首页|基于SVM算法的超声波速度-土壤含水率估计模型

基于SVM算法的超声波速度-土壤含水率估计模型

A SVM algorithm-based model for estimating ultrasonic velocity-soil moisture

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为快速准确获取土壤含水率信息,便于农业精准灌溉,引入支持向量机算法(SVM)对4种不同干湿交替处理下超声波速度与土壤含水率进行拟合分析和回归训练优化,构建基于超声波速度的土壤含水率预测模型.结果显示,与传统的烘干法相比较,利用该模型在田间验证土壤含水率,平均相对误差为1.5%左右.研究结果表明,基于SVM模型构建的超声波速度-土壤含水率预测模型能够较好地描述被研究区域内土壤含水率,可为利用超声波特性实现对农田土壤水分的持续监测提供参考.
It is extremely important to obtain accurate information of soil moisture and understand the dynamic change pattern of soil moisture.A support vector machine algorithm(SVM)was introduced for fit-ting analysis and regression training optimization of ultrasonic velocity-soil moisture under four different treatments of alternating wet and dry,and a prediction model of the soil moisture based on ultrasonic veloci-ty was constructed.The prediction model was used to estimate the water content of soil in farmland tillage layer with different moisture requirements.The results showed that the average relative error of verifying soil moisture in the field with the model constructed was about 1.5%compared with the traditional drying method.It is indicated that the prediction model for ultrasonic velocity-soil moisture based on SVM model can effectively describe the soil moisture in the area studied.It will provide reference for utilizing ultrasonic characteristics to achieve continuous monitoring of soil moisture in farmland.

soil moistureultrasonic speedSVMalternating wet and dry

陈盈宜、潘丽敏、叶勇、李君、黄光文

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华南农业大学工程学院,广州 510642

岭南现代农业科学与技术广东省实验室,广州 510642

土壤含水率 超声波速度 SVM算法 干湿交替

岭南现代农业广东省实验室科研项目国家荔枝产业技术体系建设项目

NZ2021040CARS-32

2024

华中农业大学学报
华中农业大学

华中农业大学学报

CSTPCD北大核心
影响因子:1.09
ISSN:1000-2421
年,卷(期):2024.43(2)
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