Leaf Disease Detection Using Deep Learning for Plant Health Monitoring

Abstract

Leaf diseases are a significant threat to crop production, causing substantial yield losses and reducing overall plant health. Early and accurate detection of leaf diseases is crucial for effective disease management strategies. Deep learning, a subset of machine learning, has emerged as a promising approach for leaf disease detection due to its ability to automatically learn complex features and patterns from large datasets. In this review, we provide a comprehensive overview of the existing literature on leaf disease detection using deep learning, including various deep learning architectures, datasets, and evaluation metrics. We discuss the advantages and limitations of deep learning-based approaches, including their accuracy, robustness, scalability, and potential for integration with other technologies for smart farming and precision agriculture. We also highlight the challenges and opportunities in leaf disease detection using deep learning, such as the need for large and diverse datasets, interpretability of deep learning models, and deployment in real-world agricultural settings. This review aims to provide valuable insights into the current state-of-the-art in leaf disease detection using deep learning and guide future research directions in the field of plant health monitoring.