Deep Learning for Tea Leaf Disease Classification

This chapter presents a comprehensive investigation into the use of deep learning (DL) and machine learning (ML) models and methods for the identification and classification of a variety of illnesses that can affect tea leaves. The research underscores the need of early diagnosis of leaf diseases in both developing countries and exporting nations alike, particularly in India, which is the fourth largest tea exporter and accounts for 10% of total exports. The authors discuss the potential of DL in increasing the precision of disease detection, the various DL architectures and visualization strategies used for this purpose, and the performance indicators employed to evaluate their effectiveness. Additionally, they also cover the latest developments and challenges in DL/ML-based tea disease detection and classification, and point out the study gaps that need further research for more accurate and efficient detection of leaf diseases.