Multispectral Image Determination of Water Content in Aquilaria sinensis Based on Machine Learning

Abstract

The real-time nondestructive monitoring of plant water content can enable operators to understand the water demands of crops in a timely manner and provide a reliable basis for precise irrigation. In this study, a method for rapid estimation of water content in Aquilaria sinensis using multispectral imaging was proposed. First, image registration and segmentation were performed using the Fourier–Mellin transform (FFT) and the fuzzy local information c-means clustering algorithm (FLICM). Second, the spectral features (SFs), texture features (TFs), and comprehensive features (CFs) of the image were extracted. Third, using the eigenvectors of the SFs, TFs, and CFs as input, a random forest regression model for estimating the water content of A. sinensis was constructed, respectively. Finally, the monarch butterfly optimization (MBO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA) were used to optimize all models to determine the best estimation model. The results showed that: (1) 60%–80% soil water content is the most suitable for A. sinensis growth. Compared with waterlogging, drought inhibited A. sinensis growth more significantly. (2) FMT + FLICM could achieve rapid segmentation of discrete A. sinensis multispectral images on the basis of guaranteed accuracy. (3) The prediction effect of TFs was basically the same as that of SFs, and the prediction effect of CFs was higher than that of SFs and TFs, but this difference would decrease with the optimization of the RFR model. (4) Among all models, SSA-RFR_CFs had the highest accuracy, with an R2 of 0.8282. These results confirmed the feasibility and accuracy of applying multispectral imaging technology to estimate the water content of A. sinensis and provide a reference for the protection and cultivation of endangered precious tree species.