Leaf Disease Detection

Computer Science and Engineering

Detecting leaf diseases in plants is imperative for maintaining agricultural productivity and ensuring food security globally. Traditional methods face challenges in early detection, leading to significant yield losses. However, convolutional neural network (CNN) algorithms have emerged as potent tools for image-based disease detection, revolutionizing agricultural practices. This paper presents a comprehensive review of existing research on leaf disease detection techniques employing CNN algorithms. The review begins by discussing the limitations of traditional methods and underscores the advantages of CNNs in early disease detection. It explores various CNN architectures and methodologies utilized in leaf disease detection systems, encompassing data preprocessing, feature extraction, and classification stages. CNNs offer robustness in learning intricate patterns from leaf images, facilitating accurate disease diagnosis. Furthermore, the paper delves into performance metrics commonly employed to assess the effectiveness of CNN-based techniques. Evaluation metrics play a crucial role in benchmarking the performance of CNN models and guiding further research endeavors. Moreover, the review identifies key challenges in CNN-based leaf disease detection, including dataset scarcity, class imbalance, and model interpretability. Addressing these challenges necessitates innovative approaches and collaborations between researchers and agricultural stakeholders. Finally, the paper outlines potential avenues for future research and improvement in CNN-based leaf disease detection. These include the development of transfer learning techniques, domain adaptation strategies, and the integration of multi-modal data sources for enhanced disease diagnosis. In summary, this review offers valuable insights into the current state-of-the-art in leaf disease detection using CNN algorithms. By synthesizing existing research, it provides researchers and practitioners with a roadmap for advancing agricultural research and fostering sustainable crop management practices.

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