Soybean and corn intercropping three-dimensional cultivation method

Abstract

The application discloses a soybean and corn interplanting three-dimensional cultivation method, relates to the technical field of soybean and corn interplanting, and scientifically determines the suitability of a land plot through calculation of a soil nutrition index, thereby guaranteeing the basis for the success of interplanting from the source; further, through prediction of the compact growth of corn at the jointing stage, a decision on the spraying of a regulator can be made in advance, the height of corn and the canopy are actively regulated, and in combination with environmental data and plant spacing parameters, the light grade of soybean is accurately predicted; the shading stress of corn on soybean is effectively relieved, an excellent light environment is created for soybean, and photosynthesis and yield formation of soybean are guaranteed; and through real-time monitoring of crop growth and soil data, dynamic analysis is made on the differentiated water and fertilizer requirements of corn and soybean, thereby avoiding resource waste and non-point source pollution. The application effectively solves the problem of interplanting, significantly improves the resource utilization rate, and provides reliable technical support for realizing green, high yield and high efficiency of agriculture.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of soybean and corn intercropping, in particular to a soybean and corn intercropping three-dimensional cultivation method. BACKGROUND

[0002] As an efficient ecological planting mode, soybean and corn intercropping can utilize the complementary effects between crops, but still faces many technical challenges in actual production, especially after the corn enters the jointing stage, the rapid growth of the corn is easy to form shading to the short-stalk soybean, resulting in insufficient light of the soybean and reduced yield. Therefore, an integrated and data-driven technical solution from soil evaluation, accurate seed selection, growth prediction to intelligent water and fertilizer management is needed to maximize the potential of the soybean and corn intercropping mode.

[0003] The prior art such as the invention application patent with the announcement number CN115909076A discloses a corn and soybean intercropping method, device, equipment and medium based on image features, which comprises extracting target corn image features and target soybean image features, and determining the growth stages of the target corn and the target soybean through the target corn image features and the target soybean image features; based on the pre-trained intercropping model and the growth stages of the target corn and the target soybean, the intercropping mode of the corn and the soybean to be planted is determined. The application inputs the images corresponding to the target corn and the target soybean into the preset image feature extraction model, determines the growth stages of the target corn and the target soybean through the output image features, and determines the intercropping mode corresponding to the current growth stage through the pre-trained intercropping model, thereby improving the planting yield of the corn and the soybean based on the image features, and solving the technical problem of low planting yield of the corn and the soybean based on the image features.

[0004] For the above-mentioned scheme, the inventors of the present application find that the above-mentioned technology at least has the following technical problems: 1. Currently, there is a lack of early growth data prediction of the compactness of the plant type at the key growth node (the jointing stage), which cannot realize result prediction, cannot actively determine whether to use chemical control agent for the corn, cannot adjust the plant height and plant type of the corn, and cannot avoid the passive situation of insufficient light in the later period; currently, there is a lack of control of the longitudinal growth of the corn, which cannot ensure that the lower soybean can obtain sufficient light, cannot reasonably control the corn plant to be stronger, and cannot enhance the resistance to lodging.

[0005] 2. Currently, there is a lack of multi-factor fusion of light simulation, which cannot quantitatively output the light level, cannot quantitatively determine the light level of the soybean in the whole growth period, and cannot obtain the light level as an important decision signal; there is a lack of prediction of the light level being continuously insufficient to trigger a series of countermeasures, and there is a lack of adjustment of water and fertilizer, consideration of light supplement or key basis for adjusting the planting scheme next year. SUMMARY

[0006] In view of the above technical deficiencies, the purpose of the present application is to provide a soybean and corn intercropping three-dimensional cultivation method.

[0007] To solve the above technical problems, the present application adopts the following technical solutions: the present application provides a soybean and corn intercropping three-dimensional cultivation method, comprising: step one, soil data analysis: based on the pre-acquired soil data, the soil nutrient index is calculated, and then it is judged whether the soil meets the demand of soybean and corn intercropping.

[0008] Step two, seed selection and planting: based on the preset seed selection standard, the corn and soybean varieties are selected; and based on the preset planting layout parameters, the intercropping seeding density is calculated, and then the planting completion data is obtained.

[0009] Step three, corn growth prediction: based on the pre-acquired corn growth data at the jointing stage, the compact prediction result is obtained, and then the corn spraying demand is analyzed to obtain the corn adjustment decision.

[0010] Step four, soybean light prediction: based on the environmental data, the compact prediction result, the corn inter-planting distance and the soybean inter-planting distance, the soybean light is predicted and analyzed to obtain the light grade.

[0011] Step five, growth water and fertilizer analysis: based on the real-time pre-acquired corn growth data, soybean growth data and soil water and fertilizer data, the water and fertilizer demand is analyzed, and the drip irrigation scheme is generated.

[0012] Preferably, the soil data includes the content of nitrogen element, the content of phosphorus element and the content of potassium element.

[0013] Preferably, the soil data includes the content of nitrogen element, the content of phosphorus element and the content of potassium element.

[0014] Preferably, the soil data includes the content of nitrogen element, the content of phosphorus element and the content of potassium element.

[0015] Preferably, the intercropping sowing density is calculated based on the preset planting layout parameters, and the planting completion data is obtained, including: determining the planting strip direction, intercropping mode and row spacing based on the preset planting layout parameters; and calculating the intercropping sowing density based on the local monoculture density through a preset sowing adjustment algorithm; and then performing planting operation based on the planting layout parameters and the intercropping sowing density to generate the planting completion data.

[0016] Preferably, the compact type prediction result is analyzed based on the pre-obtained corn growth data at the corn jointing stage, including: A1, the compact type prediction probability is calculated by a calculation formula to obtain the compact type prediction probability , , wherein H represents the corn plant height at the corn jointing stage, , wherein LAI represents the corn leaf area index at the corn jointing stage, , wherein e represents a natural constant, , , , and , wherein a represents a regression coefficient.

[0017] A2, the compact type prediction probability is compared with a preset compact type prediction probability threshold value, when the compact type prediction probability is greater than or equal to the preset compact type prediction probability threshold value, the compact type prediction result is 1, and when the compact type prediction probability is less than the preset compact type prediction probability threshold value, the compact type prediction result is 0.

[0018] Preferably, the corn spraying demand is analyzed to obtain the corn adjustment decision, including:

[0019] When the compact type prediction result is 1, the corn is determined as a compact type, and the corn has no spraying demand; when the compact type prediction result is 0, the corn has a spraying demand, and a spraying plant growth regulator decision is generated.

[0020] Preferably, the light illumination of the soybean is predicted and analyzed based on the compact type prediction result, the corn inter-planting distance and the soybean inter-planting distance to obtain the light illumination grade, including: the initial light transmission index is calculated based on the compact type prediction result, the corn inter-planting distance and the soybean inter-planting distance; the environmental factors are obtained through environmental data analysis, and the initial light transmission index is corrected using the environmental factors to obtain the corrected light transmission index, so as to generate the light illumination grade through a preset light illumination grade mapping rule, and the adjustment operation is generated based on the light illumination grade.

[0021] Preferably, the light level is generated by a preset light level mapping rule, and the adjustment operation is generated based on the light level, comprising: B1, comparing the corrected light transmission index with the upper limit value and the lower limit value of the corrected light transmission index, when the corrected light transmission index is greater than or equal to the upper limit value of the corrected light transmission index, the light level is recorded as sufficient; when the corrected light transmission index is less than the upper limit value and greater than or equal to the lower limit value, the light level is recorded as one level of insufficient; when the corrected light transmission index is less than the lower limit value of the corrected light transmission index, the light level is recorded as two levels of insufficient.

[0022] B2, when the light level is sufficient, no operation; when the light level is one level of insufficient, the sparse operation is performed; when the light level is two levels of insufficient, the sparse operation and the potassium dihydrogen phosphate spraying operation are performed at the same time.

[0023] Preferably, the water and fertilizer demand is analyzed based on the real-time pre-acquired corn growth data, soybean growth data and soil water and fertilizer data, and a drip irrigation scheme is generated, comprising: based on the real-time pre-acquired corn growth data, soybean growth data and soil water and fertilizer data, the water and fertilizer demand index is calculated; comparing the water and fertilizer demand index with the preset water and fertilizer demand index threshold value, when the water and fertilizer demand index is greater than or equal to the water and fertilizer demand index threshold value, it is judged that water and fertilizer drip irrigation is needed, and a drip irrigation scheme is generated, otherwise, no water and fertilizer drip irrigation is performed.

[0024] The beneficial effects of the present application are: 1. The soybean and corn intercropping three-dimensional cultivation method provided by the present application scientifically determines the suitability of the plot through the calculation of the soil nutrition index, ensuring the basis for the success of intercropping from the source; further, by predicting the compact growth of corn at the jointing stage, the spraying decision of the regulator can be made in advance, and the height of corn and the canopy are actively controlled, and the light level of soybean is accurately predicted in combination with environmental data and plant spacing parameters; effectively alleviate the shading stress of corn on soybean, create an excellent light environment for soybean, and ensure its photosynthesis and yield formation; and real-time monitoring of crop growth and soil data, dynamic analysis of the differentiated water and fertilizer demand of corn and soybean, avoiding resource waste and non-point source pollution. The present application effectively solves the problem of intercropping, significantly improves the resource utilization rate, and provides reliable technical support for realizing green high yield and high efficiency of agriculture.

[0025] 2. This application analyzes the soil before planting to provide quantitative basis for decision-making; it avoids the waste of resources and risk of yield reduction caused by blindly planting in barren or unsuitable soils, and ensures the foundation for successful intercropping from the source; the clear soil nutrient index provides accurate data support for the base fertilizer formula and application amount before sowing, avoiding fertilizer waste and environmental pollution; through scientific seed selection, it fully utilizes the "edge row advantage" of corn and the nitrogen-fixing and fertilization effect of soybeans, while reducing the competition between the two for light, water and fertilizer. At the same time, the parameterized layout ensures the rationalization of the field population structure, which not only guarantees the light and ventilation of corn, but also leaves the necessary living space for soybeans, laying the foundation for subsequent high yields; "planting completion data" makes each sowing operation traceable, providing an initial data source for realizing precision agriculture and digital management.

[0026] 3. This application uses early growth data to predict the future plant compactness at a critical growth node (jointing stage), enabling outcome prediction and proactively determining whether chemical control agents need to be used on corn to adjust its plant height and shape, rather than remediating after problems occur; proactive intervention effectively avoids the passive situation of insufficient light in the later stages; by controlling the longitudinal growth of corn, it ensures that the soybeans below can receive sufficient light, which is the key to high soybean yield under intercropping; reasonable chemical control makes corn plants stronger, enhances lodging resistance, and is also beneficial to corn itself.

[0027] 4. This application uses a multi-factor fusion light simulation to construct a micro-scale field light distribution prediction. By quantitatively outputting light levels, it accurately quantifies the amount of light that soybeans can obtain throughout the entire growth period, especially during the critical reproductive growth period. This is something that traditional experience cannot do, providing a basis for integrated agronomic measures. The obtained light level is an important decision signal. If the predicted light level is consistently "insufficient," it can trigger a series of countermeasures, such as adjusting water and fertilizer, considering supplemental lighting, or serving as a key basis for adjusting the planting plan for the following year. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating the steps involved in implementing the method described in this application. Detailed Implementation

[0030] With reference to the accompanying drawings and examples of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0031] Please refer to Figure 1 As shown in the drawings, the present application provides a soybean and corn intercropping three-dimensional cultivation method, comprising: step one, soil data analysis: based on the pre-acquired soil data, the soil nutrient index is calculated, and then it is judged whether the soil meets the demand of soybean and corn intercropping.

[0032] In one specific example, the soil data includes the content of nitrogen element, the content of phosphorus element and the content of potassium element.

[0033] It should be noted that the soil data is obtained by a portable soil analyzer.

[0034] In one specific example, the soil data includes the content of nitrogen element, the content of phosphorus element and the content of potassium element.

[0035] It should be noted that the soil data is obtained by a portable soil analyzer.

[0036] It should be noted that based on the pre-acquired soil data, the soil nutrient index is calculated by a preset soil nutrient evaluation method, specifically by a calculation formula The soil nutrient index is obtained , represents the content of nitrogen element in the soil, represents the reference value of the content of nitrogen element in the soil, represents the content of phosphorus element in the soil, represents the reference value of the content of phosphorus element in the soil, represents the content of potassium element in the soil, a content reference value of potassium in the soil, , and a weight factor corresponding to nitrogen in the soil, a weight factor corresponding to phosphorus in the soil, and a weight factor corresponding to potassium in the soil.

[0037] Further, , , , ; the weight factor corresponding to nitrogen in the soil, the weight factor corresponding to phosphorus in the soil, and the weight factor corresponding to potassium in the soil are obtained by a factor analysis method, first, information condensation of nitrogen, phosphorus and potassium in the soil is performed, then a variance explained rate after rotation is obtained, and the weights are obtained by excluding the cumulative variance explained rate.

[0038] It should be noted that the factor analysis method is a known technology, which is a multivariate statistical analysis method of reducing a number of variables with complex relationships to a few comprehensive factors from the dependent relationship of internal correlation of the variables; information condensation means calculating the median; the variance explained rate is the amount of information extracted by factors, and the variance explained rate = eigenvalue / total number of analysis items; the variance explained rate after rotation means the variance explained rate of factors after maximum variance rotation.

[0039] Step two, selecting and planting: selecting corn and soybean varieties based on preset selection standards; and based on preset planting layout parameters, calculating the intercropping seeding density to obtain planting completion data.

[0040] It should be noted that the planting layout parameters include south-north oriented planting belts, 2-row corn and 4-row soybean intercropping, and row spacing of 60-70 cm, etc.

[0041] It should be noted that the planting completion data includes planting time, planting location, crop variety, seeding density information, corn interplanting distance and soybean interplanting distance, etc., and is recorded by a sensor for growth monitoring and decision-making of corn and soybeans.

[0042] In one specific example, the corn and soybean varieties are selected based on preset selection standards, including: the preset selection standards include compact corn plant type, leaf upwelling and small leaf angle, and soybean is shade-tolerant; and according to the preset selection standards, corresponding corn and soybean varieties are selected.

[0043] In one specific example, the intercropping seeding density is calculated based on the preset planting layout parameters, and the planting completion data is obtained, including: determining the planting strip direction, the intercropping mode and the row spacing based on the preset planting layout parameters; and calculating the intercropping seeding density based on the local monoculture density through a preset seeding adjustment algorithm; and then performing the planting operation based on the planting layout parameters and the intercropping seeding density to generate the planting completion data.

[0044] It should be noted that the intercropping seeding density is calculated based on the local monoculture density through a preset seeding adjustment algorithm; the calculation formula is The corn intercropping seeding density is obtained by , wherein represents the local monoculture density of corn, represents the corn adjustment coefficient; the calculation formula is The soybean intercropping seeding density is obtained by , wherein represents the local monoculture density of soybean, represents the soybean adjustment coefficient.

[0045] It should be noted that the corn intercropping seeding density represents the number of corn plants per unit area, which is the optimized corn planting density in the corn-soybean intercropping mode; the local monoculture density of corn represents the number of corn plants per unit area in the local traditional monoculture mode, which is the basic reference density for corn seeding; and the corn adjustment coefficient represents the scaling factor of corn density based on the intercropping mode, which is a parameter for compensating for the impact of intercropping competition. The soybean intercropping seeding density represents the number of soybean plants per unit area, which is the optimized soybean planting density in the intercropping mode; the local monoculture density of soybean represents the number of soybean plants per unit area in the local traditional monoculture mode, which is the basic reference density for soybean seeding; and the soybean adjustment coefficient represents the scaling factor of soybean density based on the intercropping mode, which is a parameter for compensating for the impact of intercropping competition.

[0046] Further, the corn adjustment coefficient and the soybean adjustment coefficient are constants obtained by optimizing historical data based on the preset intercropping mode; for example, for 2-row corn and 4-row soybean intercropping, the value range of the corn adjustment coefficient is 0.6 to 0.8, and the value range of the soybean adjustment coefficient is 0.7 to 0.9, which are determined by the least square method to fit the yield data for many years to maximize the land equivalent ratio and the group photosynthetic efficiency.

[0047] The application analyzes the soil before planting to provide quantitative basis for decision-making; avoids resource waste and yield reduction risk caused by blind planting in infertile or unsuitable soil, guarantees the basis for intercropping success from the source; the clear soil nutrition index provides accurate data support for the formula and application amount of base fertilizer before sowing, avoiding fertilizer waste and environmental pollution; through scientific seed selection, the "edge row advantage" of corn and the nitrogen fixation and fertilization effect of soybean are fully utilized, while the competition of the two for light, water and fertilizer is reduced, and the parameterized layout ensures the rationalization of field population structure, which guarantees the light ventilation of corn and provides necessary survival space for soybean, laying the foundation for subsequent high yield; the "planting completion data" makes each sowing operation traceable, providing an initial data source for realizing precision agriculture and digital management.

[0048] Step three, corn growth prediction: based on the pre-acquired corn growth data at the corn jointing stage, a compact prediction result is analyzed to analyze the corn spraying demand and obtain a corn adjustment decision.

[0049] It should be noted that the corn growth data at the corn jointing stage includes plant height and leaf area index.

[0050] In one specific example, the compact prediction result based on the pre-acquired corn growth data at the corn jointing stage includes: A1, a compact prediction probability is obtained by a calculation formula represents the corn plant height at the corn jointing stage, represents the corn leaf area index at the corn jointing stage, represents a natural constant, and represents a regression coefficient.

[0051] A2, the compact prediction probability is compared with a preset compact prediction probability threshold value, when the compact prediction probability is greater than or equal to the preset compact prediction probability threshold value, the compact prediction result is 1, and when the compact prediction probability is less than the preset compact prediction probability threshold value, the compact prediction result is 0.

[0052] It should be noted that the compact prediction probability represents the possibility of corn growth being compact; the corn plant height at the corn jointing stage represents the vertical height of the corn plant, which is in meters and participates in the calculation in the form of a constant; the corn leaf area index at the corn jointing stage represents the ratio of corn leaf area to land area per unit land area, which is a dimensionless constant; ​​​​​e.g. 2.71828; the regression coefficient is optimized by training on historical corn growth data, specifically optimized on historical data including plant height, leaf area index and corresponding compact classification labels recorded in multi-year variety tests by maximum likelihood estimation method to minimize prediction error; the compact prediction probability threshold is optimized by ROC curve analysis based on historical data to balance the misjudgment rate and adjustment cost to ensure the accuracy of the decision.

[0053] In a specific example, the analysis of the corn spraying requirement to obtain the corn adjustment decision includes: when the compact prediction result is 1, the corn is judged as compact, and the corn has no spraying requirement; when the compact prediction result is 0, the corn has a spraying requirement, and a spraying plant growth regulator decision is generated.

[0054] It should be noted that the spraying plant growth regulator decision includes the type, dose and spraying time of the regulator, etc.; the spraying plant growth regulator is searched in the plant growth regulator in the database; the plant growth regulator is, for example, amidoxy and ethylene.

[0055] The present application uses early growth data to predict the future compactness of plant type at key growth nodes (jointing stage), realizes result prediction, and actively judges whether to use chemical control agent for corn to adjust its plant height and plant type, rather than remedying after the problem occurs; active intervention effectively avoids the passive situation of insufficient light in the later stage; by controlling the longitudinal growth of corn, it ensures that the lower soybeans can obtain sufficient light, which is the key to high yield of soybeans in intercropping mode; reasonable chemical control makes the corn plant more robust, enhances the resistance to lodging, and is also beneficial to the corn itself.

[0056] Step four, soybean light prediction: based on environmental data, compact prediction results, corn intercropping distance and soybean intercropping distance, the soybean light is predicted and analyzed to obtain the light grade.

[0057] It should be noted that the environmental data includes temperature, humidity and wind speed, etc., which are obtained by sensors, such as temperature and humidity obtained by air sensors.

[0058] In a specific example, the prediction and analysis of the soybean light based on the compact prediction results, corn intercropping distance and soybean intercropping distance to obtain the light grade includes: based on the compact prediction results, corn intercropping distance and soybean intercropping distance, the initial light transmission index is calculated; the environmental factors are obtained by environmental data analysis, and then the environmental factors are used to correct the initial light transmission index to obtain the corrected light transmission index, so as to generate the light grade by the preset light grade mapping rule, and generate the adjustment operation based on the light grade.

[0059] It should be noted that the initial light transmission index is calculated based on the compact prediction result, the corn inter-planting distance and the soybean inter-planting distance, and the specific process is as follows: the initial light transmission index is calculated by the following formula . , is represented as the compact prediction result, is represented as the corn inter-planting distance, is represented as the soybean inter-planting distance, , and are respectively represented as the weight factor corresponding to the compact prediction result, the weight factor corresponding to the corn inter-planting distance and the weight factor corresponding to the soybean inter-planting distance.

[0060] It should be noted that the initial light transmission index represents the potential sufficiency of the side light and scattered light transmission to the soybean based on the plant structure and inter-planting distance prediction, quantifies the influence of the corn canopy structure and the soybean planting layout on the light resource allocation, and the higher the value, the greater the light transmission potential; the corn inter-planting distance represents the corn row distance, in meters; the soybean inter-planting distance represents the soybean row distance, in meters; the corn inter-planting distance and the soybean inter-planting distance participate in the calculation in the form of a constant.

[0061] It should be noted that , , , ; the weight factor corresponding to the compact prediction result, the weight factor corresponding to the corn inter-planting distance and the weight factor corresponding to the soybean inter-planting distance are obtained by factor analysis method, first the information condensation of the compact prediction result, the corn inter-planting distance and the soybean inter-planting distance is carried out, then the variance explained after rotation is obtained, and the weight is obtained by excluding the cumulative variance explained.

[0062] It should be noted that the environmental factor is obtained by analyzing the environmental data, and then the initial light transmission index is corrected using the environmental factor to obtain the corrected light transmission index, and the specific process is as follows: the environmental correction factor is obtained by the following formula . , wherein is represented as the number corresponding to the environmental data, , is represented as the total number of environmental data, is represented as the environmental data, is represented as the reference value of the environmental data.

[0063] Further, the corrected light transmission index is equal to the environmental correction factor multiplied by the initial light transmission index; the corrected light transmission index represents the light transmission sufficiency after comprehensively considering the plant structure and the environmental factor.

[0064] In one specific example, the light level is generated by a preset light level mapping rule, and the adjustment operation is generated based on the light level, including: B1, comparing the corrected light transmission index with the upper limit value and the lower limit value of the corrected light transmission index, when the corrected light transmission index is greater than or equal to the upper limit value of the corrected light transmission index, the light level is recorded as sufficient; when the corrected light transmission index is less than the upper limit value and greater than or equal to the lower limit value, the light level is recorded as one level insufficient; when the corrected light transmission index is less than the lower limit value of the corrected light transmission index, the light level is recorded as two levels insufficient.

[0065] It should be noted that the upper limit value and the lower limit value of the corrected light transmission index are obtained by optimizing historical yield data, and are specifically based on the correlation analysis of light transmission index and soybean photosynthetic efficiency in multi-year field trials, and are determined by ROC curve analysis to balance light resource utilization and operation cost; the essence of light level mapping is to discretize the continuous light transmission index into operation decision basis, and the upper limit value and the lower limit value of the corrected light transmission index are adjusted according to the light characteristics of different regions and seasons.

[0066] B2, when the light level is sufficient, no operation; when the light level is one level insufficient, perform thinning operation; when the light level is two levels insufficient, perform thinning operation and potassium dihydrogen phosphate spraying operation at the same time.

[0067] It should be noted that the thinning operation is to adjust the soybean planting density to increase light transmission, which is specifically achieved by reducing the number of plants per unit area; potassium dihydrogen phosphate spraying is used to enhance the shade tolerance and photosynthetic efficiency of soybeans, and the operation parameters are retrieved from a preset database. The specific implementation of thinning operation and spraying operation is based on agricultural management specifications, for example, the density is reduced by 10% to 20% by thinning operation, and the concentration of potassium dihydrogen phosphate spraying is 0.2% to 0.3%, which is executed by intelligent equipment to ensure accuracy.

[0068] The multi-factor fusion light simulation of the present application constructs a micro-scale field light distribution prediction; through light level quantization output, the light amount that soybeans can obtain during the entire growth period, especially during the key reproductive growth period, is accurately quantified, which is not achieved by traditional experience and provides a basis for comprehensive agricultural measures. The light level obtained is an important decision signal; if the predicted light level is continuously "insufficient", a series of response measures can be triggered, such as adjusting water and fertilizer, considering light supplement or as a key basis for adjusting the planting scheme next year.

[0069] Step five, growth water and fertilizer analysis: based on the real-time pre-acquired corn growth data, soybean growth data and soil water and fertilizer data, the water and fertilizer demand is analyzed, and a drip irrigation scheme is generated.

[0070] In one specific example, the water and fertilizer demand is analyzed based on the real-time pre-acquired corn growth data, soybean growth data and soil water and fertilizer data, and a drip irrigation scheme is generated, including: based on the real-time pre-acquired corn growth data, soybean growth data and soil water and fertilizer data, the water and fertilizer demand index is calculated; the water and fertilizer demand index is compared with the preset water and fertilizer demand index threshold value, when the water and fertilizer demand index is greater than or equal to the water and fertilizer demand index threshold value, it is judged that water and fertilizer drip irrigation is needed, and the drip irrigation scheme is generated, otherwise water and fertilizer drip irrigation is not performed.

[0071] It should be noted that the water and fertilizer demand index is calculated based on the real-time pre-acquired corn growth data, soybean growth data and soil water and fertilizer data; wherein the water and fertilizer demand index is equal to the corn plant height multiplied by the weight factor corresponding to the corn plant height, plus the corn leaf area index multiplied by the weight factor corresponding to the corn leaf area index, plus the soybean plant height multiplied by the weight factor corresponding to the soybean plant height, plus the soybean leaf area index multiplied by the weight factor corresponding to the soybean leaf area index, plus the soil nitrogen element content multiplied by the weight factor corresponding to the soil nitrogen element content, plus the soil phosphorus element content multiplied by the weight factor corresponding to the soil phosphorus element content, plus the soil potassium element content multiplied by the weight factor corresponding to the soil potassium element content, plus the soil water content multiplied by the weight factor corresponding to the soil water content.

[0072] Further, the weight factors corresponding to the corn plant height, the weight factors corresponding to the corn leaf area index, the weight factors corresponding to the soybean plant height, the weight factors corresponding to the soybean leaf area index, the weight factors corresponding to the soil nitrogen element content, the weight factors corresponding to the soil phosphorus element content, the weight factors corresponding to the soil potassium element content and the weight factors corresponding to the soil water content are obtained by factor analysis method, first, the information of corn plant height, corn leaf area index, soybean plant height, soybean leaf area index, soil nitrogen element content, soil phosphorus element content, soil potassium element content and soil water content is condensed, then the variance explanation rate after rotation is obtained, and the weights are obtained by excluding the cumulative variance explanation rate.

[0073] It should be noted that the water and fertilizer demand index threshold value is obtained by ROC curve analysis based on historical data to balance the decision accuracy of water and fertilizer saving and yield improvement; the water and fertilizer demand index threshold value is dynamically adjusted according to different planting areas and seasonal characteristics to ensure adaptation to environmental variations.

[0074] It should be noted that the drip irrigation scheme includes laying drip irrigation belts in the corn row with an inner diameter of 4mm, and precise water and fertilizer application is performed by an intelligent fertilizer pump; the specific parameters of the drip irrigation scheme are generated based on the water and fertilizer demand index and a preset drip irrigation parameter mapping rule, including water and fertilizer application amount, application time and application frequency; the intelligent fertilizer pump automatically adjusts the ratio of nitrogen, phosphorus and potassium fertilizers according to the water and fertilizer demand index to achieve the optimization goal of minimizing water and fertilizer use while maximizing yield.

[0075] The soybean corn interplanting three-dimensional cultivation method provided by the application scientifically determines the suitability of the land by calculating the soil nutrition index, thereby guaranteeing the basis for the success of interplanting from the source; further, by predicting the compact growth of corn at the jointing stage, the decision of spraying the regulator can be made in advance, the height of corn and the canopy are actively regulated, and the light grade of soybean is accurately predicted in combination with the environmental data and the plant spacing parameters; the shading stress of corn on soybean is effectively relieved, an excellent light environment is created for soybean, and the photosynthesis and yield formation of soybean are guaranteed; and the crop growth and soil data are monitored in real time, the differentiated water and fertilizer demand of corn and soybean is dynamically analyzed, and the waste of resources and the non-point source pollution are avoided. The application effectively solves the problem of interplanting, significantly improves the resource utilization rate, and provides reliable technical support for realizing green, high yield and high efficiency of agriculture.

[0076] The above content is only an example and explanation of the concept of the application, and those skilled in the art of the technology field can make various modifications or supplements to the described specific embodiments or replace them with similar ways, as long as they do not deviate from the concept of the application or exceed the scope defined in the specification, which shall belong to the protection scope of the application.

Claims

1. A method for three-dimensional cultivation of soybean and corn intercropping, characterized by, include: Step 1: Soil data analysis: Based on the pre-acquired soil data, the soil nutrient index is calculated to determine whether the soil meets the requirements for soybean-corn intercropping. Step 2, Seed Selection and Planting: Select corn and soybean varieties based on preset seed selection criteria; and calculate the intercropping sowing density based on preset planting layout parameters to obtain planting completion data. Step 3: Maize growth prediction: Based on the pre-acquired maize growth data at the jointing stage, a compact prediction result is obtained, and then the maize spraying demand is analyzed to obtain maize regulation decisions. The analysis of maize growth data at the pre-acquired jointing stage yields compact growth prediction results, including: A1, by the calculation formula obtaining a compact prediction probability , denotes the corn plant height at the corn jointing stage, denotes the corn leaf area index at the corn jointing stage, denotes a natural constant, , , , and denotes a regression coefficient; A2. Compare the compact prediction probability with the preset compact prediction probability threshold. When the compact prediction probability is greater than or equal to the preset compact prediction probability threshold, the compact prediction result is 1. When the compact prediction probability is less than the preset compact prediction probability threshold, the compact prediction result is 0. An analysis of corn foliar spraying requirements is conducted to arrive at corn regulation decisions, including: When the compactness prediction result is 1, the corn is judged to be compact, and there is no need for spraying. When the compactness prediction result is 0, there is a need for spraying, and a decision on spraying plant growth regulators is generated. Step 4: Soybean light forecasting: Based on environmental data, compact forecasting results, intercropping distance of maize and intercropping distance of soybean, soybean light forecasting analysis is performed to obtain light intensity level; Step 5: Growth Water and Fertilizer Analysis: Based on real-time pre-acquired corn growth data, soybean growth data, and soil water and fertilizer data, analyze water and fertilizer requirements and generate a drip irrigation plan.

2. The soybean and corn intercropping three-dimensional cultivation method according to claim 1, characterized in that, The soil data includes the content of nitrogen, phosphorus, and potassium.

3. The soybean and corn interplanting three-dimensional cultivation method according to claim 1, characterized by, The calculation of soil nutrient index based on pre-acquired soil data to determine whether the soil meets the requirements for soybean-corn intercropping includes: Based on the pre-acquired soil data, the soil nutrient index is calculated using a preset soil nutrient assessment method. The soil nutrient index is then compared with a preset soil nutrient index threshold to determine whether the soil nutrients meet the requirements for soybean-corn intercropping. When the soil nutrient index is greater than the soil nutrient index threshold, it is determined that the soil nutrients meet the requirements for soybean-corn intercropping, and step two is performed. When the soil nutrient index is less than or equal to the soil nutrient index threshold, it is determined that the soil nutrients do not meet the requirements for soybean-corn intercropping, and fertilization recommendations are generated based on the soil data.

4. The soybean and corn interplanting three-dimensional cultivation method according to claim 1, characterized by, The selection of corn and soybean varieties based on preset selection criteria includes: The preset selection criteria include compact plant type, upward-pointing leaves and small leaf angle for corn, and shade-tolerant type for soybeans; and select corresponding corn and soybean varieties according to the preset selection criteria.

5. The soybean and corn interplanting three-dimensional cultivation method according to claim 1, characterized by, The process involves calculating the intercropping sowing density based on preset planting layout parameters, and then obtaining planting completion data, including: Based on preset planting layout parameters, the direction of the planting strip, intercropping pattern, and row spacing are determined; and based on the local monoculture density, the intercropping sowing density is calculated using a preset sowing adjustment algorithm; then, based on the planting layout parameters and intercropping sowing density, planting operations are performed to generate planting completion data.

6. The soybean and corn interplanting stereoscopic cultivation method according to claim 1, characterized by, The method of predicting and analyzing soybean light intensity based on environmental data, compact prediction results, intercropping distances for maize and soybeans to obtain light intensity levels includes: Based on the compact prediction results, intercropping distances for maize and soybean, the initial light transmittance index is calculated. Environmental factors are obtained through environmental data analysis, and then the initial light transmittance index is corrected using these environmental factors to obtain the corrected light transmittance index. Light levels are then generated according to preset light level mapping rules, and adjustment operations are generated based on the light levels.

7. The soybean and corn interplanting stereoscopic cultivation method according to claim 6, characterized by, The process of generating illumination levels through preset illumination level mapping rules and generating adjustment operations based on those illumination levels includes: B1. Compare the corrected light transmittance index with its upper and lower limits. When the corrected light transmittance index is greater than or equal to the upper limit, the light level is recorded as sufficient; when the corrected light transmittance index is less than the upper limit but greater than or equal to the lower limit, the light level is recorded as Level 1 insufficient; when the corrected light transmittance index is less than the lower limit, the light level is recorded as Level 2 insufficient. B2. When the light level is sufficient, no operation is required; when the light level is insufficient (Level 1), sparse and dense operation is performed; when the light level is insufficient (Level 2), sparse and dense operation and potassium dihydrogen phosphate spraying are performed simultaneously.

8. The soybean-corn intercropping three-dimensional cultivation method according to claim 1, characterized in that, The method analyzes water and fertilizer requirements based on real-time pre-acquired corn growth data, soybean growth data, and soil water and fertilizer data, and generates a drip irrigation plan, including: Based on real-time pre-acquired corn growth data, soybean growth data, and soil water and fertilizer data, a water and fertilizer demand index is calculated. The water and fertilizer demand index is compared with a preset water and fertilizer demand index threshold. If the water and fertilizer demand index is greater than or equal to the water and fertilizer demand index threshold, it is determined that drip irrigation is required and a drip irrigation plan is generated. Otherwise, drip irrigation is not carried out.

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