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A UAV-aided prediction system of soil moisture content relying on thermal infrared remote sensing

文献类型： 外文期刊

作者： Li, W. 1 ; Liu, C. 1 ; Yang, Y. 2 ; Awais, M. 1 ; Li, W. 1 ; Ying, P. 2 ; Ru, W. 1 ; Cheema, M. J. M. 4 ;

作者机构： 1.Jiangsu Univ, Res Ctr Fluid Machinery Engn Sr Technol, Zhenjiang, Jiangsu, Peoples R China

2.Chinese Acad Sci, Inst Engn Thermophys, Beijing, Peoples R China

3.Jiangsu Acad Agr Sci, Nanjing, Peoples R China

4.PMAS Arid Agr, Fac Agr Engn & Technol, Rawalpindi, Pakistan

关键词： UAV application; Soil monitoring; Infrared image processing; Principal component analysis; Radial basis function neural network

期刊名称：INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY （ 影响因子：3.519； 五年影响因子：3.463 ）

ISSN： 1735-1472

年卷期：

页码：

收录情况： SCI

摘要： The aerial platforms have the potential of realizing the precision agriculture, especially the rational allocation of water resources and the intelligent irrigation management of crops. This paper investigates a Unmanned Aerial Vehicle (UAV)-assisted intelligent monitoring method of soil temperature and moisture. The UAV platform is equipped with a high-precision infrared sensor to sample the discrete-time images of the target ground object. By combining the adaptive median filtering, the mean filtering and the Canny edge detection algorithms, we design a composite image preprocessing scheme to obtain the precise location information, and then extract the crop canopy temperature. Specifically, the soil moisture prediction model based on Radial Basis Function Neural Network (RBFNN) and Principal Component Analysis (PCA) are established to achieve accurate prediction of farmland moisture. Experimental results show that (1) the proposed adaptive method is capable of improving the detection accuracy of the target canopy boundary and soil background; (2) by establishing the linear regression model between the real ground temperature and the UAV data, it is shown that the Canny algorithm can improve the extraction accuracy of the canopy temperature data from R<^>2 = 0.7673 to R<^>2 = 0.9355; (3) compared with the PCA method can greatly reduce the dimension of sample data, while the accuracy of the soil moisture content is almost unchanged. This article establishes a set of intelligent prediction methods for soil moisture content, which greatly improves the level of agricultural intelligence.

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