Early detection of gray blight in tea leaves and rapid screening of resistance varieties by hyperspectral imaging technology

Abstract

AbstractBACKGROUNDGray blight (GB) is a significant disease of tea leaves, posing a severe threat to both the yield and quality. In this study, the process of leaf infection by a pathogenic isolate of the GB disease (DDZ‐6) was simulated. Hyperspectral images of normal leaves, infected leaves without symptoms, and infected leaves with mild and moderate symptoms were collected. Combining convolution neural network (CNN), long short‐term memory (LSTM), and support vector machine (SVM) algorithms, the early detection model of GB disease, and the rapid screening model of resistant varieties were established. The generality of this method was verified by collecting datasets under field conditions.RESULTSThe visible red‐light band demonstrated a pronounced responsiveness to GB disease, with three sensitive bands identified through rigorous screening processes utilizing uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the successive projections algorithm (SPA). The 693, 727, and 766 nm bands emerged as highly sensitive indicators of GB. Under ideal conditions, the CARS‐LSTM model excelled in early detection of GB, achieving an accuracy of 92.6%. However, under field conditions, the combination of 693 and 727 nm bands integrated with a CNN provided the most effective early detection model, attaining an accuracy of 87.8%. For screening tea varieties resistant to GB, the SPA‐LSTM model excelled, achieving an accuracy of 82.9%.CONCLUSIONThis study provides a core algorithm for a GB disease instrument with detection capabilities, which is of great importance for the early prevention of GB disease in tea plantations. © 2024 Society of Chemical Industry.