Rapid Detection of Soil Moisture Content Based on UAV Multispectral Image
LI Xin-xing1, ZHU Chen-guang1, FU Ze-tian1,3, YAN Hai-jun2, PENG Yao-qi3, ZHENG Yong-jun3*
1. Beijing Laboratory of Food Quality and Safety, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. College of Water Conservancy and Civil Engineering, China Agricultural University, Beijing 100083, China
3. College of Engineering, China Agricultural University, Beijing 100083, China
Abstract：In this paper, we studied the correlation between multispectral soil reflectance and soil moisture content. In this paper, the Tongzhou experimental station of China Agricultural University was selected as the research area. In the experimental wheat field of this experimental station,wecollected100 groups of soil samples on the spot. According to a certain gradient, we prepared the soil moisture content between 10% and 50%. The real value of soil content was determined by drying method. Multispectral cameras are convenient. Red Edged-M multispectral camera was mounted on innovative Phantom 3 UAV. The multispectral images of soil samples were collected in a sunny environment. The multispectral images of soil samples were processed. The spectral reflectance of soil samples was extracted, and the model between multispectral soilinformation and water content was established. The multispectral information of soil samples was extracted by ENVI5.3 software.We, asthe reflectance of the standard whiteboard is 100%. The spectral reflectance of soil samples in five bands of blue, green, red and near-infrared was calculated. In order to explore the correlation between spectral reflectance and moisture content of soil samples, we established BP neural network algorithm, support vector machine algorithm, andpartial least squares algorithm prediction models of soil moisture content based on UAV multispectral images. Based on 80 sets of soil sample data as a training set, a prediction model of soil moisture content based on the multi-spectral image was established. The BP neural network algorithm is improved bythe Levenberg-Marquardt method, which improves the training speed of its BP neural network model. When the network structure is 5-10-1, the number of iterations is the least, and MSE is the least. This paper chooses the network structure. The support vector machine algorithm adopts the Gauss kernel function, and when the parameter is 0.56, the model has the best effect. In this study, normalized root means square error (NRMSE) and decision coefficient were used to compare the three prediction models of soil moisture content quantitatively. We used 20 sets of soil sample data as the test set, the results showed that the normalized root mean square error of soil moisture content prediction model based on BP neural network was 0.268, and the decision coefficient was 0.872; the normalized root mean square error of soil moisture content prediction model based on support vector machine was 0.298, and the decision coefficient was 0.821; The normalized root mean square error (NRMSE) of the prediction model multiplied by soil moisture content is 0.316, and the decision coefficient is 0.789. According to the analysis of the three models, the prediction model of soil moisture content based on BP neural network has a good effect. Through the research, we can know that itis a close correlation between soil spectral reflectance and water content. The multi-spectral camera can effectively monitor soil moisture content in real-time on the UAV. In this study, multi-spectral technology is applied in the field of soil moisture content detection to provide technical support and theoretical support for monitoring soil moisture.
Key words：Multispectral; Unmanned aerial vehicle; Soil moisture; Prediction model
[1] CHENG Gong, LI Jia-xuan, DAI Zhi-xiu(成 功, 李嘉璇, 戴之秀). Journal of Geology(地质学刊), 2017, 41(3): 394.
[2] XIA Nan, Tashpolat-Tiyip, DING Jian-li, et al(夏 楠, 塔西甫拉提-特依拜, 丁建丽, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(6): 263.
[3] ZHANG Zhi-tao, WANG Hai-feng, HAN Wen-ting, et al(张智韬, 王海峰, 韩文霆, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2018, 49(2): 173.
[4] Li Zhu, Wu Longhua, Zhang Hao, et al. Journal of Soils and Sediments, 2015, 15(7): 1510.
[5] Li X L, Yi S L, He S L, et al. Precision Agriculture, 2016, 17(3): 365.
[6] Liu Zhongbao. Journal of Astrophysics and Astronomy, 2016, 37(2): 12.
[7] Vanhoy G, Schucker T, Bose T. Analog Integrated Circuits and Signal Processing, 2017, 91(2): 305.
[8] Weakley A T, Miller A L, Griffiths P R, et al. Analytical and Bioanalytical Chemistry, 2014, 406(19): 4715.
[9] Ryan K, Ali K. Ocean Science Journal, 2016, 51(2): 209.
[10] ZHANG Zhi-tao, TAN Cheng-xuan, XU Chong-hao, et al(张智韬, 谭丞轩，许崇豪, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2019, 50(7): 246.
[11] Li N, Xu Z, Zhao H, et al. Cluster Computing, 2017, 20(2): 1271.
[12] Huang Y, Wu A, Zhang G. Wireless Personal Communications, 2017, 95(3): 2223.
[13] Ryan K, Ali K. Ocean Science Journal, 2016, 51(2): 209.
[14] Sotnezova K M, Samokhin A S, Revelsky I A . Journal of Analytical Chemistry, 2017, 72(14): 1419.
