Leaf water content determination of oilseed rape using near-infrared hyperspectral imaging with deep learning regression methods

Abstract

Water content is crucial for plant growth. Determination of water content can help monitor plant growth status. In this study, spectral data in the range of 900–1700 nm acquired by near-infrared hyperspectral imaging and corrected by black-white calibration were used to detect the water content of fresh oilseed rape leaves. The oilseed leaves were analyzed without particular treatments. Conventional machine learning (support vector regression, partial least squares regression and least absolute shrinkage and selection operator) and deep learning regression models (Convolutional Neural Network and Long Short-Term Memory) were developed to predict oilseed rape leaf water content. The performance of CNN-LSTM-R was highly accurate. The coefficient of determination and root mean square error of the testing set (RMSEP) were 0.814 and 0.005, respectively. The characteristic wavelengths with strong correlation with water content prediction of the regression models were analyzed. The results showed that the deep learning-based regression models showed great potential for water content determination of oilseed rape leaves. Therefore, this study provides an important theoretical basis and practical application for the detection of fresh plant water content.