A Comprehensive Review on Deep Learning for Accurate Papaya Disease Identification

Abstract

This comprehensive review delves into the application of deep learning techniques for the precise identification of papaya diseases. With the increasing importance of papaya as a major tropical fruit crop, the accurate and timely diagnosis of diseases is crucial for effective disease management. The paper synthesizes recent advancements in deep learning methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, applied to image-based disease identification in papaya plants. The review assesses the strengths and limitations of various deep learning models, explores the integration of multi-modal data sources, and evaluates the performance metrics employed for disease detection accuracy. Additionally, the study discusses challenges and future directions in leveraging deep learning for papaya disease identification, aiming to provide a comprehensive understanding of the current state and potential advancements in this critical agricultural domain.