Corn kernel maturity monitoring method based on red edge normalized vegetation index
By using a method based on the red-edge normalized vegetation index and leveraging UAV imagery and the PROSAIL model, the moisture content of maize kernels can be rapidly monitored, solving the problem of monitoring maize maturity and improving harvesting efficiency and crop quality.
Patent Information
- Application Number
- CN202511488334.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-17
- Publication Date
- 2026-01-13
AI Technical Summary
Existing technologies make it difficult to quickly and accurately monitor kernel moisture content during the corn ripening period, affecting the scheduling of harvest time and resulting in poor yield and quality.
A method based on red-edge normalized vegetation index was adopted. Maize reflectance data was collected by UAV imagery, and the PROSAIL model was used to simulate leaf reflectance. By combining cost function and regression model, the relationship between vegetation index and maize kernel moisture content was analyzed to achieve rapid maturity monitoring.
It enables rapid identification of kernel moisture content during the corn maturity stage, improves the efficiency of harvest scheduling, enhances corn harvest efficiency, and ensures yield and quality.
Smart Images
Figure CN121324349A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of maturity monitoring, in particular to a corn kernel maturity monitoring method based on red edge normalized difference vegetation index. BACKGROUND
[0002] Grain Moisture Content (GMC) variation analysis of corn at the mature stage provides important reference information for the development of corn harvesting sequence and precision agriculture applications. Corn is an important food crop in China and plays a dominant role in the country's food supply. High corn yields are crucial to the country's food security and food industry. For corn, early or late harvesting can have an impact on its yield and quality, so it is important to ensure corn yield and better arrange the harvesting date of corn labor or machinery by retrieving the maturity of corn.
[0003] Compared with satellite remote sensing, which is easily affected by factors such as atmosphere, cloud cover, rainy and snowy weather, revisit period, and mixed pixels in the image, it is difficult to meet the needs of high-frequency, high-resolution data in specific areas. Unmanned aerial vehicle remote sensing technology has the advantages of convenience, speed, high temporal and spatial resolution of images, low operational requirements and cost, etc. It greatly promotes the timely and rapid acquisition and long-term dynamic monitoring of crop, vegetation, water, soil and other agro-ecological environment element information, and gradually becomes the main data source for the development of precision agriculture and provides an effective solution for crop growth monitoring. Therefore, exploring crop maturity monitoring methods based on unmanned aerial vehicle image vegetation index is one of the effective ways to improve crop yield and quality. SUMMARY
[0004] The purpose of the present application is to provide a corn kernel maturity monitoring method based on red edge normalized difference vegetation index, which can quickly identify the moisture content of corn kernels at the corn mature stage through image vegetation index, so as to realize the maturity monitoring of corn crops and the arrangement of harvesting time.
[0005] To achieve the above purpose, the present application provides a corn kernel maturity monitoring method based on red edge normalized difference vegetation index, comprising the following steps: Step one, extracting corn reflectance from unmanned aerial vehicle image; Step two, obtaining corn leaf and canopy parameters, and solar and unmanned aerial vehicle sensor parameters for PROSAIL model simulation; Step three, simulating reflectance scale conversion; Step four, determining the best match through the cost function; Step five, correlation analysis of vegetation index, canopy chlorophyll and corn kernel moisture content; Step six, vegetation index sensitivity analysis; Step seven, determine the optimal vegetation index and corn maturity mapping.
[0006] Preferably, in step one, unmanned aerial vehicle remote sensing image data of red, green, blue, red edge, near infrared 5 multi-spectral sensor channels are collected, radiation calibration and flight height are set before unmanned aerial vehicle flight, and the collected image is imported into Pix4D mapper software for image stitching after unmanned aerial vehicle flight, to obtain corn field image reflectivity data.
[0007] Preferably, in step two, corn leaf and canopy parameters are obtained according to actual field collection, and the data range of solar parameters and sensor parameters is set.
[0008] Preferably, in step three, the reflectivity simulation conversion strategy based on spectral response function converts the spectral reflectivity simulated based on PROSAIL model with 1nm interval to the reflectivity of each wavelength range of multi-spectral image, as shown in the following formula: ; is the simulated unmanned aerial vehicle band reflectivity; 、 are the maximum and minimum wavelength ranges of different unmanned aerial vehicle bands respectively; is the spectral reflectivity simulated by PROSAIL model, spectral response function.
[0009] Preferably, in step four, the cost function is used to find the best match between the simulated leaf reflectivity and the ground reflectivity obtained by unmanned aerial vehicle image observation, the least square error LSE is used as the cost function, the top 10% of the optimal solution corresponding to the minimum LSE value is selected and the average value is calculated as the final inversion result of leaf chlorophyll content LCC and leaf area index LAI, and the cost function is as follows: ; wherein represents the true value, represents the estimated value, represents the number.
[0010] Preferably, in step five, correlation analysis is used to verify different regression models, and the regression models include linear, exponential, power and logarithmic models.
[0011] Preferably, in step six, in the sensitivity analysis of vegetation index, the relative change of vegetation index value is quantified based on the change of canopy chlorophyll content of corn in the mature period, and the calculation formula of sensitivity analysis of corn mature period vegetation index is as follows: (8) wherein and are the vegetation index values corresponding to the maximum and minimum values of CCC in the corn mature period, respectively.
[0012] Therefore, the present application has the following beneficial effects by using the above-mentioned corn kernel maturity monitoring method based on the red edge normalized vegetation index: (1) The method of the present application can quickly identify the water content of corn kernels in the corn mature period through the image vegetation index, so as to realize the maturity monitoring of corn crops and the arrangement of the harvesting time, save time, and improve the efficiency of subsequent harvesting of corn crops; (2) Compared with the existing corn maturity determination method, the method of the present application innovatively uses the change of corn canopy chlorophyll as the judgment medium and combines the selected vegetation index to quickly invert the field scale corn maturity.
[0013] The technical solutions of the present application will be further described in detail below with the aid of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS
[0014] Figure 1 is a method flowchart of the embodiment of the present application; Figure 2 is a relative change (ΔVI) graph of the vegetation index of corn in the mature period under different CCC values of the embodiment of the present application; Figure 3 is a cross-validation scatter plot of the estimated corn kernel water content GMC based on the VIs empirical model in the field WQG2 and SLC of the embodiment of the present application and the ground measured GMC; Figure 4 is a corn kernel water content inversion result graph based on the optimal vegetation index NDRE in the corn mature period of the embodiment of the present application. DETAILED DESCRIPTION
[0015] In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear and understandable, the embodiments of the present application will be further described in detail below with the aid of the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present application and do not limit 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 labor fall within the scope of the present application. The examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout.
[0016] It is to be understood that the terms "including", "comprising", "having" and
[0017] Similar reference numerals and letters in the various figures indicate similar items, and once an item is defined in one figure, that definition should be applied to other drawings containing the same item.
[0018] In the description of the application, it should be noted that the terms "upper", "lower", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the application is usually placed, and are only for the convenience of describing the application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application.
[0019] In the description of the application, it should be noted that, unless otherwise explicitly specified and limited, the terms "arrangement", "installation", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication between the two elements inside. For those skilled in the art, the specific meaning of the above terms in the application can be understood according to the specific circumstances.
[0020] Embodiments As Figure 1 shown, the corn kernel maturity monitoring method based on the red edge normalized vegetation index of the application comprises the following steps: Step one, extract the corn reflectivity of the unmanned aerial vehicle image.
[0021] Specific content: Collect multispectral unmanned aerial vehicle image data, which are unmanned aerial vehicle remote sensing images of red, green, blue, red edge, and near-infrared five multispectral sensor channels, respectively. The center wavelengths of the five multispectral channels are 650, 560, 450, 730, and 840 nm, respectively, and the bandwidths are 32, 32, 32, 32, and 52 nm, respectively. The images are taken between 11-14 o'clock in the morning on a clear and cloudless day. The heading and lateral overlap are 80% and 70%, respectively. Before flying, the camera is radiometrically calibrated using the radiometric calibration plate. The unmanned aerial vehicle is set to fly at a height H of 190 m. After the flight, the images are imported into the Pix4D mapper software for image stitching to obtain the corn field image reflectance data. The ground sampling distance GSD = (H / 18.9) cm / pixel.
[0022] Step two, obtain the corn leaf and canopy parameters, and the sun, unmanned aerial vehicle sensor parameters for PROSAIL model simulation.
[0023] Specific content: The corn canopy parameters are obtained according to actual field collection. The sun parameters, sensor parameters, and other parameters involved are set to a certain data range. Table 1 shows the parameter settings of the PROSAIL model.
[0024] Table 1 PROSAIL model parameter settings
[0025] Step three, simulate reflectance scale conversion.
[0026] Based on the PROSAIL model simulation of spectral reflectance, the spectral curve interval is 1 nm, but the multispectral unmanned aerial vehicle image band is within a certain spectral range, as shown in Table 2. There is a significant scale effect between the two. Based on the conversion strategy of the spectral response function, the simulated 1 nm interval spectral reflectance is converted to the reflectance of each wavelength range of the multispectral image band, as shown in formula (1).
[0027] Table 2 Multispectral band information
[0028] Based on the spectral response function, the reflectance simulation conversion strategy is as follows: (1); is the simulated unmanned aerial vehicle band reflectance; , are the maximum and minimum wavelength ranges of the unmanned aerial vehicle different bands, respectively; is the PROSAIL model simulated spectral reflectance, spectral response function.
[0029] Step four: Determine the best match using the cost function.
[0030] Specific steps: Use a cost function to find the best match between the simulated leaf reflectance and the ground reflectance obtained from UAV imagery. The least squares error (LSE) is used as the cost function as shown in formula (2). The top 10% of the optimal solutions corresponding to the minimum LSE value are selected and their average value is calculated as the final inversion results of chlorophyll (LCC) and leaf area index (LAI) to reduce the impact of uncertainties caused by measurement errors and insufficient models.
[0031] The cost function is as follows: (2); in Represents the true value. Represents the estimated value. Represents the number.
[0032] Step 5: Correlation analysis of vegetation index, canopy chlorophyll and maize grain moisture content (GMC).
[0033] Correlation analysis was used to validate different regression models, including linear (y=ax+b) and exponential (y=a) regression models. x ), power (y=x) a ) and logarithmic model (y=Log a X).
[0034] Specific steps: Correlation analysis was performed by selecting different regression models. Among all the selected regression models, the logarithmic model obtained the optimal R-squared value. 2 Table 3 describes the fitting relationship between vegetation indices (VIs) and grain moisture content (GMC) at maturity in maize. The p-values for all parameters in the different regression models are <0.01, indicating that the established regression models have significant statistical value.
[0035] Table 3. Fitting equations and R values between canopy chlorophyll (CCC) and vegetation index and maize grain moisture content in maize fields. 2 value
[0036] Table 4. Calculation formulas and meanings of vegetation index
[0037] Step 6: Sensitivity analysis of vegetation indices.
[0038] Specific steps: In the sensitivity analysis of vegetation index, the relative change of vegetation index value is quantified based on the change of canopy chlorophyll content during the maturity period of maize. The specific method refers to the research of Sun et al. (2019), which studied the relative change of VIs value caused by the change of maize canopy chlorophyll CCC during the crop maturity period (Sun et al., 2019), as shown in formula (8). and These represent the vegetation index values corresponding to the maximum and minimum CCC values at maize maturity. The sensitivity analysis results for each vegetation index at maize maturity are as follows: Figure 2 As shown.
[0039] (8); Step 7: Determine the optimal vegetation index and map the maize maturity level.
[0040] Specific steps: As can be seen from Table 3, among all vegetation indices, NDRE showed the best fit with the grain moisture content (GMC) at maize maturity. 2 The values were all greater than 0.6, significantly higher than LCI (0.566 and 0.437), GNDVI (0.520 and 0.374), NDVI (0.501 and 0.359) and RENDVI (0.262 and 0.200). Figure 3 The scatter plot generated by the cross-validation process is shown, where the corn kernel moisture content (GMC) is estimated based on the VIs logarithmic empirical model. Figure 4 This is an inversion diagram of the mature maize kernel moisture content at maturity for two fields based on the optimal vegetation index NDRE.
[0041] The method of this invention can explain more than 60% of the changes in grain moisture content of mature crops, indicating a certain degree of correlation, and R at the field scale. 2 The values did not change significantly, indicating that the vegetation index method is not greatly affected by the external environment.
[0042] Detailed explanation: Combining the preceding experimental data, experimental results, and R... 2 As a result, the R² of the GMC for maize based on vegetation index methods (as shown in Table 3) is: WQG 2 =0.600, SLC=0.619), the coefficient of determination R of the fitting results 2 Above 0.6, it can explain more than 60% of the changes in grain moisture content in mature crops, indicating a certain degree of correlation, and at the field scale, R0... 2The values showed little variation, indicating that the vegetation index method is not significantly affected by the external environment. This example demonstrates a relatively close relationship between the spectrum, canopy chlorophyll, and grain moisture content, providing a reference for future exploration of deeper applications and promotion to other related fields and crops in precision agriculture.
[0043] Therefore, the present invention adopts the above-mentioned method for monitoring maize kernel maturity based on red-edge normalized vegetation index. By using the image vegetation index, the moisture content of maize kernels can be quickly identified during the maize maturity period, so as to realize the monitoring of maize crop maturity and the arrangement of harvest time.
[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.
Claims
1. A method for monitoring maize kernel maturity based on red-edge normalized vegetation index, characterized in that: Includes the following steps: Step 1: Extract the corn reflectance from the drone image; Step 2: Obtain corn leaf and canopy parameters, as well as solar and drone sensor parameters, for PROSAIL model simulation; Step 3: Simulate reflectivity scale conversion; Step four: Determine the best match using the cost function; Step 5: Correlation analysis of vegetation index, canopy chlorophyll, and maize kernel moisture content; Step 6: Sensitivity analysis of vegetation indices; Step 7: Determine the optimal vegetation index and map the maize maturity level.
2. The method for monitoring maize kernel maturity based on red-edge normalized vegetation index according to claim 1, characterized in that: In step one, UAV remote sensing image data from five multispectral sensor channels (red, green, blue, red edge, and near-infrared) are collected. Radiometric calibration and flight altitude are set before the UAV flies. After the UAV flies, the collected images are imported into Pix4D mapper software for image stitching to obtain cornfield image reflectance data.
3. The method for monitoring maize kernel maturity based on red-edge normalized vegetation index according to claim 1, characterized in that: In step two, the parameters of corn leaves and canopy are obtained based on actual field data collection, and the data ranges for solar parameters and sensor parameters are set.
4. The method for monitoring maize kernel maturity based on red-edge normalized vegetation index according to claim 1, characterized in that: In step three, a reflectance simulation conversion strategy based on the spectral response function is used to convert the spectral reflectance simulated based on the PROSAIL model at 1nm intervals into reflectance for each wavelength range of the multispectral image, as shown in the following formula: ； The simulated UAV band reflectivity; , These represent the maximum and minimum wavelength ranges for different bands of the UAV; The spectral reflectance simulated by the PROSAIL model. Spectral response function.
5. The method for monitoring maize kernel maturity based on red-edge normalized vegetation index according to claim 1, characterized in that: In step four, a cost function is used to find the best match between the simulated leaf reflectance and the ground reflectance obtained from UAV imagery. The least squares error (LSE) is used as the cost function. The top 10% of the optimal solutions corresponding to the minimum LSE value are selected, and their average value is calculated as the final inversion result of chlorophyll LCC and leaf area index (LAI). The cost function is as follows: ； in Represents the true value. Represents the estimated value. Represents the number.
6. The method for monitoring maize kernel maturity based on red-edge normalized vegetation index according to claim 1, characterized in that: In step five, correlation analysis is used to validate different regression models, including linear, exponential, power, and logarithmic models.
7. The method for monitoring maize kernel maturity based on red-edge normalized vegetation index according to claim 1, characterized in that: In step six, the sensitivity analysis of vegetation indices quantifies the relative changes in vegetation index values based on changes in canopy chlorophyll content during maize maturity. The formula for calculating the sensitivity analysis of vegetation index at maize maturity is as follows: （8）； in and These are the vegetation index values corresponding to the maximum and minimum CCC values at the maize maturity stage.