Agricultural seeding machinery automatic control system based on ground environment

Abstract

The invention relates to the technical field of intelligent agriculture, in particular to an agricultural seeding machinery automatic control system based on a ground environment, and the system comprises a data collection module which is used for collecting the environment data of a target planting area, the soil data of a plurality of collection points, and the previous crop data of a plurality of previous crops; the soil analysis module is used for dividing the target planting area into a plurality of planting sub-areas; the environment analysis module is used for determining an environment influence index; the crop analysis module is used for determining crop distribution characteristics of a single planting sub-region and determining an edge influence index corresponding to each adjacent planting sub-region; and the seeding analysis module is used for determining an initial seeding parameter of a single planting sub-region, and determining whether to adjust the initial seeding parameter of the single planting sub-region or not based on the environment influence index and the edge influence index corresponding to each adjacent planting sub-region. According to the invention, multi-dimensional data sensing can be realized, the seeding parameters are accurately determined, and the seeding quality is improved.

Description

Technical Field

[0001] This invention relates to the field of smart agriculture technology, and in particular to an automated control system for agricultural sowing machinery based on the farming environment. Background Technology

[0002] Intercropping, as a highly efficient multi-cropping planting model, fully utilizes agricultural resources such as light, heat, water, and soil by sowing the following crop between the rows of the previous crop during its later growth stage, significantly improving land utilization and yield per unit area. It plays a vital role in my country's food security and sustainable agricultural development. However, the sowing process in intercropping has always been a challenge in agricultural production due to factors such as shading from the previous crop, limited space between rows, and complex and variable planting environments.

[0003] Currently, intercropping mainly relies on manual or semi-mechanized operations: manual sowing is labor-intensive and inefficient, and sowing parameters (depth, spacing, etc.) are entirely based on experience, resulting in poor accuracy; semi-mechanized sowing often uses modified general-purpose sowing machinery, lacking specific designs for intercropping scenarios. While existing automated control systems for agricultural sowing machinery can automatically adjust some parameters, they have significant limitations for intercropping: firstly, environmental perception only focuses on basic soil data (such as moisture), failing to consider the impact of key parameters like plant height, row spacing, and stem diameter of the previous crop on sowing, thus failing to reflect the complex "planting environment-crop interaction" relationship; secondly, a decision-making mechanism for sowing parameters that considers the synergistic effect of soil characteristics, environmental conditions, and previous crop parameters has not been established, ultimately making it difficult to output optimal sowing parameters suitable for the intercropping scenario, leading to uneven emergence and growth of the subsequent crop, affecting yield and quality. Therefore, developing an automated control system for agricultural sowing machinery that is suitable for intercropping methods and integrates multi-dimensional parameters is crucial to overcoming the bottlenecks in intercropping sowing. Summary of the Invention

[0004] To address this issue, the present invention provides an automated control system for agricultural sowing machinery based on the planting environment, which overcomes the problem of low sowing quality caused by the single sensing dimension in the prior art, making it difficult to accurately determine the intercropping sowing parameters.

[0005] To achieve the above objectives, the present invention provides an automated control system for agricultural seeding machinery based on the farming environment, comprising:

[0006] The data acquisition module is used to collect environmental data of the target planting area, soil data of several collection points, and previous season crop data of several previous season crops.

[0007] The soil analysis module, which is connected to the data acquisition module, is used to divide the target planting area into several planting sub-regions based on the soil data of the target planting area, and to determine the soil condition of a single planting sub-region based on the soil data of a single planting sub-region.

[0008] An environmental analysis module, which is connected to the data acquisition module, is used to predict environmental data for a future preset time period based on environmental data within a target time period, and to determine the environmental impact index based on the environmental data within the target time period and the environmental data for the future preset time period.

[0009] The crop analysis module is connected to the data acquisition module and the soil analysis module respectively. It is used to determine the crop distribution characteristics of a single planting sub-region based on the previous crop data of several previous crops in a single planting sub-region, and to determine the edge influence index corresponding to each adjacent planting sub-region based on the previous crop data of each previous crop in adjacent planting sub-regions. The crop distribution characteristics include aboveground distribution characteristics and belowground distribution characteristics.

[0010] The sowing analysis module is connected to the soil analysis module, the environmental analysis module, and the crop analysis module, respectively. It is used to determine the initial sowing parameters of a single planting sub-region based on the soil condition and crop distribution characteristics of the single planting sub-region, and to determine whether to adjust the initial sowing parameters of the single planting sub-region based on the environmental impact index and the edge impact index corresponding to each adjacent planting sub-region, so as to obtain the target sowing parameters of the single planting sub-region.

[0011] Furthermore, the soil analysis module includes:

[0012] The regional division submodule, which is connected to the data acquisition module, is used to cluster the soil data of each acquisition point and divide the target planting area into several planting sub-regions based on the clustering results.

[0013] The state analysis submodule is connected to both the data acquisition module and the region division submodule. It is used to determine the soil evaluation index corresponding to a single planting subregion based on the soil data within that subregion, thereby determining the soil state of the single planting subregion.

[0014] Furthermore, the environmental analysis module includes:

[0015] The current analysis submodule, which is connected to the data acquisition module, is used to determine the current impact characterization value based on environmental data within the target time period;

[0016] The predictive analysis submodule, which is connected to the data acquisition module, is used to predict environmental data for a future preset time period based on environmental data within the target time period, and to determine the predictive impact characterization value.

[0017] The comprehensive analysis submodule is connected to both the current analysis submodule and the predictive analysis submodule, and is used to determine the environmental impact index based on the current impact characterization value and the predicted impact characterization value.

[0018] Furthermore, the crop analysis module includes:

[0019] The aboveground distribution analysis submodule is connected to the data acquisition module and the soil analysis module respectively, and is used to determine the crop growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region, so as to determine the aboveground distribution characteristics of a single planting sub-region.

[0020] The underground distribution analysis submodule is connected to the data acquisition module and the soil analysis module, respectively, and is used to determine the root growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region, so as to determine the underground distribution characteristics of a single planting sub-region.

[0021] Furthermore, the crop analysis module includes:

[0022] The edge analysis submodule, which is connected to the data acquisition module and the soil analysis module respectively, is used to determine the edge influence index corresponding to each adjacent planting sub-region based on the comparison results of the previous season's crop data of each previous season's crop in the adjacent areas of any adjacent planting sub-region.

[0023] Furthermore, the seeding analysis module includes:

[0024] An initial analysis submodule, which is connected to the soil analysis module and the crop analysis module respectively, is used to determine the soil influencing factors of a single planting area based on the soil condition of a single planting sub-area, determine the crop influencing factors of a single planting area based on the crop distribution characteristics of a single planting sub-area, and determine the initial sowing parameters of a single planting sub-area based on the soil influencing factors, the crop influencing factors, and the standard sowing parameters.

[0025] The determination adjustment submodule, which is connected to the initial analysis submodule, the environmental analysis module, and the crop analysis module, is used to determine whether to adjust the initial sowing parameters based on the environmental impact index and the edge impact index, so as to obtain the target sowing parameters.

[0026] Furthermore, the state analysis submodule determines the soil evaluation value corresponding to each collection point based on the soil data of each collection point within a single planting sub-region, and determines the soil evaluation index corresponding to a single planting sub-region based on the soil evaluation value corresponding to each collection point within a single planting sub-region.

[0027] Furthermore, the state analysis submodule determines the soil state of a single planting sub-region based on the comparison results between the soil evaluation index corresponding to the single planting sub-region and the preset evaluation index, including the soil strong influence state and the soil weak influence state.

[0028] Furthermore, the aboveground distribution analysis submodule determines the crop growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region and a preset crop growth model, and determines the aboveground distribution characterization value corresponding to each previous crop based on the crop growth status of each previous crop, so as to determine the aboveground distribution characteristics of a single planting sub-region.

[0029] Furthermore, the underground distribution analysis submodule determines the root growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region and a preset root growth model, and determines the underground distribution characterization value corresponding to each previous crop based on the root growth status of each previous crop, so as to determine the underground distribution characteristics of a single planting sub-region.

[0030] Compared with existing technologies, the beneficial effects of this invention are as follows: By setting up a data acquisition module, this invention can achieve multi-dimensional data perception, breaking through the limitations of traditional sowing systems that only collect single soil or environmental data. It incorporates previous season crop parameters into the collection scope, providing a comprehensive and accurate data foundation for subsequent analysis. By setting up a soil analysis module, the target planting area is divided into several sub-regions based on soil data, enabling refined management. Based on soil data within each sub-region, the soil condition of each sub-region is accurately assessed, laying the foundation for subsequent adjustments to regional sowing parameters and improving land resource utilization. By setting up an environmental analysis module, current and future environmental data are comprehensively considered to determine the environmental impact index, reducing the impact of environmental risks on sowing quality. By setting up a crop analysis module, based on data from several previous season crops within a single planting sub-region, the aboveground and belowground distribution characteristics of crops are determined. This comprehensively assesses the physical disturbance and resource competition impact of previous season crops on subsequent planting, improving the local adaptability of parameter adjustments. In intercropping, previous season crops in adjacent planting sub-regions may have interactive effects such as cross-shading and root entanglement. By determining the edge impact index corresponding to each adjacent planting sub-region, the degree of crop interference between adjacent planting sub-regions is quantified, providing a basis for the sowing analysis module to adjust sowing parameters in edge regions. By setting up a sowing analysis module, based on the soil conditions and crop distribution characteristics of a single planting sub-region, initial sowing parameters are determined, improving the basic adaptability of sowing parameters. Combined with environmental impact index and edge impact index, the initial sowing parameters are dynamically adjusted, enabling precise determination of sowing parameters, improving sowing quality, and enhancing the automation level of agricultural production.

[0031] Furthermore, the soil analysis module of this invention, by setting up a regional division submodule, performs cluster analysis on soil data from each collection point, dividing complex planting areas into sub-regions with similar soil characteristics, thereby achieving refined management and improving resource utilization efficiency. By setting up a state analysis submodule, it comprehensively evaluates various soil indicators to determine the soil evaluation index corresponding to a single planting sub-region, thus accurately quantifying and assessing soil condition, improving management efficiency and scientific rigor.

[0032] Furthermore, the environmental analysis module of this invention, by setting a current analysis submodule, determines the current impact characterization value based on environmental data within a target time period, comprehensively reflecting the actual environmental state at the time of sowing and quickly assessing the impact of the current environment on agricultural production. By setting a predictive analysis submodule, it predicts environmental data for a future preset time period based on the environmental data within the target time period and determines the predicted impact characterization value, comprehensively reflecting the future environmental state at the time of sowing. By predicting future environmental changes, its impact on agricultural production can be assessed in advance. By setting a comprehensive analysis submodule, it determines an environmental impact index based on the current and predicted impact characterization values, integrating the impacts of the immediate and future environments into a unified environmental impact index, providing a scientific basis for adjusting sowing parameters and ensuring the timeliness and adaptability of management measures.

[0033] Furthermore, the crop analysis module of this invention, by setting up a aboveground distribution analysis submodule, quantitatively assesses the crop growth status of each previous season's crop based on the previous season's crop data within a single planting sub-region, thereby accurately analyzing the aboveground distribution characteristics of a single planting sub-region and accurately capturing aboveground distribution differences at the planting sub-region scale. By setting up a sub-module for underground distribution analysis, it quantitatively assesses the root growth status of each previous season's crop based on the previous season's crop data within a single planting sub-region, achieving an indirect quantitative assessment of root system influence, thereby accurately analyzing the underground distribution characteristics of a single planting sub-region and accurately capturing underground distribution differences at the planting sub-region scale. This provides data guidance for subsequent sowing strategies and further improves sowing quality.

[0034] Furthermore, the crop analysis module of this invention, by setting up an edge analysis sub-module, analyzes and determines the edge influence index corresponding to each adjacent planting sub-region, focuses on the differences in crop parameters between adjacent planting sub-regions, quantifies the edge interaction influence, avoids conflicts between local edge adjustments and overall layout, reduces the negative impact of edge effects, optimizes the sowing layout, and further improves sowing quality.

[0035] Furthermore, the sowing analysis module of this invention, by setting an initial analysis submodule, comprehensively considers soil and crop factors to generate reasonable initial sowing parameters for each planting sub-region. Based on the initial sowing parameters, it integrates the environmental and edge interaction effects for secondary optimization, thereby accurately determining the sowing parameters and improving sowing quality. Attached Figure Description

[0036] Figure 1 This is a structural block diagram of an automated control system for agricultural sowing machinery based on the farming environment, according to an embodiment of the present invention.

[0037] Figure 2 This is a structural block diagram of the soil analysis module according to an embodiment of the present invention;

[0038] Figure 3 This is a structural block diagram of the environmental analysis module in an embodiment of the present invention;

[0039] Figure 4 This is a structural block diagram of the crop analysis module in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0041] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0042] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0043] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0044] Please see Figures 1-4 As shown, Figure 1 This is a structural block diagram of an automated control system for agricultural sowing machinery based on the farming environment, according to an embodiment of the present invention. Figure 2 This is a structural block diagram of the soil analysis module according to an embodiment of the present invention; Figure 3 This is a structural block diagram of the environmental analysis module in an embodiment of the present invention; Figure 4 This is a structural block diagram of the crop analysis module in an embodiment of the present invention; an embodiment of the present invention provides an automated control system for agricultural sowing machinery based on the planting environment, comprising:

[0045] The data acquisition module is used to collect environmental data of the target planting area, soil data of several collection points, and previous season crop data of several previous season crops.

[0046] In implementation, the soil data includes soil particle size distribution, soil porosity, soil moisture, and soil bulk density; the environmental data includes ambient temperature, ambient humidity, and light intensity; and the previous season's crop data includes plant height, stem diameter, plant spacing, row spacing, and leaf area. Data preprocessing is performed on the environmental data of the target planting area, the soil data from several collection points, and the previous season's crop data for several previous seasons. This preprocessing includes at least data cleaning and standardization, mapping the data to the range of 0 to 1 to eliminate differences in dimensions and magnitudes between different data.

[0047] It is understandable that no specific limitations are made on the equipment and methods for collecting environmental data of the target planting area, soil data of several collection points, and data of previous season crops, as these are existing technologies and will not be elaborated further.

[0048] It is understandable that in the target planting area, some previous crops are intercropped and the next crop is sown in the later stage of the previous crop's growth. For example, previous crop: tomato, next crop: cabbage; previous crop: potato, next crop: Chinese cabbage.

[0049] Understandably, several collection points are evenly set up within the target planting area. The number of collection points is positively correlated with the area of ​​the target planting area. Generally, collection points can be set at the middle of any adjacent previous season's crop. The target planting area is evenly divided into several grids based on each collection point, with the collection point being the center of the grid.

[0050] The soil analysis module, which is connected to the data acquisition module, is used to divide the target planting area into several planting sub-regions based on the soil data of the target planting area, and to determine the soil condition of a single planting sub-region based on the soil data of a single planting sub-region.

[0051] Specifically, the soil analysis module includes:

[0052] The regional division submodule, which is connected to the data acquisition module, is used to cluster the soil data of each acquisition point and divide the target planting area into several planting sub-regions based on the clustering results.

[0053] In implementation, soil data from each collection point is clustered to obtain several cluster groups, each containing soil data from at least one collection point. For any collection point within a cluster group, it is further clustered based on its location to obtain several sub-clusters, each containing at least one collection point. The target planting area is then divided into several planting sub-regions based on each cluster group. Each cluster group corresponds to one planting sub-region, which is the largest area formed by connecting the grids corresponding to the collection points within the cluster group.

[0054] The state analysis submodule is connected to both the data acquisition module and the region division submodule. It is used to determine the soil evaluation index corresponding to a single planting subregion based on the soil data within that subregion, thereby determining the soil state of the single planting subregion.

[0055] Specifically, the state analysis submodule determines the soil evaluation value corresponding to each collection point based on the soil data of each collection point within a single planting sub-region, and determines the soil evaluation index corresponding to a single planting sub-region based on the soil evaluation value corresponding to each collection point within a single planting sub-region.

[0056] Specifically, the state analysis submodule determines the soil state of a single planting sub-region based on the comparison between the soil evaluation index corresponding to the single planting sub-region and the preset evaluation index, including the soil strong influence state and the soil weak influence state.

[0057] In practice, for any collection point in any planting sub-region: TY1, TY2, ..., TY j , ..., TY m Standard soil data: TE1, TE2, ..., TE j , ..., TE m Then the soil evaluation value TP corresponding to this sampling point is (∑ m j＝1 TY j ×TE j ) / (sqrt(∑ m j＝1 (TY j ) 2 )×sqrt(∑ m j＝1 (TE j ) 2 `sqrt()` is a preset square root determination function, where `j` = 1, 2, ..., `m`, and `m` represents the number of soil data points collected. Practitioners can set standard soil data based on actual conditions or the average of soil data from subsequent crop sowing times that passed qualification tests in historical data.

[0058] It is understandable that, for any planting sub-region, the average soil evaluation value corresponding to each collection point within that planting sub-region is determined as the soil evaluation index corresponding to that planting sub-region.

[0059] Understandably, for any planting sub-region, if the soil evaluation index corresponding to that sub-region is greater than the preset evaluation index, it indicates that the current soil condition in that sub-region has a positive impact on the growth of the subsequent crop, and the soil condition of that sub-region is determined to be a state of weak soil influence. If the soil evaluation index corresponding to that sub-region is less than or equal to the preset evaluation index, it indicates that the current soil condition in that sub-region has a negative impact on the growth of the subsequent crop, and the soil condition of that sub-region is determined to be a state of strong soil influence. Practitioners can set the preset evaluation index based on actual conditions; preferably, the preset evaluation index is set to a range of 0.7 to 0.8.

[0060] This invention's soil analysis module, through a regional division submodule, performs cluster analysis on soil data from various collection points, dividing complex planting areas into sub-regions with similar soil characteristics. This enables refined management and improves resource utilization efficiency. Furthermore, by setting up a state analysis submodule, it comprehensively evaluates various soil indicators to determine the soil evaluation index corresponding to each individual planting sub-region. This allows for precise quantitative assessment of soil condition, improving management efficiency and scientific rigor.

[0061] An environmental analysis module, which is connected to the data acquisition module, is used to predict environmental data for a future preset time period based on environmental data within a target time period, and to determine the environmental impact index based on the environmental data within the target time period and the environmental data for the future preset time period.

[0062] Specifically, the environmental analysis module includes:

[0063] The current analysis submodule, which is connected to the data acquisition module, is used to determine the current impact characterization value based on environmental data within the target time period;

[0064] The predictive analysis submodule, which is connected to the data acquisition module, is used to predict environmental data for a future preset time period based on environmental data within the target time period, and to determine the predictive impact characterization value.

[0065] The comprehensive analysis submodule is connected to both the current analysis submodule and the predictive analysis submodule, and is used to determine the environmental impact index based on the current impact characterization value and the predicted impact characterization value.

[0066] During implementation, the environmental data collected at the i-th collection time point within the target time period is: HY i,1 HY i,2 , ..., HY i,g , ..., HY i,h Standard environmental data: HE1, HE2, ..., HE g HE hThen the environmental impact characterization value TP corresponding to the i-th collection time point i =(∑ h g＝1 HY i,g ×HE g ) / (sqrt(∑ h g＝1 (HY i,g ) 2 )×sqrt(∑ h g＝1 (HE g ) 2 )); where i = 1, 2, ..., n; g = 1, 2, ..., h; n is the number of time points collected within the target time period, and h is the number of environmental data points. Then, the current impact characterization value HP = (∑ n i＝1 TP i ) / n. Practitioners can set standard environmental data based on actual conditions or the average values ​​of various environmental data at the time of sowing of subsequent crops that passed compliance inspections in historical data.

[0067] It is understood that an environmental training dataset is constructed based on environmental data from the sowing of later-season crops in historical data, and an initial environmental prediction model is trained to obtain a target environmental prediction model. Environmental data within the target time period is input into the target environmental prediction model to obtain the output environmental data within a future preset time period. It should be noted that those skilled in the art know that any prediction model in the prior art that can predict environmental data within a future preset time period falls within the protection scope of this invention, and will not be elaborated further here.

[0068] It is understandable that the environmental data within the future preset time period: WY1, WY2, ..., WY g , ..., WY h Standard environmental data: HE1, HE2, ..., HE g HE h Then the predicted impact characterization value WP = (∑ h g＝1 WY g ×HE g ) / (sqrt(∑ h g＝1 (WY g ) 2 )×sqrt(∑ h g＝1 (HE g ) 2 )).

[0069] It is understandable that the ratio of the current impact characterization value to the predicted impact characterization value is determined as the environmental impact index.

[0070] It is understood that, preferably, the target time period is set to a range of 12h to 24h, and the preset time period is set to a range of 3h to 5h.

[0071] The environmental analysis module of this invention, through setting a current analysis submodule, determines the current impact characterization value based on environmental data within a target time period, comprehensively reflecting the actual environmental state at the time of sowing and quickly assessing the impact of the current environment on agricultural production. Through setting a predictive analysis submodule, it predicts environmental data for a future preset time period based on the environmental data within the target time period and determines the predicted impact characterization value, comprehensively reflecting the future environmental state at the time of sowing. By predicting future environmental changes, its impact on agricultural production can be assessed in advance. Through setting a comprehensive analysis submodule, an environmental impact index is determined based on the current and predicted impact characterization values, integrating the impacts of the immediate and future environments into a unified environmental impact index. This provides a scientific basis for adjusting sowing parameters, ensuring the timeliness and adaptability of management measures.

[0072] The crop analysis module is connected to the data acquisition module and the soil analysis module respectively. It is used to determine the crop distribution characteristics of a single planting sub-region based on the previous crop data of several previous crops in a single planting sub-region, and to determine the edge influence index corresponding to each adjacent planting sub-region based on the previous crop data of each previous crop in adjacent planting sub-regions. The crop distribution characteristics include aboveground distribution characteristics and belowground distribution characteristics.

[0073] Specifically, the crop analysis module includes:

[0074] The aboveground distribution analysis submodule is connected to the data acquisition module and the soil analysis module respectively, and is used to determine the crop growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region, so as to determine the aboveground distribution characteristics of a single planting sub-region.

[0075] Specifically, the aboveground distribution analysis submodule determines the crop growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region and a preset crop growth model, and determines the aboveground distribution characterization value corresponding to each previous crop based on the crop growth status of each previous crop, so as to determine the aboveground distribution characteristics of a single planting sub-region.

[0076] In practice, the complete growth process of the previous season crop is simulated based on the previous season crop data of the same type of previous season crop growth stage in historical data, so as to construct a preset crop growth model (e.g., Logistic growth model, Gompertz growth model, etc.). The previous season crop data of any previous season crop in any planting sub-region is input into the preset crop growth model to obtain the crop growth status of the previous season crop. In practice, implementers can assign values ​​to the crop growth status at each stage of the previous season's complete growth process (e.g., emergence, flowering, maturity, and withering) (e.g., dividing the emergence stage into three crop growth states with values ​​of 0, 0.5, and 1; dividing the flowering stage into four crop growth states with values ​​of 1, 2, 3, and 4; dividing the maturity stage into five crop growth states with values ​​of 4, 5.5, 7, 8.5, and 10; and dividing the withering stage into six crop growth states with values ​​of 10, 12, 14, 16, 18, and 20) to determine the aboveground distribution characteristic value corresponding to each previous season's crop. Based on the distribution of the aboveground distribution characteristic values ​​corresponding to each previous season's crop within a single planting sub-region, the aboveground distribution characteristics of a single planting sub-region can be determined.

[0077] The underground distribution analysis submodule is connected to the data acquisition module and the soil analysis module, respectively, and is used to determine the root growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region, so as to determine the underground distribution characteristics of a single planting sub-region.

[0078] Specifically, the underground distribution analysis submodule determines the root growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region and a preset root growth model, and determines the underground distribution characterization value corresponding to each previous crop based on the root growth status of each previous crop, so as to determine the underground distribution characteristics of a single planting sub-region.

[0079] In practice, the root growth process of the previous season crop is simulated based on the previous season crop data of the same type of previous season crop growth stage in historical data, so as to construct a preset root growth model (e.g., Logistic growth model, Gompertz growth model, etc.). The previous season crop data of any previous season crop in any planting sub-region is input into the preset root growth model to obtain the root growth status of the previous season crop. In practice, implementers can assign quantitative values ​​to the root growth status of the previous season's crop at each stage of root growth (e.g., seed germination, primary root growth, lateral root growth, root maturity, and root senescence). For example, the seed germination stage can be divided into two root growth states with values ​​of 0 and 0.2; the primary root growth stage into three root growth states with values ​​of 0.2, 0.6, and 1; the lateral root growth stage into four root growth states with values ​​of 1, 2, 3, and 4; the root maturity stage into five root growth states with values ​​of 4, 5.5, 7, 8.5, and 10; and the root senescence stage into six root growth states with values ​​of 10, 12, 14, 16, 18, and 20. This will determine the underground distribution characteristic value corresponding to each previous season's crop. Based on the distribution of the underground distribution characteristic values ​​corresponding to each previous season's crop within a single planting sub-region, the underground distribution characteristics of a single planting sub-region can be determined.

[0080] This invention's crop analysis module, through the establishment of a aboveground distribution analysis submodule, quantitatively assesses the growth status of each previous season's crop within a single planting sub-region based on previous season's crop data. This allows for precise analysis of the aboveground distribution characteristics of a single planting sub-region, accurately capturing differences in aboveground distribution at the planting sub-region scale. Furthermore, by establishing a sub-module for underground distribution analysis, it quantitatively assesses the root growth status of each previous season's crop within a single planting sub-region based on previous season's crop data. This enables indirect quantitative assessment of root system influence, thereby accurately analyzing the underground distribution characteristics of a single planting sub-region and precisely capturing differences in underground distribution at the planting sub-region scale. This provides data guidance for subsequent sowing strategies, further improving sowing quality.

[0081] Specifically, the crop analysis module includes:

[0082] The edge analysis submodule, which is connected to the data acquisition module and the soil analysis module respectively, is used to determine the edge influence index corresponding to each adjacent planting sub-region based on the comparison results of the previous season's crop data of each previous season's crop in the adjacent areas of any adjacent planting sub-region.

[0083] In practice, the adjacent area is the boundary zone between any two adjacent planting sub-regions. For example, for herbaceous crops, the boundary zone is 0.5m to 1m, and for woody crops, it is 2m to 3m. This avoids the ambiguity of the boundary effect. For example, if planting sub-region A and planting sub-region B are adjacent, the boundary zone AB (1m on side A + 1m on side B) is the "adjacent area" for this comparison. It is ensured that the number of previous season crops in side A and side B is the same.

[0084] Understandably, for adjacent planting sub-regions, planting sub-region A and planting sub-region B, the edge influence index corresponding to planting sub-region A and planting sub-region B is determined based on the comparison results of the aboveground distribution characteristic values ​​and belowground distribution characteristic values ​​of each previous season's crop within planting sub-region A and planting sub-region B. For example, the aboveground distribution characteristic values ​​of each previous season's crop within planting sub-region A are: AY1, AY2, ..., AY2. u , ...,AY v Aboveground distribution characteristic values ​​of each previous season's crop within planting sub-region B: BY1, BY2, ..., BY u ... BY v The underground distribution characteristic values ​​of each previous season's crop within planting sub-region A: AE1, AE2, ..., AE u , ..., AE v The underground distribution characteristic values ​​of each previous season's crop within planting sub-region B: BE1, BE2, ..., BE u , ..., BE v Then the influence value on the edge ground is QY = (∑ v u＝1 AY u ×BY u ) / (sqrt(∑ v u＝1 (AY u ) 2 )×sqrt(∑ v u＝1 (BY u ) 2 ), Marginal underground influence value SE=(∑ v u＝1 AE u ×BE u ) / (sqrt(∑ v u＝1 (AE u ) 2 )×sqrt(∑ v u＝1 (BE u ) 2Let u = 1, 2, ..., v, where v is the quantity of previous season's crops in planting sub-region A / planting sub-region B. The mean of the edge aboveground influence value and the edge underground influence value is determined as the edge influence index corresponding to each adjacent planting sub-region.

[0085] The crop analysis module of this invention, by setting up an edge analysis sub-module, analyzes and determines the edge influence index corresponding to each adjacent planting sub-region, focuses on the differences in crop parameters between adjacent planting sub-regions, quantifies the edge interaction influence, avoids conflicts between local edge adjustments and overall layout, reduces the negative impact of edge effects, optimizes the sowing layout, and further improves sowing quality.

[0086] The sowing analysis module is connected to the soil analysis module, the environmental analysis module, and the crop analysis module, respectively. It is used to determine the initial sowing parameters of a single planting sub-region based on the soil condition and crop distribution characteristics of the single planting sub-region, and to determine whether to adjust the initial sowing parameters of the single planting sub-region based on the environmental impact index and the edge impact index corresponding to each adjacent planting sub-region, so as to obtain the target sowing parameters of the single planting sub-region.

[0087] Specifically, the seeding analysis module includes:

[0088] An initial analysis submodule, which is connected to the soil analysis module and the crop analysis module respectively, is used to determine the soil influencing factors of a single planting area based on the soil condition of a single planting sub-area, determine the crop influencing factors of a single planting area based on the crop distribution characteristics of a single planting sub-area, and determine the initial sowing parameters of a single planting sub-area based on the soil influencing factors, the crop influencing factors, and the standard sowing parameters.

[0089] In practice, implementers can assign and quantify the soil condition of the planting sub-area according to the actual situation. For example, if the soil condition of a single planting sub-area is a strong soil influence state, the soil influence factor of the planting area is set to 0.7. If the soil condition of a single planting sub-area is a weak soil influence state, the soil influence factor of the planting area is set to 0.3. The sum of the soil influence factor corresponding to the strong soil influence state and the soil influence factor corresponding to the weak soil influence state is 1.

[0090] It is understandable that the ratio of the aboveground distribution characteristic value to the underground distribution characteristic value of each previous crop in a single planting sub-region is determined as the crop influence coefficient of each previous crop, and the mean of the crop influence coefficients of each previous crop in a single planting sub-region is determined as the crop influence factor of that planting sub-region.

[0091] It is understandable that the product of soil influencing factors and crop influencing factors is determined as the comprehensive influence coefficient, and the product of the comprehensive influence coefficient and the standard sowing parameters is determined as the initial sowing parameters for a single planting sub-region.

[0092] It is understood that sowing parameters include, but are not limited to, sowing spacing (distance from the nearest previous crop) and sowing depth. For example, the initial sowing spacing for a single planting sub-region can be determined by multiplying any comprehensive influence coefficient by the standard sowing spacing. Practitioners can set standard sowing parameters based on actual conditions or the average sowing parameters of subsequent crops that have passed compliance testing in historical data.

[0093] The determination adjustment submodule, which is connected to the initial analysis submodule, the environmental analysis module, and the crop analysis module, is used to determine whether to adjust the initial sowing parameters based on the environmental impact index and the edge impact index, so as to obtain the target sowing parameters.

[0094] Specifically, the determination and adjustment submodule determines whether to adjust the initial sowing parameters to obtain the target sowing parameters based on the comparison results of the environmental impact index and the standard environmental index, and the comparison results of the edge impact index and the preset edge index.

[0095] During implementation, if the environmental impact index is greater than the standard environmental index, or the edge impact index is greater than the preset edge index, the initial seeding parameters will be adjusted. Specifically, if only the environmental impact index is greater than the standard environmental index, the product of the environmental impact index and the initial seeding parameters will be used as the target seeding parameters. If only the edge impact index is greater than the preset edge index, the product of the edge impact index and the initial seeding parameters will be used as the target seeding parameters for adjacent areas, and no adjustments will be made for areas outside the adjacent areas. If both the environmental impact index and the edge impact index are greater than the preset edge index, the product of the environmental impact index, the edge impact index, and the initial seeding parameters will be used as the target seeding parameters for adjacent areas, and for areas outside the adjacent areas, the product of the environmental impact index and the initial seeding parameters will be used as the target seeding parameters.

[0096] The sowing analysis module of this invention generates reasonable initial sowing parameters for each planting sub-region by setting an initial analysis sub-module, taking into account soil and crop factors. Based on the initial sowing parameters, it performs secondary optimization by integrating environmental and edge interaction effects to accurately determine the sowing parameters, thereby improving sowing quality.

[0097] This invention, by incorporating a data acquisition module, enables multi-dimensional data perception, overcoming the limitations of traditional sowing systems that only collect single soil or environmental data. It includes parameters from previous season crops in the data collection scope, providing a comprehensive and accurate data foundation for subsequent analysis. The soil analysis module divides the target planting area into several sub-regions based on soil data, enabling refined management. Based on soil data within each sub-region, it accurately assesses the soil condition of each sub-region, laying the foundation for subsequent adjustments to sowing parameters and improving land resource utilization. The environmental analysis module comprehensively considers current and future environmental data to determine the environmental impact index, reducing the impact of environmental risks on sowing quality. The crop analysis module, based on data from several previous season crops within a single sub-region, determines the aboveground and belowground distribution characteristics of crops, comprehensively assessing the physical interference and resource competition impact of previous season crops on subsequent sowing, improving the local adaptability of parameter adjustments. In intercropping, previous season crops in adjacent sub-regions may interact through cross-shading, root intertwining, and other factors. By determining the edge impact index corresponding to each adjacent sub-region, the degree of crop interference between adjacent sub-regions is quantified, providing a basis for the sowing analysis module to adjust sowing parameters in edge regions. By setting up a sowing analysis module, initial sowing parameters can be determined based on the soil conditions and crop distribution characteristics of a single planting sub-region, improving the basic adaptability of sowing parameters. By combining environmental impact index and edge impact index, the initial sowing parameters can be dynamically adjusted, enabling precise determination of sowing parameters, improving sowing quality and the level of automation in agricultural production.

[0098] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An automated control system for agricultural seeding machinery based on the farming environment, characterized in that, include: The data acquisition module is used to collect environmental data of the target planting area, soil data of several collection points, and previous season crop data of several previous season crops. The soil analysis module, which is connected to the data acquisition module, is used to divide the target planting area into several planting sub-areas based on the soil data of the target planting area, and to determine the soil condition of a single planting sub-area based on the soil data of a single planting sub-area. An environmental analysis module, which is connected to the data acquisition module, is used to predict environmental data for a future preset time period based on environmental data within a target time period, and to determine the environmental impact index based on the environmental data within the target time period and the environmental data for the future preset time period. The crop analysis module is connected to the data acquisition module and the soil analysis module respectively. It is used to determine the crop distribution characteristics of a single planting sub-region based on the previous crop data of several previous crops in a single planting sub-region, and to determine the edge influence index corresponding to each adjacent planting sub-region based on the previous crop data of each previous crop in adjacent planting sub-regions. The crop distribution characteristics include aboveground distribution characteristics and belowground distribution characteristics. The sowing analysis module is connected to the soil analysis module, the environmental analysis module, and the crop analysis module, respectively. It is used to determine the initial sowing parameters of a single planting sub-region based on the soil condition and crop distribution characteristics of the single planting sub-region, and to determine whether to adjust the initial sowing parameters of the single planting sub-region based on the environmental impact index and the edge impact index corresponding to each adjacent planting sub-region, so as to obtain the target sowing parameters of the single planting sub-region.

2. The automated control system for agricultural seeding machinery based on the farming environment according to claim 1, characterized in that, The soil analysis module includes: The regional division submodule, which is connected to the data acquisition module, is used to cluster the soil data of each acquisition point and divide the target planting area into several planting sub-regions based on the clustering results. The state analysis submodule is connected to both the data acquisition module and the region division submodule. It is used to determine the soil evaluation index corresponding to a single planting subregion based on the soil data within that subregion, thereby determining the soil state of the single planting subregion.

3. The automated control system for agricultural seeding machinery based on the farming environment according to claim 2, characterized in that, The environmental analysis module includes: The current analysis submodule, which is connected to the data acquisition module, is used to determine the current impact characterization value based on environmental data within the target time period; The predictive analysis submodule, which is connected to the data acquisition module, is used to predict environmental data for a future preset time period based on environmental data within the target time period, and determine the predictive impact characterization value. The comprehensive analysis submodule is connected to both the current analysis submodule and the predictive analysis submodule, and is used to determine the environmental impact index based on the current impact characterization value and the predicted impact characterization value.

4. The automated control system for agricultural seeding machinery based on the farming environment according to claim 3, characterized in that, The crop analysis module includes: The aboveground distribution analysis submodule is connected to the data acquisition module and the soil analysis module respectively, and is used to determine the crop growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region, so as to determine the aboveground distribution characteristics of a single planting sub-region. The underground distribution analysis submodule is connected to the data acquisition module and the soil analysis module, respectively, and is used to determine the root growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region, so as to determine the underground distribution characteristics of a single planting sub-region.

5. The automated control system for agricultural seeding machinery based on the farming environment according to claim 4, characterized in that, The crop analysis module includes: The edge analysis submodule, which is connected to the data acquisition module and the soil analysis module respectively, is used to determine the edge influence index corresponding to each adjacent planting sub-region based on the comparison results of the previous season's crop data of each previous season's crop in the adjacent areas of any adjacent planting sub-region.

6. The automated control system for agricultural seeding machinery based on the farming environment according to claim 5, characterized in that, The seeding analysis module includes: An initial analysis submodule, which is connected to the soil analysis module and the crop analysis module respectively, is used to determine the soil influencing factors of a single planting area based on the soil condition of a single planting sub-area, determine the crop influencing factors of a single planting area based on the crop distribution characteristics of a single planting sub-area, and determine the initial sowing parameters of a single planting sub-area based on the soil influencing factors, the crop influencing factors, and the standard sowing parameters. The determination adjustment submodule, which is connected to the initial analysis submodule, the environmental analysis module, and the crop analysis module, is used to determine whether to adjust the initial sowing parameters based on the environmental impact index and the edge impact index, so as to obtain the target sowing parameters.

7. The automated control system for agricultural seeding machinery based on the farming environment according to claim 6, characterized in that, The state analysis submodule determines the soil evaluation value corresponding to each collection point based on the soil data of each collection point within a single planting sub-region, and determines the soil evaluation index corresponding to a single planting sub-region based on the soil evaluation values ​​corresponding to each collection point within a single planting sub-region.

8. The automated control system for agricultural seeding machinery based on the farming environment according to claim 2 or 7, characterized in that, The state analysis submodule determines the soil state of a single planting sub-region, including strong soil influence state and weak soil influence state, based on the comparison results between the soil evaluation index corresponding to a single planting sub-region and the preset evaluation index.

9. The automated control system for agricultural seeding machinery based on the farming environment according to claim 8, characterized in that, The aboveground distribution analysis submodule determines the crop growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region and a preset crop growth model, and determines the aboveground distribution characterization value corresponding to each previous crop based on the crop growth status of each previous crop, so as to determine the aboveground distribution characteristics of a single planting sub-region.

10. The automated control system for agricultural seeding machinery based on the farming environment according to claim 8, characterized in that, The underground distribution analysis submodule determines the root growth status of each previous crop based on the previous crop data of each previous crop in a single planting sub-region and a preset root growth model, and determines the underground distribution characterization value corresponding to each previous crop based on the root growth status of each previous crop, so as to determine the underground distribution characteristics of a single planting sub-region.

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