Enhancement of tea leaf diseases identification using modified SOTA models

The identification of tea leaf diseases holds considerable significance for preserving the health of tea plants and preventing losses in tea production. This study introduced a hybrid framework by combining modified state-of-the-art (SOTA) models with feature selection and Machine Learning (ML) classifiers for recognizing four types of tea leaf diseases. The investigation utilized SOTA models, namely VGG16, Xception, and ResNet152V2. These architectures underwent modification by adding extra layers, serving as feature extractors from tea leaves. The extracted features then underwent a feature selection process to identify the most relevant ones, which were subsequently employed in ML classifiers for predicting tea leaf diseases. The proposed method demonstrated outstanding performance with a 2-fold average accuracy of 99.5%, an Area Under the Curve (AUC) of 1.0, and a p value of 0.001.