Plant disease detection using machine learning algorithms

Computer Engineering

India, with its reliance on agriculture and a significant portion of its population engaged in farming, heavily depends on the agricultural sector. Agriculture contributes to one-third of the nation's income. However, farmers face losses due to crop diseases, especially considering the vast cultivated areas they have to monitor. Detecting plant diseases early and accurately is crucial to minimize crop losses, maintain crop quality, and maximize yield. Timely diagnosis and intervention can reduce the impact of plant diseases and avoid unnecessary use of pesticides. In the past, automatic detection of plant diseases relied on image processing techniques. Our approach involves designing machine learning mechanisms and image processing tools for disease detection and classification. The process encompasses stages such as image acquisition, image pre-processing, image feature extraction, and feature classification. To extract image features, we utilize a global image feature extraction technique.

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