A method and system for precision cultivation of medium-late maturity corn based on density regulation

Abstract

The application discloses a kind of based on density regulation's middle-late mature corn precision cultivation method and system, specifically related to the field of agricultural plant cultivation environment control, difference value is generated by collecting plant spacing information, combine leaf area index and photosynthetic parameter comparison, determine the growth state of crop in region, and adjust moisture supply accordingly, combine real-time environmental parameter optimization cultivation density, finally the correspondence between water demand and cultivation density is converted into irrigation control, realize the precision management of corn under dynamic environment.The application makes cultivation space distribution balanced through step-by-step difference calculation, parameter comparison and matching adjustment, avoids the problem caused by local over-dense or over-sparse;Through the cross operation of leaf area index, photosynthetic rate, transpiration rate and temperature, a double matching of health index and density is formed;Through the difference comparison of water and density correlation, dynamic adjustment of irrigation amount is realized, so that water distribution between plants is more reasonable, and the precision of cultivation management is improved.

Description

Technical Field

[0001] This invention relates to the field of environmental control in agricultural plant cultivation, and in particular to a method and system for precision cultivation of mid-to-late maturing maize based on density regulation. Background Technology

[0002] The field of agricultural plant cultivation environmental control technology encompasses various methods and techniques for optimizing crop cultivation processes by regulating the plant's growth environment. The core of this field is researching how to improve plant growth conditions, thereby increasing crop yield and quality, through environmental control factors such as temperature, humidity, light, CO2 concentration, soil moisture, and nutrients. Agricultural plant cultivation environmental control technology is not only applied to greenhouse cultivation but also widely used in the precision management of field crops. The technologies involved in this field include the design of environmental control equipment, the optimization of cultivation methods, and other auxiliary technologies related to agricultural production, such as irrigation systems and fertilization systems.

[0003] Among them, the density-based precision cultivation method for mid-to-late maturing maize refers to optimizing the plant's growth environment by adjusting the plant spacing and row spacing, thereby improving crop growth efficiency and yield. It mainly involves the precise control of maize planting density and addressing cultivation needs under different climatic conditions by adjusting the planting density at different growth stages. Employing density-based techniques, this method ensures maize grows in an optimal spatial distribution by adjusting the density at different growth stages, avoiding uneven growth caused by over-density or under-density planting. This method does not rely on complex computational models but optimizes the maize cultivation environment through actual cultivation processes and density control.

[0004] Current technologies rely heavily on fixed planting spacing and empirical watering, lacking real-time recording and regional comparison of individual plant spacing differences. This leads to the failure to promptly identify local deviations, resulting in the spread of over-density or under-density phenomena. Existing leaf area index (LAI) measurements often use single-point averages to represent the whole, failing to reflect regional differences and easily distorting data when leaves are densely packed or sparse in certain areas. Data such as photosynthetic rate and transpiration rate are usually recorded in a scattered manner, lacking cross-comparison with LAI and density data, making it difficult to form a complete health assessment. This can lead to situations where leaf area is too large but photosynthesis is insufficient, or photosynthesis is too strong but plant spacing is insufficient. Environmental monitoring often stops at simple temperature and humidity recording, lacking quantitative integration of regional difference coefficients and differences in plant population distribution, resulting in inaccurate adjustments between adjacent areas. In the irrigation process, existing systems often control water flow and valve opening with uniform flow rate and valve opening, failing to link water demand with differences in planting density. This can easily lead to a disconnect between water supply and the actual needs of the plants, such as excessive water supply in overly dense areas or insufficient water supply in overly sparse areas, ultimately affecting the balanced growth of the corn population. Summary of the Invention

[0005] The main objective of this invention is to provide a method and system for precise cultivation of mid-to-late maturing maize based on density control. By collecting data on plant spacing differences, obtaining leaf area index, monitoring photosynthetic parameters and environmental factors, and combining water control and irrigation adjustments, this invention solves the problems of uneven density distribution and water supply imbalance during maize cultivation.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A precision cultivation method for medium-late maturing maize based on density control, the specific steps of which are as follows: Step 001: Collect the plant spacing of each corn plant using field plant positioning sensors, calculate and summarize the difference between adjacent plant spacings to generate a plant spacing difference value, compare this value with the preset density standard, screen out the areas that need adjustment, and obtain a preliminary density assessment value. Step 002: Based on the preliminary density assessment value, obtain the leaf area index of the area. Compare the plant spacing difference value with the leaf area index to analyze whether there is excessive density or excessive sparseness. If the plant spacing difference value is large and the leaf area index is high, reduce the water supply; otherwise, increase the water supply. Generate the regional water regulation value. Step 003: Acquire photosynthesis sensor data, monitor photosynthetic rate, transpiration rate, and leaf temperature, compare photosynthetic efficiency data with leaf area index, identify crop health status, analyze plant spacing difference value and photosynthetic efficiency data, and generate crop health assessment value. Step 004: Based on the crop health assessment value and real-time environmental data such as soil moisture and temperature, adjust the planting density, calculate the density difference between adjacent areas, optimize the planting density, ensure the best spatial distribution of crops, and combine soil moisture data with crop health assessment values ​​to generate optimized planting density values. Step 005: Based on the regional water regulation value and the optimized cultivation density value, adjust the irrigation system. By monitoring the cultivation density and water demand, adjust the irrigation amount to ensure a reasonable match between water and density, and generate irrigation amount adjustment results.

[0007] Preferably, the preliminary density assessment values ​​include density deviation range, uniformity index, and adjustment demand determination; the regional water control values ​​specifically include water supply reduction, water supply increase, and control execution interval; the crop health assessment values ​​include photosynthetic rate level, transpiration intensity index, and leaf temperature coefficient; the optimized cultivation density values ​​specifically refer to spatial distribution coefficient, regional density difference, and plant distribution uniformity; and the irrigation adjustment values ​​include total water consumption, zoned water supply ratio, and fertilizer application ratio.

[0008] Preferably, step 001 specifically includes: Step 011: Collect data from the field plant positioning sensors, record the plant spacing information of each corn plant, calculate the point-by-point difference between adjacent plant spacings, and collect all the difference data in the order of regions to generate a plant spacing difference sequence. Step 012: Based on the plant spacing difference sequence, the values ​​in each region are statistically summarized, the weighted calculation method is used to obtain the overall difference, and all values ​​are integrated by region to generate the plant spacing difference value. Step 013: Based on the plant spacing difference value, call the preset density uniformity standard, compare the deviation between the two, screen out the areas that need to be adjusted, and obtain the preliminary density assessment value.

[0009] Preferably, step 002 specifically includes: Step 021: Based on the preliminary density assessment value, obtain the leaf area index data of the region, convert the leaf projection area to the corresponding ground surface area, and integrate the values ​​of different measurement points in a regionalized manner to generate the leaf area index value. Step 022: Call the leaf area index value and compare it with the obtained plant spacing difference value to determine the relationship between the two in the region. If the plant spacing difference value exceeds the density deviation threshold and the leaf area index value is greater than the plant coverage benchmark, it is marked as an overly dense region; otherwise, it is marked as an overly sparse region, and the region density determination value is obtained. Step 023: Adjust the regional water supply based on the regional density determination value and the regional water supply. Reduce the water supply when the region is determined to be too dense and increase the water supply when the region is determined to be too sparse, thus generating the regional water control value.

[0010] Preferably, step 003 specifically includes: Step 031: Acquire photosynthesis sensor data, record photosynthetic rate, transpiration rate and leaf temperature, integrate the data from each monitoring point in the order of regions to form a continuous photosynthetic efficiency record, and generate photosynthetic parameter values. Step 032: Call up the photosynthetic parameter values ​​and compare them with the leaf area index values ​​to determine whether there is insufficient photosynthesis or excessive consumption in the plant population in the region. Calculate the health index range based on the comparison between the two and generate the health index values. Step 033: Analyze the health index values ​​and plant spacing differences to determine the degree of matching between crop density and photosynthesis in each region, and generate crop health assessment values.

[0011] Preferably, step 004 specifically includes: Step 041: Based on the crop health assessment values, obtain the real-time soil moisture and ambient temperature of the region, integrate the monitoring point values ​​according to the regional location, calculate their average change trend, and generate environmental parameter values. Step 042: Based on environmental parameter values, compare the difference coefficients between regions, calculate the changes in the number of plants and spacing between adjacent regions, obtain the population distribution difference degree between regions, and generate density difference values. Step 043: Call up the density difference value and crop health assessment value, compare them with the soil moisture value, adjust the regional cultivation density, determine the spatial distribution balance range, and generate the optimized cultivation density value.

[0012] Preferably, step 005 specifically includes: Step 051: Based on the regional water regulation value and the optimized cultivation density value, obtain the water flow and valve opening parameters of the irrigation system, compare the regulation requirements of different regions with the current operating parameters of the irrigation equipment, and generate irrigation parameter difference values. Step 052: Based on the differences in irrigation parameters, monitor the density distribution and real-time water demand of the cultivation area, adjust the water flow of the irrigation equipment to the range corresponding to the density demand of the area, and generate a water matching degree value. Step 053: Call the water matching degree value, calculate the difference between the required irrigation amount and the actual water supply of the equipment in each area, adjust the water supply range and determine the water supply ratio of each area, and generate the irrigation amount adjustment result.

[0013] This invention also discloses a density-controlled precision cultivation system for mid-to-late maturing maize, used in the above-mentioned method. The system includes: The plant spacing acquisition module acquires positioning data from field plant positioning sensors, collects the plant spacing of individual corn plants, calculates the difference between plant spacing of adjacent plants, and generates plant spacing difference values. The uniformity calculation module, based on the plant spacing difference value, calls the regional leaf area index data, performs a set operation on the two, calculates the regional plant distribution uniformity index, and generates the uniformity index. The photosynthesis monitoring module collects photosynthetic rate, leaf temperature and transpiration rate data from the photosynthesis sensor based on the uniformity index value, compares the various sensor data with the uniformity index value, and generates photosynthetic status value. The density control module collects soil moisture sensor and temperature data from meteorological monitoring station for photosynthetic state value. It jointly judges the photosynthetic state, soil moisture value and temperature value. When the photosynthetic state rate is low and the soil moisture is at the drought threshold, it is determined that the cultivation density needs to be adjusted. When the photosynthetic state rate is high and the soil moisture is at the saturation threshold, it is determined that the cultivation density needs to be reduced. Based on the judgment results, the regional cultivation plant spacing is reset and a density control value is generated. Irrigation linkage module: calls the density control value, collects the water output data of regional irrigation equipment, compares the water output with the density control value, adjusts the regional irrigation control parameters, and generates irrigation allocation values.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention collects plant spacing data using sensors and calculates the difference point-by-point. Regional differences are compared with set standards to form adjustable regions, achieving precise spatial distribution identification and avoiding biased conclusions from single average values. The leaf area index is calculated by converting the plant leaf projection area to the ground surface area, and then compared with the plant spacing difference value. When the difference is too large and the coverage area increases, water reduction is implemented; when the difference is too large and the coverage is insufficient, water replenishment is implemented. Water allocation is driven by numerical calculations rather than empirical judgment, eliminating human bias. Parameters such as photosynthetic rate, transpiration rate, and leaf temperature are integrated into the regional sequence and correlated with the leaf area index. This system establishes a correlation between planting density and water supply, calculating health indicators and coupling them with plant spacing differences to create a dual matching relationship between spatial distribution and photosynthetic capacity, avoiding distortions caused by single-dimensional judgments. Real-time monitoring of environmental humidity and temperature calculates regional difference coefficients and, combined with plant quantity and spacing differences, generates a population distribution difference degree, which is more accurate than previous extensive density statistics methods. In irrigation, by comparing target and actual values ​​of water flow and valve opening, parameter differences are identified. These differences are then combined with comparisons of plant quantity and water demand to quantify the difference between actual and target water supply as a matching degree. Ultimately, these differences are transformed into adjustments to regional proportions, forming a numerically driven irrigation plan. This entire logic, through step-by-step numerical comparison, difference transformation, and matching adjustment, enables quantitative matching of cultivation density and water supply in a dynamic environment, eliminating errors caused by relying solely on experience. The gains are reflected in the balance of cultivation space distribution and the accuracy of water allocation. Attached Figure Description

[0015] Figure 1 A flowchart of a density-controlled precision cultivation method for mid-to-late maturing maize in some embodiments of the present invention; Detailed Implementation

[0016] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the linguistic context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0017] It should be understood that the terms "system," "device," "unit," and / or "module" as used in this specification are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0018] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0019] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0020] like Figure 1 As shown in the figure, this embodiment discloses a precision cultivation method for medium-late maturing maize based on density control, specifically including: Step 001: Collect the plant spacing of each corn plant using field plant positioning sensors, calculate and summarize the difference between adjacent plant spacings to generate a plant spacing difference value, compare this value with a preset density standard, screen out the areas that need adjustment, and obtain a preliminary density assessment value; the preliminary density assessment value includes the density deviation range, uniformity index, and adjustment requirement determination. Step 002: Based on the preliminary density assessment value, obtain the leaf area index of the region, compare the plant spacing difference value with the leaf area index, analyze whether there is excessive density or excessive sparseness, and determine that when the plant spacing difference value is large and the leaf area index is high, reduce the water supply, and vice versa, increase the water supply to generate regional water control values; the regional water control values ​​are specifically the amount of water supply reduction, the amount of water supply increase, and the control execution range. Step 003: Acquire photosynthesis sensor data, monitor photosynthetic rate, transpiration rate, and leaf temperature, compare photosynthetic efficiency data with leaf area index, identify crop health status, analyze plant spacing difference value and photosynthetic efficiency data, and generate crop health assessment value; crop health assessment value includes photosynthetic rate level, transpiration intensity index, and leaf temperature coefficient. Step 004: Based on the crop health assessment value and real-time environmental data such as soil moisture and temperature, adjust the planting density, calculate the density difference between adjacent areas, optimize the planting density, and ensure the optimal spatial distribution of crops. Combine soil moisture data with crop health assessment values ​​to generate optimized planting density values. Optimized planting density values ​​specifically refer to spatial distribution coefficient, regional density difference, and plant distribution balance. Step 005: Based on the regional water regulation value and the optimized cultivation density value, adjust the irrigation system. By monitoring the cultivation density and water demand, adjust the irrigation amount to ensure a reasonable match between water and density, and generate the irrigation amount adjustment result. The irrigation amount adjustment value includes adjusting the total water consumption, the regional water supply ratio, and the fertilizer application ratio.

[0021] In some embodiments of the present invention, step 001 specifically includes: Step 011: Collect data from the field plant positioning sensors, record the plant spacing information of each corn plant, calculate the point-by-point difference between adjacent plant spacings, and collect all the difference data in the order of regions to generate a plant spacing difference sequence. Specifically, firstly, positioning sensor nodes are deployed within the corn planting area. Each node sequentially scans the row and plant positions, recording the horizontal and vertical coordinates of each individual corn plant. For example, the horizontal coordinates of 10 corn plants in a certain area are 0.45m, 0.95m, 1.48m, 2.05m, 2.55m, 3.08m, 3.62m, 4.12m, 4.65m, and 5.20m, respectively, while the vertical coordinates are fixed at a row spacing of 2.00m. This yields the two-dimensional position data for each corn plant. After arranging this data according to plant order, the horizontal distance between adjacent plants is calculated sequentially. The difference value is calculated by taking the distance between the second and first plants as 0.95-0.45=0.50m, the distance between the third and second plants as 1.48-0.95=0.53m, and so on until the last plant. After completing the difference calculation process point by point, the difference sequence between each plant is stored as a sequence set. For example, the obtained sequence is {0.50,0.53,0.57,0.50,0.53,0.54,0.50,0.53,0.55}. This sequence data is bound to the corresponding area identifier number to form the plant distance difference sequence within the area. Step 012: Based on the plant spacing difference sequence, the values ​​in each region are statistically summarized, the weighted calculation method is used to obtain the overall difference, and all values ​​are integrated by region to generate the plant spacing difference value. During implementation, the difference data within each area are statistically summarized. First, all values ​​in the sequence are summed, for example, 0.50+0.53+0.57+0.50+0.53+0.54+0.50+0.53+0.55=4.75. Then, the sum is divided by the number of plant spacing differences, 9, to obtain the average difference, which is 4.75 / 9≈0.528m. Next, the deviation of each difference from the average difference is weighted, with the weight set based on the positional order. For example, plants near the edge of the area are assigned a weight of 0.8, and plants in the middle are assigned a weight of 0.8. The plant is assigned a weight of 1.2. After weight adjustment, the overall difference is recalculated. For example, if the difference of the first plant is 0.50m, the deviation is |0.50-0.528|=0.028m. Multiplying this by the edge weight of 0.8 gives a weighted deviation of 0.0224m. All weighted deviations are summed and the average is calculated to obtain the overall difference value. For example, the calculation result is 0.036m. This value is used as the overall difference value of the region. The difference value is then compared with the calculation results of other regions and integrated by region number to finally form the regional plant distance difference value. Step 013: Based on the plant spacing difference value, call the preset density uniformity standard, compare the deviation between the two, screen out the areas that need to be adjusted, and obtain the preliminary density assessment value.

[0022] Based on the plant spacing difference, a preset density uniformity standard is used for comparison. The density uniformity standard is set with the theoretical plant spacing of 0.55m as the baseline, and the allowable deviation range is set to ±0.05m. Therefore, when the plant spacing difference is less than 0.05m, it is judged as uniform; when the plant spacing difference is between 0.05m and 0.10m, it is judged as a slight deviation; and when the plant spacing difference is greater than 0.10m, it is judged as a significant deviation. When performing the comparison, if the difference in a certain area is 0.036m, it is compared with the standard range of 0.05m. Since it is less than 0.05m, the density in this area is judged as uniform and no adjustment is needed. If the difference in another area is 0.085m, it is judged as a slight deviation after comparing with the 0.05m threshold and needs to be recorded as an adjustable area. If the difference is 0.112m, it exceeds the range after comparing with the 0.10m threshold and is judged as a significant deviation, and is directly filtered as an area that needs adjustment. Finally, the judgment results of all areas are sorted together to obtain the preliminary density assessment value.

[0023] In some embodiments of the present invention, step 002 specifically includes: Step 021: Based on the preliminary density assessment value, obtain the leaf area index data of the region, convert the leaf projection area to the corresponding ground surface area, and integrate the values ​​of different measurement points in a regionalized manner to generate the leaf area index value. For example, measurements are first taken in the field according to the evaluation area numbers. Fixed sampling points are set up in each area; for instance, five sampling points are selected within a 10m² area. At each sampling point, the leaves of a single corn plant are unfolded layer by layer, and the projected length and width of each leaf are measured. The projected area of ​​each leaf is approximated by multiplying the length by the width. For example, if a corn plant has 10 leaves with an average leaf length of 0.60m and a width of 0.08m, then the area of ​​a single leaf is 0.048m², and the total projected area of ​​10 leaves is 0.48m². Then, the projected areas of the leaves from multiple corn plants are added together to obtain the total projected leaf area of ​​the sampling point. The total area of ​​the 5 corn plants is 2.40 m². This value is then converted proportionally to the surface area corresponding to the measurement point. For example, if the area of ​​the measurement point is 1.00 m², then the leaf area index (LAI) value of that point is 2.40 / 1.00 = 2.40. The LAI values ​​of different measurement points are integrated according to the region. If the LAI values ​​of the 5 measurement points in this region are 2.40, 2.35, 2.50, 2.20, and 2.45 respectively, then the average value of all values ​​is calculated to obtain the regional LAI value as (2.40 + 2.35 + 2.50 + 2.20 + 2.45) / 5 = 2.38. The final LAI value for this region is then generated.

[0024] Step 022: Call the leaf area index value and compare it with the obtained plant spacing difference value to determine the relationship between the two in the region. If the plant spacing difference value exceeds the density deviation threshold and the leaf area index value is greater than the plant coverage benchmark, it is marked as an overly dense region; otherwise, it is marked as an overly sparse region, and the region density determination value is obtained. Specifically, the deviation threshold for plant spacing difference is first set to 0.05m, and the leaf area index (LAI) baseline value is 2.00. The plant spacing difference value of a certain area is compared with the threshold. If the difference value is greater than 0.05m, it is marked as a deviation area; if it is less than or equal to 0.05m, it is judged as a uniform area. Then, the LAI value is further judged. When the plant spacing difference value exceeds 0.05m and the LAI value is greater than 2.00, a classification action is performed, and the area is classified as an over-dense area. For example, if the plant spacing difference value of a certain area is 0.085m... If the leaf area index (LAI) is 2.38, and both conditions meet the threshold, the area is marked as overly dense. If the difference in plant spacing exceeds 0.05m but the LAI is less than or equal to 2.00, the area is marked as overly sparse. For example, if the difference in plant spacing is 0.075m and the LAI is 1.80, the area is marked as overly sparse. If the difference in plant spacing is less than or equal to 0.05m, the area is recorded as having uniform density regardless of the LAI value. Finally, through this series of comparisons and judgments, the density determination value of the area is obtained.

[0025] Step 023: Adjust the regional water supply based on the regional density determination value and the regional water supply. Reduce the water supply when the region is determined to be too dense and increase the water supply when the region is determined to be too sparse, thus generating the regional water control value.

[0026] Based on the regional density assessment value and the regional water supply, adjustments are made. Initially, a baseline water supply of 30L / m² is set. When a region is deemed overly dense, the water supply is reduced by 10% of the baseline supply, i.e., 3L / m². For example, if a region's original water supply was 30L / m², it is adjusted to 27L / m². Conversely, when a region is deemed underly sparse, the water supply is increased by 10% of the baseline supply, i.e., 3L / m². For example, if a region's original water supply was 30L / m², the adjusted supply is 27L / m². The adjusted value is 33L / ㎡. If the area is determined to be uniform, the original water supply will remain unchanged. The control range will be determined according to the actual area range of each area number. For example, if area A is 20㎡ and is determined to be an overly dense area, the total water supply will be reduced from 600L to 540L. If area B is 25㎡ and is determined to be an overly sparse area, the total water supply will be increased from 750L to 825L. The final regional water control values ​​are the water supply reduction, water supply increase and corresponding control range for each area.

[0027] In another embodiment of the present invention, step 3 includes steps 301-32: Step 031: Acquire photosynthesis sensor data, record photosynthetic rate, transpiration rate and leaf temperature, integrate the data from each monitoring point in the order of regions to form a continuous photosynthetic efficiency record, and generate photosynthetic parameter values. For example, firstly, fixed monitoring points are set up within the area, and the photosynthetic rate Pn, transpiration rate Tr, and leaf temperature T are recorded one by one. For instance, if five monitoring points are set up in a certain area, the photosynthetic rate collected at the same time will be 20.5 μmol·m⁻¹. -2 ˙s -1 22.3 μmol˙m -2 ˙s -1 21.8 μmol·m -2 ˙s -1 19.7 μmol·m -2 ˙s -1 23.0 μmol˙˙m -2 ˙s -1 The transpiration rate was 4.2 mmol·m³. -2 ˙s -1 4.5 mmol·mm -2 ˙s -1 4.4 mmol·m -2 ˙s -1 4.0 mmol·m -2˙s -1 4.6 mmol·m -2 ˙s -1 Leaf temperatures were 28.5℃, 28.9℃, 29.1℃, 28.2℃, and 29.3℃. Data from each point in the same area were arranged in the order of monitoring points, and the values ​​from all monitoring points were then aggregated to form a continuous time series. Numerical binding was performed between the three parameters of photosynthetic rate, transpiration rate, and leaf temperature, so that each monitoring point formed a three-dimensional data set. Finally, the data were integrated into a continuous record of photosynthetic efficiency for the region. The average value and fluctuation range of this record were calculated. For example, the average photosynthetic rate was 21.5 μmol·m³. -2 ˙s -1 The average transpiration rate was 4.34 mmol·m³. -2 ˙s -1 The average leaf temperature was 28.8℃, and the integrated results of this group were used as photosynthetic parameter values.

[0028] Step 032: Call up the photosynthetic parameter values ​​and compare them with the leaf area index values ​​to determine whether there is insufficient photosynthesis or excessive consumption in the plant population in the region. Calculate the health index range based on the comparison between the two and generate the health index values. The photosynthetic parameter values ​​were compared with the leaf area index (LAI) values. First, a baseline LAI value of 2.00 was set. The ratio of photosynthetic rate to LAI was used as the basis for calculating the health indicator, defined as H = Pn / LAI, where Pn is the average photosynthetic rate of the region and LAI is the regional leaf area index value. Taking the aforementioned data as an example, Pn = 21.5 μmol·m⁻¹ -2 ˙s -1 Given LAI = 2.38, H = 21.5 / 2.38 ≈ 9.03. The health indicator range is set to three levels: H < 7.00 indicates insufficient photosynthesis, 7.00 ≤ H ≤ 11.00 indicates normal photosynthesis, and H > 11.00 indicates excessive consumption. During the judgment, the calculated H value of 9.03 is compared with the threshold. Since it falls between 7.00 and 11.00, this area is judged as having a normal photosynthetic level. The judgment result is recorded as the health indicator value, which corresponds to the photosynthetic level of the area. Step 033: Analyze the health index values ​​and plant spacing differences to determine the degree of matching between crop density and photosynthesis in each region, and generate crop health assessment values.

[0029] The analysis is based on health indicator values ​​and plant spacing differences. First, the health indicator value of 9.03 from the previous step is retrieved and matched with the plant spacing difference value. For example, if the plant spacing difference value in a certain area is 0.085m, which is greater than the deviation threshold of 0.05m, it is necessary to check whether the health indicator falls within the normal range. If the health indicator is in the normal range but the plant spacing difference value is large, it indicates that there is a certain imbalance between density and photosynthesis. During the process, a binary judgment matrix is ​​formed between the health indicator value and the plant spacing difference value. Each area is classified according to the combination of the two types of parameters. In the example, the plant spacing difference value is 0.085m and the health indicator value is 9.03. The corresponding classification result is density deviation but normal photosynthesis. Finally, the judgment matrix results of all areas are organized into a health assessment sequence to generate crop health assessment values.

[0030] In some embodiments of the present invention, step 004 specifically includes: Step 041: Based on the crop health assessment values, obtain the real-time soil moisture and ambient temperature of the region, integrate the monitoring point values ​​according to the regional location, calculate their average change trend, and generate environmental parameter values. For example, firstly, soil moisture and ambient temperature monitoring points are set up in each area. The soil moisture θ and temperature T collected by the sensors are recorded and stored in chronological order. Then, the values ​​of multiple monitoring points in the same area are summarized. For example, if five points are set up in a certain area, the soil moisture is 22.1%, 23.0%, 21.8%, 22.5%, and 23.2% respectively, and the ambient temperature is 27.5℃, 27.8℃, 28.0℃, 27.3℃, and 27.9℃ respectively. The average of the five soil moisture values ​​is 22.5%, and the average ambient temperature is 27.7℃. While calculating the average, the difference between the values ​​at adjacent monitoring times is compared to obtain the rate of change. For example, the rate of change between the humidity of point 1 (22.1%) and point 2 (23.0%) is (23.0-22.1) / 22.1≈4.07%. The same calculation is performed for other monitoring points in sequence. All rates of change are integrated in chronological order to obtain the average trend. The combined value of humidity and temperature is used as the environmental parameter value of the area.

[0031] Step 042: Based on environmental parameter values, compare the difference coefficients between regions, calculate the changes in the number of plants and spacing between adjacent regions, obtain the population distribution difference degree between regions, and generate density difference values. First, we define the formula for calculating the coefficient of variation: C = σ / μ, where σ is the standard deviation and μ is the mean. We substitute the soil moisture and temperature parameters of two adjacent regions into the formula. For example, region A has a moisture content of 22.5% and a temperature of 27.7℃, while region B has a moisture content of 24.0% and a temperature of 28.2℃. The mean moisture content of the two regions is μ = 23.25%, and the standard deviation is σ = √[((22.5-23.25)²+(24.0-23.25)²) / 2] = 0.75%. Therefore, the coefficient of variation for moisture content is C = 0.75 / 23. 25≈0.032. Similarly, the difference coefficient for temperature is calculated to obtain C=0.25 / 27.95≈0.009. Then, the difference coefficient is combined with the data on the number of plants and the spacing between plants in the region. For example, region A has 100 plants with an average spacing of 0.53m, and region B has 110 plants with an average spacing of 0.50m. The difference in the number of plants is calculated to be 10 plants, and the difference in the spacing between plants is 0.03m. The difference coefficient is combined with the difference in the number of plants and the difference in the spacing between plants to obtain the difference in population distribution between regions. Finally, this difference is used as the density difference value. Step 043: Call up the density difference value and crop health assessment value, compare them with the soil moisture value, adjust the regional cultivation density, determine the spatial distribution balance range, and generate the optimized cultivation density value.

[0032] By comparing soil moisture values, a spatial distribution equilibrium range threshold of ±0.05m is initially set. When the density difference in a certain area exceeds this threshold, density adjustment is required, increasing or decreasing the plant spacing to approximate the equilibrium range. For example, if the health assessment value is "normal" and the area's humidity is 22.5%, within the baseline humidity range of 20%–25%, but the density difference is 0.085m (greater than 0.05m), a certain number of plants need to be reduced, increasing the plant spacing from 0.50m to 0.55m through transplanting or directional thinning. Once the adjustment is complete, if the density difference in another area is 0.035m and within the threshold range, no adjustment is needed. If the humidity is below 20%, the number of plants should be reduced while adjusting the plant spacing to avoid excessive density due to insufficient water. For example, if the humidity in a certain area is 18.9%, the health assessment value is "normal", and the density difference is 0.065m, then the plant spacing adjustment range should be increased to 0.58m. Finally, through the above comparison and adjustment, the spatial distribution of each area is classified into the balance range, generating an optimized cultivation density value.

[0033] In some embodiments of the present invention, step 005 specifically includes: Step 051: Based on the regional water regulation value and the optimized cultivation density value, obtain the water flow and valve opening parameters of the irrigation system, compare the regulation requirements of different regions with the current operating parameters of the irrigation equipment, and generate irrigation parameter difference values. In this embodiment, the control target data for each region is first retrieved. For example, the water control target for region A is a 10% reduction in water supply, and the target for optimized planting density is a plant spacing of 0.55m. Then, the real-time operating parameters of the irrigation system are obtained, and the current water flow rate Q and valve opening α are read point by point. For example, the current water flow rate in region A is 32L / min and the valve opening is 70%, while the water flow rate in region B is 28L / min and the valve opening is 65%. The target water supply demand is then converted into the target water flow rate. For example, the area of ​​region A is 20㎡, the baseline water supply is 30L / ㎡, and the target is 540L after a 10% reduction. The total water supply needs to be completed within 1 hour, so the target water flow rate is 9L / min. Compared with the actual 32L / min, the difference is 32-9=23L / min. At the same time, the difference in valve opening is calculated. The target water flow rate corresponds to a valve opening of about 25%, which is 45% compared with the actual 70%. The comparison results of all regions are recorded as irrigation parameter difference values. Step 052: Based on the differences in irrigation parameters, monitor the density distribution and real-time water demand of the cultivation area, adjust the water flow of the irrigation equipment to the range corresponding to the density demand of the area, and generate a water matching degree value. Specifically, firstly, the plant spacing and number of plants in each area are integrated into a density factor D. For example, area A has an area of ​​20㎡ and a plant spacing of 0.55m, corresponding to approximately 100 plants; area B has an area of ​​25㎡ and a plant spacing of 0.50m, corresponding to approximately 110 plants. Then, the area's water requirement is divided by the number of plants to obtain the water requirement per plant. For example, area A requires 540L / 100 plants = 5.4L / plant, and area B requires 825L / 110 plants = 7.5L / plant. Finally, the actual water supply from the irrigation equipment is divided by the number of plants to obtain the actual water supply per plant. If the actual water supply in area A is... 960L / 100 plants = 9.6L / plant, Zone B is 720L / 110 plants = 6.5L / plant. Then compare the actual value with the target value. For example, the difference in Zone A is 9.6-5.4=4.2L / plant, and the difference in Zone B is 6.5-7.5=-1.0L / plant. Finally, judge according to the difference value and the threshold set range (±0.5L / plant). If it exceeds the range, adjust the water flow of the equipment, reduce the water flow of Zone A to the target value, and increase the water flow of Zone B to the target value. The record generated after the adjustment is completed is the moisture matching value. Step 053: Call the water matching degree value, calculate the difference between the required irrigation amount and the actual water supply of the equipment in each area, adjust the water supply range and determine the water supply ratio of each area, and generate the irrigation amount adjustment result.

[0034] For example, first compare the target irrigation volume with the adjusted irrigation volume. For instance, the target for area A is 540L, and the actual adjusted volume is 570L, a difference of 30L. The target for area B is 825L, and the actual adjusted volume is 810L, a difference of -15L. Standardize the difference by area: for area A, 30L / 20㎡ = 1.5L / ㎡; for area B, -15L / 25㎡ = -0.6L / ㎡. Then, fine-tune the water supply area based on the difference, setting the adjustment benchmark as follows: ±1L / m². If the value exceeds this benchmark, the water supply range will be adjusted. For example, if the value in Zone A is 1.5L / m², which is greater than 1L / m², the water supply range needs to be lowered. If the value in Zone B is -0.6L / m², which is within the threshold range, the water supply range will remain unchanged. Finally, the adjustment amount for each zone will be proportionally allocated. For example, if the total adjustment amount for Zone A is 30L and the total adjustment amount for Zone B is 0L, the final water supply ratio will be 30L / 30L = 100% for Zone A and 0% for Zone B. This calculation result will be used as the irrigation volume adjustment result.

[0035] This invention also discloses a density-controlled precision cultivation system for mid-to-late maturing maize, used to perform the above-described method, characterized in that the system comprises: The plant spacing acquisition module acquires positioning data from field plant positioning sensors, collects the plant spacing of individual corn plants, calculates the difference between plant spacing of adjacent plants, and generates plant spacing difference values. The uniformity calculation module, based on the plant spacing difference value, calls the regional leaf area index data, performs a set operation on the two, calculates the regional plant distribution uniformity index, and generates the uniformity index. The photosynthesis monitoring module collects photosynthetic rate, leaf temperature and transpiration rate data from the photosynthesis sensor based on the uniformity index value, compares the various sensor data with the uniformity index value, and generates photosynthetic status value. The density control module collects soil moisture sensor and temperature data from meteorological monitoring station for photosynthetic state value. It jointly judges the photosynthetic state, soil moisture value and temperature value. When the photosynthetic state rate is low and the soil moisture is at the drought threshold, it is determined that the cultivation density needs to be adjusted. When the photosynthetic state rate is high and the soil moisture is at the saturation threshold, it is determined that the cultivation density needs to be reduced. Based on the judgment results, the regional cultivation plant spacing is reset and a density control value is generated. Irrigation linkage module: calls the density control value, collects the water output data of regional irrigation equipment, compares the water output with the density control value, adjusts the regional irrigation control parameters, and generates irrigation allocation values.

[0036] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A precision cultivation method for medium-late maturing maize based on density control, characterized in that, The specific steps are as follows: Step 001: Collect the plant spacing of each corn plant using field plant positioning sensors, calculate and summarize the difference between adjacent plant spacings to generate a plant spacing difference value, compare this value with the preset density standard, screen out the areas that need adjustment, and obtain a preliminary density assessment value. Step 002: Based on the preliminary density assessment value, obtain the leaf area index of the area, compare the plant spacing difference value with the leaf area index, and analyze whether there is excessive density or sparseness. When the plant spacing difference value exceeds the density deviation threshold and the leaf area index value is greater than the plant coverage benchmark, the corresponding area is determined to be an excessively dense area and the water supply is reduced. When the difference in plant spacing exceeds the density deviation threshold and the leaf area index is less than or equal to the plant coverage baseline, the corresponding area is determined to be an overly sparse area and water supply is increased to generate regional water regulation values. Step 003: Acquire photosynthesis sensor data, monitor photosynthetic rate, transpiration rate, and leaf temperature, integrate the photosynthetic rate, transpiration rate, and leaf temperature of each monitoring point by region to form photosynthetic parameter values, perform ratio calculation on the photosynthetic rate and leaf area index value in the photosynthetic parameter values ​​to generate health index values, and analyze the health index values ​​and plant spacing difference values ​​to determine the degree of matching between crop density and photosynthesis in each region, and generate crop health assessment values; Step 004: Based on the crop health assessment value and combined with real-time environmental data such as soil moisture and temperature, adjust the planting density. Based on the environmental parameter values, compare the difference coefficient between regions C=σ / μ, where σ is the standard deviation and μ is the mean. Calculate the difference in the number of plants and the difference in plant spacing between adjacent regions. Combine the difference coefficient with the difference in the number of plants and the difference in plant spacing to obtain the difference in population distribution between regions. Use this difference as the density difference value. Call the density difference value and the crop health assessment value, combined with the soil moisture value, to generate the optimized planting density value. Step 005: Based on the regional water regulation value and the optimized cultivation density value, adjust the irrigation system. By monitoring the cultivation density and water demand, adjust the irrigation amount to ensure a reasonable match between water and density, and generate irrigation amount adjustment results.

2. The cultivation method according to claim 1, characterized in that, The preliminary density assessment values ​​include density deviation range, uniformity index, and adjustment demand determination; the regional water control values ​​specifically include water supply reduction, water supply increase, and control execution range; the crop health assessment values ​​include photosynthetic rate level, transpiration intensity index, and leaf temperature coefficient; the optimized cultivation density values ​​specifically refer to spatial distribution coefficient, regional density difference, and plant distribution uniformity; the irrigation adjustment results include total water consumption, regional water supply ratio, and fertilizer application ratio.

3. The cultivation method according to claim 1, characterized in that, Step 001 specifically includes: Step 011: Collect data from the field plant positioning sensors, record the plant spacing information of each corn plant, calculate the point-by-point difference between adjacent plant spacings, and collect all the difference data in the order of regions to generate a plant spacing difference sequence. Step 012: Based on the plant spacing difference sequence, the values ​​in each region are statistically summarized, the weighted calculation method is used to obtain the overall difference, and all values ​​are integrated by region to generate the plant spacing difference value. Step 013: Based on the plant spacing difference value, call the preset density uniformity standard, compare the deviation between the two, screen out the areas that need to be adjusted, and obtain the preliminary density assessment value.

4. The cultivation method according to claim 1, characterized in that, Step 003 specifically includes: Step 031: Acquire photosynthesis sensor data, record photosynthetic rate, transpiration rate and leaf temperature, integrate the data from each monitoring point in order of region to form a continuous photosynthetic efficiency record, and generate photosynthetic parameter values. Step 032: Call up the photosynthetic parameter values ​​and compare them with the leaf area index values ​​to determine whether there is insufficient photosynthesis or excessive consumption in the plant population in the region. Calculate the health index range based on the comparison between the two and generate the health index values. Step 033: Analyze the health index values ​​and plant spacing differences to determine the degree of matching between crop density and photosynthesis in each region, and generate crop health assessment values.

5. The cultivation method according to claim 1, characterized in that, Step 005 specifically includes: Step 051: Based on the regional water regulation value and the optimized cultivation density value, obtain the water flow and valve opening parameters of the irrigation system, compare the regulation requirements of different regions with the current operating parameters of the irrigation equipment, and generate irrigation parameter difference values. Step 052: Based on the differences in irrigation parameters, monitor the density distribution and real-time water demand of the cultivation area, adjust the water flow of the irrigation equipment to the range corresponding to the density demand of the area, and generate a water matching degree value. Step 053: Call the water matching degree value, calculate the difference between the required irrigation amount and the actual water supply of the equipment in each area, adjust the water supply range and determine the water supply ratio of each area, and generate the irrigation amount adjustment result.

6. A precision cultivation system for medium-late maturing maize based on density control, used to perform the method according to any one of claims 1-5, characterized in that, The system includes: The plant spacing acquisition module acquires positioning data from field plant positioning sensors, collects the plant spacing of individual corn plants, calculates the difference between plant spacing of adjacent plants, and generates plant spacing difference values. The uniformity calculation module, based on the plant spacing difference value, calls the regional leaf area index data, performs a set operation on the two, calculates the regional plant distribution uniformity index, and generates the uniformity index. The photosynthesis monitoring module collects photosynthetic rate, leaf temperature and transpiration rate data from the photosynthesis sensor based on the uniformity index value, compares the various sensor data with the uniformity index value, and generates photosynthetic status value. The density control module collects soil moisture sensor and temperature data from meteorological monitoring station for photosynthetic state value. It jointly judges the photosynthetic state, soil moisture value and temperature value. When the photosynthetic state rate is low and the soil moisture is at the drought threshold, it is determined that the cultivation density needs to be adjusted. When the photosynthetic state rate is high and the soil moisture is at the saturation threshold, it is determined that the cultivation density needs to be reduced. Based on the judgment results, the regional cultivation plant spacing is reset and a density control value is generated. Irrigation linkage module: calls the density control value, collects the water output data of regional irrigation equipment, compares the water output with the density control value, adjusts the regional irrigation control parameters, and generates irrigation allocation values.

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