Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases

Exploring the intersection of deep learning and agricultural disease detection, this paper presents a novel methodology for identifying prevalent diseases in tea leaves using advanced image analysis techniques. Using a 4,000-picture collection from the Kannan Devan Hills in Kerala, this study captures different tea leaf states, including eight prevalent illnesses and healthy states. Zoom and rotation approaches were used to augment the photos to improve the dataset for a more strong training environment.Carefully designed with layers suited for exact picture classification, the proposed deep learning model has architectural integrity. Beginning with an input layer processing $256 \times 256$ pixel pictures, the model consists of a complex arrangement of Conv2D layers, batch normalization, activation functions, and residual blocks intended to capture thorough patterns and features in tea leaves. Using many optimizers-SGD, RMSprop, Adam, and Nadam-all set with a learning rate of 0.001-highlights the Adam optimizer’s exceptional performance, obtaining a surprising $99 \%$ accuracy according to a thorough categorization report. This work not only emphasizes how well deep learning might detect agricultural diseases but also prepares the ground for more developments in precision farming.