Plant Leaf Disease Detection and Classification Using Convolutional Neural Networks

Le résumé fourni par la source

Global agricultural output is being threatened by plant diseases, which result in large losses. Conventional approaches to disease identification and categorization are labor-intensive, inefficient, and often constrained in scalability. The emergence of machine learning (ML) methodologies, especially in image processing and data analysis, presents a potential approach for automating and improving plant disease diagnosis. This study focuses on image-based data from plant leaves, fruits, and stems and offers a thorough method for detecting and classifying plant diseases using machine learning algorithms. The adoption of this technology utilizes advanced neural networks, namely convolutional neural networks (CNNs), in combination with data preprocessing, feature extraction, and model assessment methods to accurately identify plant diseases across diverse agricultural environments. The use of pre-trained models through transfer learning further enhances the accuracy and efficiency of the system, addressing challenges such as limited labeled datasets. Despite the promising results, challenges remain in data collection, model generalization, and real-world deployment. The study concludes by emphasizing the potential of machine learning to revolutionize plant disease management by lowering crop losses, encouraging sustainable farming methods, and detecting issues earlier.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.