Camellia Sinensis (Tea) Plant Disease Classification Using RESNET

Tea, a globally consumed beverage, holds a significant place in the agricultural sector. However, the cultivation of tea is threatened by various diseases that can adversely affect the quality and yield of the tea crop. Timely detection and management of these diseases are essential for sustainable tea production. In this research, we present a novel methodology to address this challenge by leveraging deep learning techniques, specifically the Residual Neural Network (ResNet), for the automated identification of tea plant diseases. The primary objective of this research is to develop a robust and accurate system that can assist farmers and researchers in identifying diseases in tea plants. Traditional methods of disease identification often require expert knowledge and can be time-consuming. In contrast, our proposed approach harnesses the power of computer vision and machine learning to streamline the identification process, enabling faster and more accurate diagnoses. To accomplish this, we have curated a diverse and comprehensive dataset of tea plant images, capturing both healthy plants and various diseases, including but not limited to blister blight, gray blight, and root rot. The quality an,,d diversity of the dataset play a pivotal role in training a reliable disease identification model. We employ a ResNet architecture, a state-of-the-art convolutional neural network known for its effectiveness in image classification tasks.