Revolutionizing Tea Leaf Disease Detection With Deep Learning Algorithms

Abstract

Tea cultivation plays a vital role in global agriculture, but diseases affecting tea leaves significantly reduce crop yield and quality. Early detection and diagnosis of tea leaf diseases are crucial for effective management and prevention of economic losses. This study presents a deep learning-based approach utilizing Convolutional Neural Networks (CNNs) to identify and classify tea leaf diseases with high accuracy. By leveraging advanced image processing and feature extraction capabilities of CNNs, the proposed method automates disease detection from images of tea leaves. The system was trained and validated on a robust dataset of diseased and healthy leaf samples, achieving promising results in terms of accuracy, precision, and recall. This approach provides a cost-effective and scalable solution for tea farmers and agronomists, enabling timely intervention and sustainable crop management practices. In this research, we leverage deep learning models, including EfficientNetB4, VGG19, VGG16 and InceptionV3 Model, for the automated detection of tea leaf disease. The datasets, based on tea leaf disease features contribute to the comprehensive analysis. The study aims to streamline the diagnosis process by automating the identification of potential indicators of tea leaf disease, thereby facilitating early intervention. Comparative analysis of the models reveals varying accuracies, with our proposed model demonstrating notable performance in tea leaf disease recognition, achieving an accuracy of 94.94%. These results underscore the potential of deep learning techniques in enhancing the precision and efficiency of tea leaf disease diagnosis. The integration of advanced technology to complement existing diagnostic methods, offering a promising avenue for early tea leaf disease detection.