CN-121393630-B - Remote sensing monitoring method and system for total phenol content of wheat grains in saline-alkali soil

CN121393630BCN 121393630 BCN121393630 BCN 121393630BCN-121393630-B

Abstract

The invention belongs to the technical field of agricultural remote sensing monitoring, and discloses a remote sensing monitoring method and a remote sensing monitoring system for total phenol content of wheat grains in saline-alkali soil, which extract meteorological and vegetation characteristics through weather matching based on multi-source remote sensing data, and constructing a protein content estimation model containing a salt stress nonlinear inhibition term, establishing a quantitative relation between protein and total phenol content according to the protein content estimation model, and finally inverting by using an optimization algorithm to obtain high-precision total phenol content so as to realize effective monitoring of wheat grain quality in a large-scale saline-alkali soil. The invention initiates a secondary inversion framework of protein content-total phenol content, indirectly realizes total phenol content monitoring through a protein remote sensing inversion technology, effectively solves the technical problem of low direct spectrum inversion precision, innovatively establishes a saline-alkali soil specific stress response model, takes the nonlinear inhibition effect of salt on the physiological process of crops into consideration in the inversion process, remarkably improves the monitoring precision and model adaptability, and has the characteristics of high monitoring precision, strong practicability, wide application range and the like.

Inventors

HAN LIJING
Ge Xianghe
XU XIAOBIN
ZHANG XIAOKANG
WANG ZHENGDONG
WANG XIANG
LI ZHENHAI

Assignees

山东科技大学

Dates

Publication Date: 20260508
Application Date: 20251022

Claims (8)

1. A remote sensing monitoring method for total phenol content of wheat grains in saline-alkali soil is characterized by comprising the following steps: S1, constructing a soil salinity remote sensing estimation model, and estimating the soil salinity based on multi-source remote sensing data; S2, constructing a multidimensional characteristic parameter extraction model, determining a wheat growth key period by adopting a pixel-by-pixel weather matching method, and calculating the accumulated value and/or the maximum value of each meteorological characteristic parameter in the key period; s3, constructing a protein content estimation model, wherein the protein content estimation model is estimated based on vegetation indexes, multidimensional weather characteristic parameters and soil salt content, and the protein content estimation model comprises a nonlinear term which is negatively related to the soil salt content and is used for representing the inhibition effect of salt stress on protein synthesis; S4, constructing a total phenol content prediction model, and predicting the total phenol content of the wheat grains through a preset functional relation based on an output result of the protein content estimation model; S5, optimizing parameters of a total phenol content monitoring model by using an optimization algorithm, wherein the parameters comprise parameter initialization, fitness calculation and parameter optimization so as to minimize errors between a predicted value and an actual measured value, and the parameters of the total phenol content monitoring model comprise a soil salinity remote sensing estimation model, a multidimensional characteristic parameter extraction model, a protein content estimation model, a total phenol content prediction model parameter and an independent variable; The expression of the protein content estimation model is as follows: ; in the formula, In order to achieve a protein content, the protein content is, Are all parameters of the model, and are all parameters of the model, Is a vegetation index of the plant, For the salt content of the soil, In order for the attenuation coefficient to be a factor, Is an error term; For the cumulative value of the vapor-out, As an accumulated value of the precipitation amount, Is the cumulative value of temperature.
2. The method for remotely sensing and monitoring the total phenol content of wheat grains in saline-alkali soil according to claim 1, wherein in step S1, the estimating the salt content of the soil based on the multi-source remote sensing data is as follows: ; in the formula, For the salt content of the soil, For a functional relationship established based on multi-source telemetry data, Is a vegetation index of the plant, In the form of a salinity index, In order to achieve a cosmid content of the granules, Is a topographic parameter.
3. The remote sensing monitoring method for the total phenol content of wheat grains in the saline-alkali soil according to claim 1, wherein in the step S2, the pixel-by-pixel weather matching method is that three periods of 30 days are shifted forward from the annual date DOY in the mature period of wheat, and the accumulated value and/or the maximum value of characteristic parameters in each period are calculated, wherein the expression is that; ; ; ; ; in the formula, For the cumulative value of the vapor-out, As an accumulated value of the precipitation amount, As a cumulative value of the temperature of the liquid, Is the maximum value of the vegetation index, For the observations within each period of time, Is that The evapotranspiration value of the time period, Is that The magnitude of the precipitation in the time period, Is that The temperature value of the time period, In order to take the maximum value it is, Is that Vegetation index of the period.
4. The remote sensing monitoring method of the total phenol content of the wheat grains in the saline-alkali soil according to claim 1, wherein in the step S4, the expression of the total phenol content prediction model is: ; in the formula, For the total phenol content to be the same, Are all parameters of the model, and are all parameters of the model, Is the protein content.
5. The remote sensing monitoring method for total phenol content of wheat grains in saline-alkali soil according to claim 1, wherein in step S5, the optimizing the parameters of the total phenol content monitoring model by using an optimization algorithm comprises: determining an initial value and setting a parameter optimization range according to model parameters established by ground data; taking Root Mean Square Error (RMSE) of the measured value and the predicted value as an fitness function; and (3) carrying out iterative optimization through a genetic algorithm, minimizing the RMSE, and obtaining an optimized parameter value.
6. The remote sensing monitoring method for the total phenol content of wheat grains in saline-alkali soil according to claim 5, wherein the fitness function has a calculation formula as follows: ; in the formula, For the degree of fitness of the individual, As the actual measurement value of the total phenol content of the sample point, As a predicted value of the total phenol content of the sample points, The number of training samples.
7. The remote sensing monitoring method for the total phenol content of the wheat grains in the saline-alkali soil according to the claim 1 is characterized by further comprising the step of bringing the optimized parameter value into a total phenol content monitoring model after the step S5, and combining the characteristic parameters of the multiple dimensions as input to realize remote sensing monitoring of the total phenol content of the wheat grains in the saline-alkali soil.
8. A remote sensing monitoring system for the total phenol content of wheat grains in saline-alkali soil for implementing the remote sensing monitoring method for the total phenol content of wheat grains in saline-alkali soil according to any one of claims 1 to 7, comprising: the soil salinity remote sensing estimation module is used for constructing a soil salinity remote sensing estimation model and estimating the salinity of the soil based on multi-source remote sensing data; The multi-dimensional characteristic parameter extraction module is used for constructing a multi-dimensional characteristic parameter extraction model, determining a wheat growth key period by adopting a pixel-by-pixel weather matching method, and calculating accumulated values and/or maximum values of a plurality of meteorological characteristic parameters related to crop growth in the key period; The protein content estimation module is used for constructing a protein content estimation model, the protein content estimation model is estimated based on a vegetation index, multidimensional weather characteristic parameters and soil salt content, and the protein content estimation model comprises a nonlinear term which is negatively related to the soil salt content and is used for representing the inhibition effect of salt stress on protein synthesis; The total phenol content prediction module is used for constructing a total phenol content prediction model, and predicting the total phenol content of the wheat grains through a preset functional relation based on the output result of the protein content estimation model; The parameter optimization module is used for optimizing parameters of the total phenol content monitoring model by utilizing an optimization algorithm, and comprises parameter initialization, fitness calculation and parameter optimization so as to minimize errors between a predicted value and an actual measured value, wherein the parameters of the total phenol content monitoring model comprise a soil salinity remote sensing estimation model, a multidimensional characteristic parameter extraction model, a protein content estimation model, a total phenol content prediction model parameter and an independent variable.

Description

Remote sensing monitoring method and system for total phenol content of wheat grains in saline-alkali soil Technical Field The invention belongs to the technical field of agricultural remote sensing monitoring, and particularly relates to a method and a system for remotely sensing and monitoring the total phenol content of wheat grains in saline-alkali soil, which are used for realizing large-scale, high-efficiency and accurate monitoring. Background Saline-alkali soil is a general term for saline-alkali soil, alkaline earth and other saline-alkali soil with different degrees, has the characteristics of poor soil structure, easy hardening, low organic matter content, barren nutrient and weak soil fertility preservation capability, and seriously influences crop growth. The saline-alkali soil can be transformed into cultivated land through development, treatment and improvement of the saline-alkali soil, which has important significance for improving grain yield, improving land resource utilization rate and promoting agricultural development. The traditional crop quality monitoring method mainly depends on manual field investigation and laboratory chemical analysis, and has the problems of low efficiency, high cost, strong destructiveness, difficulty in large-area application and the like. In recent years, with the development of remote sensing technology, especially the application of multispectral and hyperspectral remote sensing technology, attempts have been made to monitor and evaluate crop quality by remote sensing means. For example, some studies have proposed using remote sensing data obtained from Landsat 8 OLI sensors in combination with a vegetation index (e.g., NDVI) to predict winter wheat grain protein content. In addition, the method is studied to monitor the protein content of the wheat seeds by adopting a hyperspectral remote sensing technology, and the current information of the protein content of the wheat seeds can be rapidly and accurately obtained. In addition, in recent years, the technology such as the agriculture Internet of things, wireless network transmission, unmanned aerial vehicle remote sensing data monitoring and the like is vigorously developed, and the transformation and development of agriculture to large-scale and intelligent management are greatly promoted. For example, there have been patents that propose crop growth condition monitoring methods based on unmanned aerial vehicle point cloud data processing, and crop growth data is acquired by a laser radar and a multispectral camera. The invention also provides a remote sensing monitoring method for the protein content of the crop based on map synergy, which predicts the protein content of the crop by acquiring texture parameters, structural parameters, growth parameters and pigment parameters of the crop. Although the above technologies have driven the development of agricultural remote sensing monitoring to a certain extent, they still have significant limitations in monitoring the total phenol content of wheat kernels in saline-alkali lands: 1. The existing remote sensing monitoring model is mostly constructed based on common cultivated land, and the stress influence of factors such as specific soil salinity, pH value, ion composition and the like of the saline-alkali soil on the formation of wheat secondary metabolites is not considered. The higher salinity in the saline-alkali soil can cause abnormal physiological metabolism of wheat and influence the synthesis and accumulation of antioxidant substances such as total phenols. 2. The monitoring index is single, most of the prior art only focuses on protein content, and cannot comprehensively capture antioxidant substances such as total phenol content and the like which are key biochemical parameters formed by the quality of wheat in saline-alkali soil, so that the applicability is insufficient. 3. The model has poor universality, the spectral response characteristics of wheat in the saline-alkali soil are not optimized based on the spectral model of the common cultivated land, the inversion accuracy is severely interfered by factors such as the salinization degree of the soil background, the structural variation of the plant canopy and the like, and the requirement of accurate monitoring of the quality of the saline-alkali soil is difficult to meet. The reason for these problems is that the ecological system of saline-alkali soil is complex, the formation of secondary metabolites of crops is affected by soil-environment-organism multiple factor interaction, and the prior art fails to establish a remote sensing monitoring system which is cross-scale, multi-index and clear in cause and effect. The difficulties encountered once include how to realize synchronous inversion of soil salinity parameters and crop biochemical parameters, how to build a spectrum diagnosis model suitable for a saline-alkali stress environment, and how to integrate multi-source remote sensing dat