Deep Learning for Accurate Papaya Disease Identification Using Vision Transformers

Abstract

This study investigates the application of Vision Transformers (ViTs) in deep learning for the accurate identification of papaya diseases. ViTs, known for their effectiveness in image classification tasks, are utilized to develop a robust model capable of precisely diagnosing various diseases that affect papaya plants. Through rigorous experimentation and validation, the study showcases the superior performance of ViTs compared to traditional convolutional neural networks (CNNs) in terms of classification accuracy and computational efficiency. The results highlight the potential of ViTs in real-world agricultural systems, enabling early and accurate disease detection to improve crop yield and ensure food security. This research contributes to the advancement of computer vision techniques in agriculture, emphasizing the importance of leveraging cutting-edge deep learning models like ViTs for enhanced disease management and sustainable agricultural practices.