Hyperspectral imaging with a band matrix reduction method to detect early drought stress in tomato

Abstract

Drought stress is one of the key abiotic stresses affecting plant growth, crop yield and food quality. The main objective of this study is to investigate the potential effectiveness of hyperspectral imaging with band selection method for the rapid detection of the early drought stress of tomatoes. First, the unsupervised algorithm - K-means and statistical histogram are used to extract samples representing each experimental treatment group. Then, to solve problems related to the high redundancy and correlation of hyperspectral data, band matrix reduction method (BMRM) based on recursive feature elimination theory is proposed to determine the optimal band subset. The band matrix is constructed according to the band ranking obtained by the discrimination coefficient -$Coef\left(i\right)$, which is calculated from the average spectral curve and the first-derivative spectrum. Finally, the effectiveness of waveband selection algorithms was validated by comparison with successive projections algorithm, competitive adaptive reweighted sampling, recursive feature elimination with cross-validation and full spectrum. The results demonstrated that BMRM achieved higher classification accuracy with fewer bands selected, and the amount of calculation is not greatly improved. The proposed method provides a more accurate, and effective way of detecting early drought stress.

Keywords:
hyperspectral imaging; drought stress; band selection; band matrix reduction method; classification