip to retain moisture. Seedbed preparation improves sowing depth consistency, enhances the sowing environment, and reduces the risk of missing or weak seedlings caused by straw residue. This step corresponds to the core concept of single-seed precision sowing, relying on an electrically driven pneumatic precision seed metering device to increase the single-seed rate, reduce double-seeding and missed sowing, and minimize seed damage caused by mechanical impact from the metering mechanism.

[0035] Step 4: Dynamically adjust the actual seeding rate based on soil environmental factors during the sowing process.

[0036] The seeder is equipped with multiple field environmental sensors, simultaneously collecting data on three environmental factors during operation: soil fertility, field moisture content, and straw cover thickness. This data is then input into an adaptive loss reduction and control model to dynamically adjust the baseline seeding rate given by the prescription map. This approach balances seed conservation goals with field germination and survival rates, avoiding operational deviations caused by relying solely on static prescription maps.

[0037] The adaptive loss reduction control model is as follows: The seeder does not plant according to fixed prescription map parameters, but rather dynamically adjusts seed metering / fertilization parameters based on information perceived during seeder operation (missed seeding, damage, etc.), adaptively correcting the seed metering device speed and seeding rate to minimize seedling loss. This control model is represented as: ; in, q t This indicates the real-time seeding rate per unit length (seeds / m). q 0 represents the baseline theoretical seeding quantity. k l This represents the seedling loss compensation coefficient. kv This indicates the correction factor for the machine's travel speed. k s This represents the soil / surface relief correction factor. n Indicates the speed of the seed metering device drive. v Indicates the forward speed of the machine. α Indicates compensation gain. t This indicates the perception-regulation time step.

[0038] When the loss rate is detected l rise, k l Increase the seeding rate automatically to compensate for potential seedling shortages and reduce losses.

[0039] Throughout the sowing process, the seed sensor continuously monitors the seed falling status and activates an in-situ instantaneous reseeding compensation mechanism. When a missed seeding is detected, the seeder does not need to stop. The control logic temporarily increases the seeding rate of adjacent seeding channels to complete local reseeding, avoiding the extra seed consumption caused by manual reseeding after a large area of ​​missing seedlings.

[0040] Step 5: Based on the prescription map and BeiDou real-time coordinates, synchronously carry out variable seed and fertilizer co-application.

[0041] During the sowing process, the prescription map data is continuously read and combined with the real-time positioning coordinates of Beidou navigation system. Simultaneously, two sets of operational parameters—target seeding rate and target fertilizer application rate—are retrieved for the current grid, enabling soil fertility-matched variable seed and fertilizer co-sowing operations. The amount of base fertilizer applied is dynamically adjusted according to the soil fertility level. In high-fertility grids, base fertilizer is appropriately reduced to avoid excessive concentrations that could burn seeds or damage buds; in low-fertility grids, base fertilizer is appropriately increased to replenish soil nutrients and reduce ineffective weak seedlings caused by nutrient imbalances.

[0042] This step implements the concept of planting according to local conditions, and accurately applies seeding and fertilization based on differences in soil fertility, thereby reducing the loss of good seeds caused by an imbalance in the seed-fertilizer ratio.

[0043] Step 6: Store the sowing operation data and iteratively optimize the variable control logic by combining it with crop growth dynamic monitoring data.

[0044] After the sowing operation is completed, the complete sowing operation data of the entire area is stored locally or in the cloud. The operation data includes at least grid location information, Beidou operation trajectory, theoretical sowing and fertilization amount of prescription map, actual sowing and fertilization amount of machinery, and raw environmental factor data collected during the operation.

[0045] Subsequent crop growth dynamic monitoring will be conducted to obtain growth data such as actual emergence rate, proportion of weak seedlings, and plant growth status for each grid. The accuracy of soil fertility values ​​for each grid will be calibrated using the growth monitoring data. During the next planting season, the calibrated grid soil fertility data will be used to iteratively optimize the soil fertility-seeding rate control model, update the field operation prescription map, and continuously optimize the loss reduction effect of pre-harvest corn planting year by year to achieve iterative optimization.

[0046] This embodiment sets seeding rates based on soil fertility differences to achieve precise seed input and reduce the loss of good seeds due to unreasonable seeding rates. The seedbed preparation with cleared stubble along the seedbed improves sowing depth consistency, thereby increasing seedling uniformity and reducing seedling loss and weak seedlings caused by excessive straw in the seedbed, thus lowering later yield losses. The use of an electrically driven pneumatic precision seed metering device increases the single-seed metering rate, reducing losses due to double-seeding and missed sowing, while also minimizing mechanical damage to seeds during metering. Through sowing quality assessment and dynamic updates to the prescription map, the seeding rate decision model is continuously optimized, gradually improving seed utilization for each season.

[0047] Example 2: This embodiment uses a 100-mu contiguous summer maize field in a plain as the target field for field operation experiment. The soil fertility of the test field has obvious spatial heterogeneity, and there are residual wheat straw from the previous crop in some parts of the field. The maize production loss reduction method based on variable seeding technology of the present invention is applied to this field to carry out field verification of summer maize sowing.

[0048] In this embodiment, the target 100-mu (approximately 6.7 hectares) field is divided into numerous independent grid units of 5m x 5m dimensions. This includes the following steps: S1 gridding: UAV remote sensing scans collect the spectrum of the previous crop at different growth stages, and assign spectral soil fertility index to each grid to form a continuous soil fertility spatial layer with soil organic matter as the core index. The baseline seeding rate is divided into high soil fertility zone (82,500 plants / hm²), medium soil fertility zone (78,000 plants / hm²), and poor soil fertility zone (75,000 plants / hm²), generating a plot-specific database. S2 high-efficiency seedbed preparation: The combined operation of seedbed preparation and deep loosening and fertilization is adopted to clean up the straw and stubble in the seedbed and realize strip clean area sowing; the operation of independent closed tillage chamber + heavy roller compaction is adopted to compact the soil after the seedbed is cleaned and cover the ground outside the seedbed with straw to effectively retain moisture.

[0049] S3 Beidou variable seeding execution: 4-channel independent electric-driven pneumatic seeding device, Beidou real-time matching grid switching seeding rate; S4 Real-time Seeding Rate Correction: The seeder integrates field sensors to monitor soil moisture content (22%) and straw cover thickness (0.8cm). The data is then fed into the control model. In areas with excellent soil fertility, the actual seeding rate is reduced by 5%. In areas with medium soil fertility, the baseline value is maintained. In areas with poor soil fertility, the actual seeding rate is increased by 5%. This balances seed conservation with seedling survival rate, taking into account both loss reduction and stable yield. The seed falling status is continuously monitored during the sowing process. If a seed is missed, the adjacent seeding rate can be increased to locally replant the seedlings without stopping the machine, eliminating the loss from manual replanting after large-scale seedling loss.

[0050] S5 variable seed and fertilizer co-sowing: Reads the gridded soil fertility database prescription map, and based on the real-time coordinates of Beidou, reduces the application of base fertilizer by 5% in grids with good soil fertility and increases the application of base fertilizer by 10% in grids with poor soil fertility, thereby reducing seed burn and nutrient imbalance.

[0051] S6 Data Iteration and Archiving: After the operation is completed, the data of the entire domain is uploaded to the cloud platform to store the sowing data of the entire domain. Combined with the crop growth dynamic monitoring data, the accuracy of the grid soil fertility values ​​is calibrated. The variable control logic is iterated and optimized in the next season to continuously improve the pre-harvest loss reduction effect of corn year by year.

[0052] Experimental comparison data Compared with conventional planting at a fixed density of 80,000 plants, the overall seed usage was reduced by 3.6%, the missed sowing rate was 0.5%, there was no need for manual replanting, and the proportion of weak seedlings in the field decreased by 7.3%.

[0053] Table 1. Comparison of Indicators between Traditional Summer Maize Planting Method and the Method of the Invention

[0054] Example 3: This embodiment provides a corn production loss reduction system based on variable seeding technology, such as... Figure 3 As shown, it includes: The gridded soil fertility database construction module is used to obtain gridded soil fertility data of the target field, which is divided into multiple independent grid units.

[0055] The prescription map generation module is used to determine the target seeding rate and target fertilizer application rate for each grid cell based on the gridded soil fertility data, and generate a prescription map.

[0056] The variable seeding module is used to perform variable seeding based on BeiDou positioning information and the prescription map control variable seeding system.

[0057] The dynamic seeding rate correction module is used to dynamically correct the actual seeding rate based on soil environmental factors during the seeding process.

[0058] The variable seed and fertilizer co-application module is used to synchronously apply variable seed and fertilizer based on the prescription map and Beidou real-time coordinates.

[0059] The data iteration and optimization module is used to store sowing operation data and iteratively optimize variable control logic.

[0060] Furthermore, the dynamic seeding rate correction module includes an adaptive loss reduction and control model. The seeder synchronously integrates factors such as missed seeding and seed damage to dynamically correct the actual seeding rate, balancing seed saving and seedling survival rate, and taking into account both loss reduction and stable yield.

[0061] Example 4: This embodiment provides an electronic device, including a memory and a processor. The memory stores computer programs, and the processor runs the computer programs to enable the electronic device to execute the corn production loss reduction method based on variable seeding technology described in Embodiment 1.

[0062] Example 5: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the corn production loss reduction method based on variable seeding technology described in Embodiment 1.

[0063] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for reducing losses in maize production based on variable seeding technology, characterized in that, include: Obtain gridded soil fertility data of the target field, which is divided into multiple independent grid units; Based on the gridded soil fertility data, a prescription map containing the target seeding rate and target fertilizer application rate for each grid is generated; Based on BeiDou positioning information and the prescription map, the control variable seeding system performs variable seeding and dynamically corrects the actual seeding rate based on soil environmental factors during the seeding process. Based on the prescription map and BeiDou real-time coordinates, variable seed and fertilizer co-application is carried out simultaneously. Store sowing operation data and iteratively optimize variable control logic by combining crop growth dynamic monitoring data.

2. The method for reducing corn production losses based on variable seeding technology according to claim 1, characterized in that, Simultaneous seedbed preparation for the target field includes: A combined operation of seedling strip preparation and deep loosening and fertilization was adopted to clean up the straw and stubble in the seed strip, forming a strip-shaped clean area; The soil after the seedling strip is cleared is compacted by using an independent enclosed tillage bin and heavy roller compaction. Straw is left on the ground outside the seedling strip to retain moisture.

3. The method for reducing corn production losses based on variable seeding technology according to claim 1, characterized in that, The acquisition of gridded soil fertility data for the target field includes: Spectral data of the previous crop at different growth stages were collected using drone remote sensing scanning. Based on the spectral data, with soil organic matter as the core indicator, spectral geofertility indicators are assigned to each grid cell to form a continuous geofertility spatial layer. Based on the continuous soil fertility spatial layer, the optimal seeding rate and fertilization rate are calculated by grid, and the prescription map is verified by superimposing the GPS grid boundary.

4. The method for reducing corn production losses based on variable seeding technology according to claim 1, characterized in that, The controlled variable seeding system performs variable seeding, including: The location of the seeder is obtained in real time through the Beidou high-precision positioning unit, and the grid partitions in the prescription map are matched based on the location. Single-row seeding is performed using independent electric-driven seeding execution units for each row, and the seeding status is monitored by a grain sensor.

5. A method for reducing corn production losses based on variable seeding technology according to claim 1, characterized in that, The dynamic correction of actual seeding rate based on soil environmental factors includes: By using an adaptive loss reduction and regulation model, the actual seeding rate is dynamically corrected by integrating three environmental factors: soil fertility, field moisture content, and straw mulch thickness, thus balancing seed saving and seedling survival rate. Through the in-situ instantaneous reseeding compensation mechanism, the seed falling status is continuously monitored during the sowing process, and the missed sowing is identified and the adjacent seeding volume is increased to replant seedlings locally.

6. The method for reducing corn production losses based on variable seeding technology according to claim 1, characterized in that, The simultaneous application of variable seed and fertilizer includes: Read the prescription map and, based on the real-time coordinates of BeiDou, simultaneously retrieve two sets of parameters for the current grid: the target seeding rate and the target fertilizer application rate. Based on the target seeding rate, seeding and fertilization are controlled, thereby achieving soil fertility-matched variable seed and fertilizer co-sowing.

7. A method for reducing corn production losses based on variable seeding technology according to claim 1, characterized in that, The storage of sowing operation data, combined with crop growth dynamic monitoring data, iteratively optimizes the variable control logic, including: Collect multi-source annual data, which includes sowing data and crop growth dynamic monitoring data; Based on the sowing data and crop growth dynamic monitoring data, grid data matching and correction are performed; The soil fertility model and variable control logic are iteratively optimized based on the corrected data for use in the next planting season.

8. A corn production loss reduction system based on variable seeding technology, characterized in that, include: A gridded soil fertility database construction module is used to acquire gridded soil fertility data of a target field, which is divided into multiple independent grid units; The prescription map generation module is used to determine the target seeding rate and target fertilizer application rate for each grid cell based on the gridded soil fertility data, and generate a prescription map. The variable seeding module is used to control the variable seeding system to perform variable seeding based on BeiDou positioning information and the prescription map; The dynamic seeding rate correction module is used to dynamically correct the actual seeding rate based on soil environmental factors during the seeding process. The variable seed and fertilizer co-application module is used to synchronously apply variable seed and fertilizer co-application based on the prescription map and Beidou real-time coordinates. The data iteration and optimization module is used to store sowing operation data and iteratively optimize variable control logic.

9. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the corn production loss reduction method based on variable seeding technology as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the corn production loss reduction method based on variable seeding technology as described in any one of claims 1-7.

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