Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images

作者：Yahui Guo, Yi Xiao, Fanghua Hao, Xuan Zhang, Jiahao Chen, Kirsten M. de Beurs, Yuhong He, Yongshuo H. Fu · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2023 · DOI：10.1016/j.jag.2023.103528 · 被引用次数：107 · 研究领域：Remote Sensing in Agriculture、Spectroscopy and Chemometric Analyses、Leaf Properties and Growth Measurement

Timely and accurately predicting maize grain yields will contribute to making adaptive measures to improve management practice and to adjust consumption patterns for ensuring food security. Unmanned aerial vehicles (UAV) are widely used to obtain high-temporal and high-spatial resolution remote sensing images of crops, enabling a possible sensor performance comparison. To date, few studies have compared the potential abilities of multispectral-based and hyperspectral-based images, only sensitive spectral wavelength and full hyperspectral spectra, and various machine learning approaches in estimating physiological characteristics such as chlorophyll meter values, leaf area index (LAI), and agricultural grain yields in high vegetation coverage. In this study, the multispectral and hyperspectral images with the ground measurement of crop traits were collected on 13 and 22 September 2021 in Nanpi experimental station, CangZhou, China. The potential ability of multispectral and hyperspectral images for estimating chlorophyll meter values, retrieving LAI, and predicting maize grain yields were explored and compared using the formed two-band (2D) vegetation indices (VIs) and 2D textural indices (TIs). The sensitive spectral wavelengths were confirmed using correlation analyses, then the sensitive spectral wavelength formed VIs and the full hyperspectral spectra were also compared for predicting maize grain yield using five commonly applied machine learning approaches and five deep l...