Image Processing and Machine Learning Approaches for Leaf Disease Identification: A Survey
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Abstract
Identification of leaf diseases is essential for maintaining plant health and raising agricultural output. Plant leaf diseases can greatly lower crop losses and improve yield quality when detected early and accurately. Combining machine learning algorithms with image processing techniques has become a potent tool for automated disease diagnosis and tracking in recent years. This survey provides a thorough summary of the current approaches that use machine learning and image processing to detect leaf diseases. The accuracy, computational efficiency, and scalability of several supervised and unsupervised learning methods, and deep learning models are examined and contrasted. In addition to discussing potential future paths for improving resilience and real-time application, the research addresses important issues such complicated backgrounds and fluctuating illumination conditions. Researchers and practitioners attempting to create intelligent plant disease diagnosis systems might use this survey as a reference.
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