利用光谱数据预测土壤含水量的遗传算法优化反向传播神经网络模型

IF 2.8 3区 农林科学 Q3 ENVIRONMENTAL SCIENCES
Jiawei Wang, Yongyi Wu, Yulu Zhang, Honghao Wang, Hong Yan, Hua Jin
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引用次数: 0

摘要

目的准确评估土壤含水量(SMC)对气候科学、水文学、生态学和农业应用至关重要。然而,传统的 SMC 特征描述和测量昂贵、耗时,而且对土壤有负面影响。最近,多光谱技术的应用为 SMC 的精确检测提供了新思路。本研究的目的是开发并比较回归算法和机器学习算法,以便从多光谱图像中估算 SMC。材料与方法使用多光谱传感器采集了山西省 125 个土壤样品的光谱图像,这些土壤样品来自五种不同的土壤质地,土壤湿度从干旱到完全饱和不等。从图像中得出了一组七个光谱参数,并根据实验室测量的 SMC 建立了预测关系。本研究比较了线性回归(LR)模型和基于遗传算法优化的反向传播神经网络模型(GA-BP)来预测 SMC。结果与讨论结果表明:(1)光谱反射率和 SMC 呈明显的负相关,SMC 越低,光谱反射率越大。(2)GA-BP 神经网络模型具有更高的预测精度和性能(R2 = 0.978 ~ 0.990,RMSE = 0.366 ~ 0.799%,MAE = 0.360 ~ 0.890%)。(3) GA-BP 模型对细砂土壤具有极佳的反演精度(R2 = 0.990,RMSE = 0.518%,MAE = 0.360%)。它进一步强调了在 SMC 预测中采用反向传播神经网络和遗传算法的显著效果,为精准农业提供了一种快速、精确、非侵入性的实用方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A genetic algorithm-optimized backpropagation neural network model for predicting soil moisture content using spectral data

A genetic algorithm-optimized backpropagation neural network model for predicting soil moisture content using spectral data

Purpose

Accurate assessment of soil moisture content (SMC) is crucial for applications in climate science, hydrology, ecology, and agriculture. However, conventional SMC characterization and measurement are expensive, time-consuming, and have negative effects on soil. Recently, the application of multispectral technology provides a new idea for SMC accurate detection. The objective of this study was to develop and compare regression and machine learning algorithms to estimate SMC from multispectral images.

Materials and methods

A multispectral sensor was used to collect spectral images of 125 soil samples from five distinct soil textures in Shanxi province at varying degrees of soil moisture, ranging from arid to fully saturated. A set of seven spectral parameters was derived from images, and predictive relationships were developed against laboratory-measured SMC. A linear regression (LR) model and a backpropagation neural network model based on genetic algorithm optimization (GA-BP) were compared in this study to predict SMC.

Results and discussion

The results showed that (1) the spectral reflectance and SMC exhibit a clear negative correlation, and the lower the SMC, the larger the spectral reflectance is. (2) The GA-BP neural network model exhibits higher prediction accuracy and performance (R2 = 0.978 ~ 0.990, RMSE = 0.366 ~ 0.799%, MAE = 0.360 ~ 0.890%). (3) The GA-BP model exhibits the excellent inversion precision for the fine sand soil (R2 = 0.990, RMSE = 0.518%, MAE = 0.360%).

Conclusions

This study introduces an effective methodology for accurate estimation of SMC using multispectral remote sensing technology. It further underscores the significant effectiveness of employing backpropagation neural networks and genetic algorithms in SMC prediction, providing a rapid, precise, non-intrusive, and practical approach towards precision agriculture.

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来源期刊
Journal of Soils and Sediments
Journal of Soils and Sediments 环境科学-土壤科学
CiteScore
7.00
自引率
5.60%
发文量
256
审稿时长
3.5 months
期刊介绍: The Journal of Soils and Sediments (JSS) is devoted to soils and sediments; it deals with contaminated, intact and disturbed soils and sediments. JSS explores both the common aspects and the differences between these two environmental compartments. Inter-linkages at the catchment scale and with the Earth’s system (inter-compartment) are an important topic in JSS. The range of research coverage includes the effects of disturbances and contamination; research, strategies and technologies for prediction, prevention, and protection; identification and characterization; treatment, remediation and reuse; risk assessment and management; creation and implementation of quality standards; international regulation and legislation.
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