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Xavier Ngu
Universiti Tun Hussein Onn
Computer Science & IT Electrical & Electronics Engineering

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Soil moisture level prediction using optical technique and artificial neural network Mars Hong Xuan Wai; Audrey Huong; Xavier Ngu
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1752-1760

Abstract

This research describes the use of an optical system combined with artificial neural network (ANN) for wireless and nondestructive prediction of soil moisture level. The former system comprising of near infrared (NIR) emitters of wavelengths 1200 nm and 1450 nm, and a photodetector for near real time soil moisture measurement in loams and peats holding different amount of water. There were 63 and 90 sets of data from loams and peats, respectively, used in the development of the dual stage-multiclass ANN model, wherein measurement of light attenuation (from nondestructive system) was correlated with percent soil moisture (from destructive gold standard approach) in pre-measurement stage. The result revealed a relatively good performance in the training of the NN with regression, R, of 0.8817 and 0.8881, and satisfactory error performance of 0.7898 and 1.172, for loams and peats, respectively. The testing of the system on 50 new samples of loam and peat showed a considerably high mean accuracy of 92 % for loams while 82 % was observed for peats. This study attributes the poorer performance of the system used on peats to the detection resolution of percent soil moisture, and structure and properties of the corresponding soil. This work concluded that the developed technology may be feasible for use in the future design and improvement of agricultural soil management.
Co-Authors Audrey Huong Mars Hong Xuan Wai