A precise fertilization and irrigation method and system based on corn and soybean ontology information

Abstract

The present invention discloses a precision fertilization and irrigation method and system based on corn and soybean ontology information, comprising the following steps: establishing a leaf nitrogen content estimation model and a moisture estimation model based on spectral information based on the full growth period growth data of the two crops, installing an innovatively developed spectral probe sensor, installing an environmental monitoring station in the field, and obtaining predicted nitrogen and moisture contents based on the available canopy spectral information of corn and soybean leaves collected by the spectral equipment. The precision fertilization and irrigation method and system based on corn and soybean ontology information of the present invention, by establishing a leaf nitrogen content estimation model for corn and soybean, can accurately calculate the nitrogen content of corn and soybean at different growth stages, accurately derive the nitrogen status of the crops based on the actual measured spectral data, thereby achieving on-demand fertilization, avoiding nutrient deficiencies or excesses that may occur in traditional fertilization, improving fertilizer utilization, and reducing fertilizer waste.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent water-fertilizer integration, and in particular to a precise fertilization and irrigation method and system based on corn and soybean ontology information. Background Art

[0002] Corn and soybeans, as important dual-purpose crops for food and cash crops, are crucial to food security. In recent years, given limited arable land resources, effectively increasing corn and soybean yields without increasing cultivated area to ensure food security has become a pressing issue. Adopting a corn and soybean strip-cropping system can achieve efficient resource utilization, increase land productivity, and boost corn and soybean yields, making it an effective way to increase soybean yields and expand soybean acreage.

[0003] In corn-soybean strip cropping, the two crops have significantly different water and fertilizer requirements. Corn requires large amounts of water and nitrogen fertilizer to grow, while soybeans, a nitrogen-fixing crop, require less nitrogen fertilizer. This leads to rough field management and excessive application of water and fertilizer, resulting in uneven fertilization and fertilizer waste. This leads to excessive soybean growth, excessive growth, and lodging, resulting in reduced yields. Although some farmers have adopted integrated water and fertilizer application techniques, whether in corn-soybean strip cropping or in monoculture, they often rely more on subjective and empirical judgments about water and fertilizer shortages. This extensive water and fertilizer management can lead to inefficient water and fertilizer resource utilization.

[0004] Crop ontology information can directly reflect the nutrient and water status of crops. Previous theoretical research has focused on monitoring and predicting water and nitrogen content in crop leaves, but this research has not been applied to production practices. In particular, there is a lack of crop ontology information models and systems that can be integrated with integrated water-fertilizer and Internet of Things technologies for precision fertilization and irrigation. Therefore, a method and system for precision fertilization and irrigation based on corn and soybean ontology information is urgently needed. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method and system for precise fertilization and irrigation based on corn and soybean ontology information, the purpose of which is to solve the problems raised in the above-mentioned background technology.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0007] A precise fertilization and irrigation method based on corn and soybean ontology information comprises the following steps:

[0008] S1: Based on the full-growing growth data of the two crops, a spectral-based nitrogen content model for corn and soybeans and a general model for corn and soybean leaf moisture content were established. The feasibility of the model was verified on an independent sample dataset.

[0009] S2: Installing a spectrum probe sensor based on the sensitive spectrum band information contained in the model constructed in step S1;

[0010] S3: Install an environmental monitoring station in the field, which includes temperature and humidity, wind speed and direction sensors, and a sensor that can sense external irradiance. The environmental monitoring station monitors temperature and humidity, wind speed, wind direction, and light intensity in real time, and imports the collected spectral reflectance data into the intelligent water and fertilizer integrated control platform;

[0011] S4: Substituting the canopy spectral information of corn and soybean leaves collected by the spectral equipment into the model in step S1 to obtain the predicted nitrogen content and water content;

[0012] S5: The minimum thresholds for nitrogen and water content corresponding to nutrient and water deficits are set in advance in the intelligent integrated water and fertilizer control platform. When the model predicts that the predicted value is lower than the set minimum threshold for a period of time, the system automatically makes a judgment through the built-in program, opens the dual-channel solenoid valve, and then water and fertilizer enter the drip tape through pipe A and are finally replenished to the crops. Here, a fixed drip irrigation time is set. When the time expires, the system automatically terminates and closes the valve. After 24 hours, the spectral information of the crop canopy is repeatedly collected and substituted into the model to calculate the current nutrient and water content. If it is still lower than the set minimum threshold, the above steps are repeated. If it is higher than the set minimum threshold, the integrated water and fertilizer drip irrigation task is terminated.

[0013] In step S1, the nitrogen content model of corn and soybean is:

[0014] Corn: Y (nitrogen content of corn leaves) = 0.1988*(RVI(696,531))2 + 18.756*RVI(696,531) + 124.31, R2 = 0.868, R2 of independent sample test = 0.915, RMSE = 5.022;

[0015] Soybean: Y (soybean leaf nitrogen content) = -0.0387 (RVI (723,531))2 + 6.318* RVI (723,531) + 0.7848, R2 = 0.813, R2 of independent sample data set test = 0.852, RMSE = 3.962;

[0016] In step S1, the general model of moisture content of corn and soybean leaves is:

[0017] Y=-31.439*(NDVI(1498,1551))2+ 8.6383* NDVI(1498,1551) + 0.2405,R2=0.565;

[0018] The R2 of the independent sample test is 0.344, and the RMSE is 0.153.

[0019] Furthermore, in step S2, the spectrum probe sensor has spectrum information of five independent sensitive bands (531 nm, 696 nm, 723 nm, 1498 nm, and 1551 nm).

[0020] Furthermore, in step S3, if the light intensity is lower than 90,000 Lux, it is determined to be cloudy / overcast, and the collected spectral sensor data is automatically filtered out; if the light intensity is higher than 90,000 Lux, it is determined to be sunny.

[0021] A precise fertilization and irrigation system based on corn and soybean ontology information includes a water source, a constant pressure controller, a debris filter 1, a debris filter 2, a fertilizer storage tank, a water and fertilizer integrated control platform module, and an environmental monitoring station module.

[0022] The beneficial effects of the present invention are:

[0023] The present invention provides a precision fertilization and irrigation method and system based on corn and soybean ontology information. By establishing a leaf nitrogen content estimation model for corn and soybeans, the nitrogen content of corn and soybeans at different growth stages can be accurately calculated. The nitrogen status of the crops can be accurately derived based on the actual measured spectral data, thereby achieving on-demand fertilization, avoiding nutrient deficiencies or excesses that may occur in traditional fertilization, improving fertilizer utilization, and reducing fertilizer waste.

[0024] Using a universal model for corn and soybean leaf moisture content, the system monitors crop moisture status in real time. Based on the model's calculations and set moisture thresholds, the system automatically controls irrigation, ensuring that crops receive the appropriate amount of water at each growth stage. This prevents drought-induced growth suppression and over-irrigation-related water waste and soil compaction.

[0025] This method integrates an intelligent integrated water and fertilizer control platform, eliminating the need for manual intervention throughout the entire process, from spectral data collection and nutrient and moisture content calculation to the automatic supply of water and fertilizer. Once thresholds and relevant parameters are set, the system automatically operates based on the actual needs of the crop, saving significant labor and time costs and improving agricultural production efficiency.

[0026] The system's environmental monitoring station provides real-time monitoring of environmental factors such as temperature, humidity, wind speed, wind direction, and light intensity. Spectral sensor data is automatically filtered when light intensity falls below 90,000 Lux, preventing data errors that can occur under poor lighting conditions such as overcast or cloudy skies. This allows the system to better adapt to varying environmental conditions and ensures the stability of data collection and fertilization and irrigation decisions.

[0027] Precision fertilization and irrigation can create a suitable growth environment for corn and soybeans, allowing the crops to obtain sufficient nutrients and water throughout their growth period, which helps the development of crop roots, plant growth and photosynthesis, enhances crop resistance, reduces the incidence of diseases and pests, and improves the quality and safety of agricultural products.

[0028] By precisely meeting the water and fertilizer needs of corn and soybeans, the growth potential of crops is fully utilized. In conjunction with other cultivation measures such as reasonable density planting, the yield and total output of corn and soybeans can be effectively increased, increasing farmers' economic benefits.

[0029] Quasi-irrigation avoids the waste of water resources caused by flooding, and performs drip irrigation according to the actual water requirements of crops, thereby improving the efficiency of water resource utilization, helping to ease the tense situation of agricultural water use, and achieving sustainable use of water resources.

[0030] Precision fertilization reduces excessive fertilizer application and reduces the risk of non-point source pollution caused by fertilizer loss into water bodies with rainwater. It also reduces problems such as soil compaction and soil pollution caused by unreasonable fertilization, which is beneficial to protecting the agricultural ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of the precise fertilization and irrigation method and system based on corn and soybean ontology information of the present invention;

[0032] Figure 2 Schematic diagram of the core components of the present invention;

[0033] Figure 3 This is a schematic diagram of the operation process of the intelligent water and fertilizer integrated control platform of the present invention;

[0034] Figure 4 Schematic diagram of the environmental monitoring station of the present invention. DETAILED DESCRIPTION

[0035] The specific embodiments of the present invention are further described below with reference to the accompanying drawings, wherein the same parts are represented by the same reference numerals.

[0036] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the accompanying drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0037] In order to make the contents of the present invention more clearly understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0038] A method and system for precise fertilization and irrigation based on corn and soybean ontology information, comprising the following steps:

[0039] 1. Based on the full-growing growth data of the two crops, a spectral-based nitrogen content model for corn and soybeans and a general model for corn and soybean leaf moisture content were established, and the feasibility of the model was verified on an independent sample data set.

[0040] 2. Based on the sensitive spectral band information contained in the model constructed in step 1, install a spectral probe sensor. This probe has spectral information of five independent sensitive bands (531nm, 696nm, 723nm, 1498nm, 1551nm). There is no need to install a probe that can perceive full spectral information, and the equipment cost is low.

[0041] 3. Install an environmental monitoring station in the field. This station includes conventional sensors for temperature, humidity, wind speed, and direction, as well as a sensor that detects external irradiance to determine whether the weather is sunny or cloudy. If the light intensity is less than 90,000 Lux, it is considered cloudy / overcast, and the collected spectral sensor data is automatically filtered out. If the light intensity is greater than 90,000 Lux, it is considered sunny, and the collected spectral reflectance data is imported into the intelligent integrated water and fertilizer control platform.

[0042] 4. Based on the available canopy spectral information of corn and soybean leaves collected by the spectral equipment, substitute it into the model in step 1 to obtain the predicted nitrogen content and moisture content.

[0043] 5. Pre-set the minimum nitrogen and water content thresholds for nutrient and water deficits in the intelligent fertigation control platform (Tables 1 and 2). When the model predicts that the predicted values ​​remain below the thresholds for a period of time, the system automatically determines, using a built-in program, that they will open the dual-path solenoid valve. Water and fertilizer will then flow through pipe A into the drip irrigation strips and ultimately to the crops. A fixed drip irrigation time (for example, 90 minutes) is set. Upon expiration, the system automatically terminates the drip irrigation process and closes the valves. After 24 hours, spectral data from the crop canopy is collected again and applied to the model to calculate the current nutrient and water content. If the values ​​remain below the thresholds, the above steps are repeated. If they exceed the thresholds, the fertigation drip irrigation task is terminated.

[0044] 6. In the present invention, the differences in the amount and period of demand for fertilizer and water between corn and soybeans are taken into consideration. In addition, the growth period of corn and soybeans is in spring and summer, and they are prone to high temperature and drought weather. Compared with lack of fertilizer, water shortage is more likely to occur, and the sensitive bands of fertilizer model and water are quite different. Therefore, a separate pipe that does not flow through the fertilizer storage barrel is set up. The pipe is directly connected to the intelligent water-fertilizer integrated control platform. When water needs to be supplemented but fertilizer is not needed, the system responds and opens one side of the dual-channel solenoid valve to allow water to flow through the pipe alone and finally replenish the crops. By setting up such an independent pipe, water is supplemented without causing an excess of fertilizer.

[0045] The present invention constructs a spectral model suitable for rapid diagnosis of nitrogen and water content in corn and soybean leaves throughout their growth period, and verifies it using an independent sample set, demonstrating the feasibility of the model.

[0046] The specific nitrogen content models for corn and soybeans are:

[0047] Corn: Y (corn leaf nitrogen content) = 0.1988 * (RVI (696,531))² + 18.756 * RVI (696,531) + 124.31, R² = 0.868. Independent samples R² = 0.915, RMSE = 5.022.

[0048] Soybean: Y (soybean leaf nitrogen content) = -0.0387(RVI(723,531))² + 6.318*RVI(723,531) + 0.7848. R² = 0.813, independent sample data set R² = 0.852, RMSE = 3.962. The model construction and testing demonstrates high accuracy and suitability for practical production.

[0049] The general model for corn and soybean leaf moisture content is Y = -31.439 * (NDVI (1498, 1551))² + 8.6383 * NDVI (1498, 1551) + 0.2405, with an R² of 0.565. The independent sample test R² is 0.344, and the RMSE is 0.153. This model calculates the real-time percentage moisture content of the leaf.

[0050] In this invention, the aforementioned model is incorporated into an intelligent integrated water and fertilizer control platform. By placing a five-band spectral sensor (531nm, 696nm, 723nm, 1498nm, and 1551nm) above the corn and soybean canopies, the system collects the crop canopy's spectral reflectance. This spectral information is then transmitted to the platform and fed into the model, enabling rapid analysis of nutrient and moisture parameters for corn and soybeans.

[0051] like Figure 1 As shown, the present invention includes a water source, a constant pressure controller, a debris filter 1, a debris filter 2, a fertilizer storage tank, a water-fertilizer integrated control platform module, and an environmental monitoring station module. The present invention will be described in detail below with reference to the accompanying drawings.

[0052] In the present invention, river water or well water is extracted through a constant pressure controller, and then passed through two debris filters to filter out debris such as silt, weeds, etc. that may clog the pipeline.

[0053] The water then flows into the core component of the present invention, such as Figure 2 As shown, A. A single water pipe. B. Water and fertilizer pipe. C. Fertilizer storage tank: stores liquid fertilizer. D. A dual-channel solenoid valve connects the two pipes, and its opening and closing are controlled by the intelligent water and fertilizer integrated control platform. E. The intelligent water and fertilizer integrated platform is the core of this system. Its built-in system stores two core models: the fertilizer requirement model for corn and soybeans, and the water requirement model for corn and soybeans. When the model calculations indicate that the plants are water deficient, the platform controls the opening and closing of the dual-channel solenoid valve, allowing only water to flow through the single water pipe. When the model calculations indicate that the plants are nitrogen deficient, the solenoid valve is controlled to allow only water to flow through the water and fertilizer pipe. Here, the integrated water and fertilizer control platform controls the opening and closing of the solenoid valve to divert water flow to pipes A and B respectively. When only nutrients or both nutrients and water are needed, the solenoid valve is controlled to direct water flow through pipe A. When only water is needed, the solenoid valve is controlled to direct water flow through pipe B.

[0054] The program workflow of the intelligent water and fertilizer integrated control platform is as follows: Figure 3 After the program starts, open the valve to allow water to fill the fertilizer storage tank, and automatically stir to mix the fertilizer and water thoroughly.

[0055] After the program is started, the spectral probe in the environmental monitoring station begins to record the spectral reflectance information of the crop canopy in real time and transmits it to the system water and fertilizer integrated control platform. Figure 4 As shown, G. Spectral probe with rainproof cover, which can measure the spectral reflectance of plant canopy. H. White board. Used for reflectance calibration of probe. It can be removed and properly stored when not in use. I. Conventional environmental monitoring sensors. Including temperature and humidity, air pressure, wind speed and direction, solar irradiance sensors, wireless transmission equipment, solar panels, etc. J. Retractable and detachable spectral fixing frame. K. Retractable and detachable fixing frame is a schematic diagram of the environmental monitoring station. The specific operation method is as follows: When farmers come to the field and want to understand the current moisture and nutrient status of field crops, start the system, first confirm that the black rubber cover is covering the probe, and collect the dark current information at this time (that is, the reflectance when there is no light, theoretically 0). Then remove the black rubber cover of the probe and place the standard reflective white board ( Figure 4 H) Place the device approximately 10 cm below the probe to collect the system's photocurrent information (i.e., the reflectivity of a standard whiteboard, theoretically 1). Check the screen on the integrated water and fertilizer control platform to see if the standard reflectivity is normal. If so, proceed to the next step. The bracket connecting the whiteboard is retractable and foldable, and the whiteboard is also removable. When not in use, fold it up and place it under the awning or remove it.

[0056] Among them, J and K represent fixed frames, which can be disassembled, extended and rotated. Their function is to ensure that the spectral probe can always be located about 1m above the crop canopy according to the needs of different field configurations and different crops, so that the probe can completely cover the crop canopy according to the changes in the crop growth process, facilitating the collection of effective spectral reflectance information. It should be noted that Figure 4 The diagram shows two spectral sensors installed on the left and right sides, allowing for simultaneous determination of the moisture and nutrient status of corn and soybeans in a corn-soybean strip multicrop system. If farmers are using the system for monoculture of corn, soybeans, or other single crops, only one spectral sensor is required.

[0057] The available spectral reflectance is wirelessly transmitted to the system's pre-established water and nutrient models, which calculate the current water and nutrient status of the crop. The program pre-sets minimum water and nutrient parameter thresholds for each key growth period, and the model's parameters are compared with the pre-set parameters. If either nutrient or water value calculated by the spectral model falls below the set threshold, the system controls the valve to open, allowing water and nutrients to flow to the plant. Note that the system controls the valve to open regardless of whether only water, fertilizer, or both are deficient; the valve opening is controlled by different channels, as explained above. Minimum thresholds for crops at different growth periods are pre-set in the system by agricultural experts or experienced farmers.

[0058] The minimum threshold reference values ​​of moisture and nutrients for corn and soybeans provided by the present invention (Table 1 and Table 2) are as follows:

[0059] Table 1 System setting thresholds for moisture and nitrogen fertilizer content in corn leaves

[0060] Table 2. System setting thresholds for moisture and nitrogen fertilizer content in soybean leaves

[0061] Once the system is fully opened, it begins counting down, setting the irrigation time to 90 minutes. When the time expires, the system responds and the solenoid valve closes. After the plants have fully absorbed the nutrients (at intervals of 24 hours), the crop canopy's spectral data is used to monitor water and nutrient status again. If the water and nutrient levels remain below the set values ​​after irrigation, this cycle repeats until the model-calculated values ​​exceed the set values, at which point the program terminates.

[0062] When the value calculated by the model is lower than the set value, it means that the crop does not need to supplement nutrients and water at this time, and there is no need for irrigation and fertilization, and the program terminates.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A precise fertilization and irrigation method based on corn and soybean ontology information, characterized in that: The following steps are involved: S1: Based on the full-growing growth data of the two crops, a spectral-based nitrogen content model for corn and soybeans and a general model for corn and soybean leaf moisture content were established. The feasibility of the model was verified on an independent sample dataset. S2: Based on the sensitive spectral band information contained in the model constructed in step S1, a spectral probe sensor is installed; S3: Install an environmental monitoring station in the field, which includes temperature and humidity, wind speed and direction sensors, and a sensor that can sense external irradiance. The environmental monitoring station monitors temperature and humidity, wind speed, wind direction, and light intensity in real time, and imports the collected spectral reflectance data into the intelligent water and fertilizer integrated control platform; S4: Substituting the canopy spectral information of corn and soybean leaves collected by the spectral equipment into the model in step S1 to obtain the predicted nitrogen content and water content; S5: The minimum thresholds for nitrogen and water content corresponding to nutrient and water deficits are set in advance in the intelligent integrated water and fertilizer control platform. When the model predicts that the predicted value is lower than the set minimum threshold for a period of time, the system automatically makes a judgment through the built-in program, opens the dual-channel solenoid valve, and then water and fertilizer enter the drip tape through pipe A and are finally replenished to the crops. Here, a fixed drip irrigation time is set. When the time expires, the system automatically terminates and closes the valve. After 24 hours, the spectral information of the crop canopy is repeatedly collected and substituted into the model to calculate the current nutrient and water content. If it is still lower than the set minimum threshold, the above steps are repeated. If it is higher than the set minimum threshold, the integrated water and fertilizer drip irrigation task is terminated. In step S1, the nitrogen content model of corn and soybean is: Corn: Nitrogen content of corn leaves Y = 0.1988*(RVI(696,531))2 + 18.756*RVI(696,531) +124.31, R2 = 0.868, R2 of independent sample test = 0.915, RMSE = 5.022; Soybean: Soybean leaf nitrogen content Y = -0.0387 (RVI (723,531))2 + 6.318* RVI (723,531) + 0.7848, R2 = 0.813, R2 of independent sample data set test = 0.852, RMSE = 3.962; In step S1, the general model of moisture content of corn and soybean leaves is: Y=-31.439*(NDVI(1498,1551))2+ 8.6383* NDVI(1498,1551) + 0.2405,R2=0.565; The R2 of the independent sample test is 0.344, and the RMSE is 0.

153.

2. The method for precise fertilization and irrigation based on corn and soybean ontology information according to claim 1, characterized in that: In step S2, the spectrum probe sensor has five independent sensitive bands: spectral information of 531nm, 696nm, 723nm, 1498nm, and 1551nm.

3. The method for precise fertilization and irrigation based on corn and soybean ontology information according to claim 1, characterized in that: In step S3, if the light intensity is lower than 90,000 Lux, it is determined to be cloudy / overcast, and the collected spectral sensor data is automatically filtered out; if the light intensity is higher than 90,000 Lux, it is determined to be sunny.

4. A precision fertilization and irrigation system based on corn and soybean ontology information, comprising the precision fertilization and irrigation method based on corn and soybean ontology information according to any one of claims 1 to 3, characterized in that: It includes a water source, a constant pressure controller, a debris filter 1, a debris filter 2, a fertilizer storage tank, a water-fertilizer integrated control platform module and an environmental monitoring station module.

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