Although plant diseases pose a major threat to food security, the lack of necessary infrastructure makes it still difficult to quickly identify plant diseases in many parts of the world. The combination of increasing global technology penetration and recent advances in machine vision made possible by machine learning has paved the way for diagnosing illnesses using python. Machine learning technique to identify 14 crop species and 26 diseases (or their absence) using a public dataset of 54,306 diseased and healthy plant leaf images collected under controlled conditions Train the network. The trained model achieved 99.35% accuracy in a sustained test set, demonstrating the feasibility of this approach. Overall, the approach of training machine learning models with increasingly large and publicly accessible image datasets represents a clear path to the diagnosis of global plant diseases.

Digital image processing, Agri-farm plant disease, Machine learning, Plant disease detection.

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