• Gayatri Rallabandi
• Dr. Pankaj Kumar Mishra
• Prof. Namrata Dewangan

Each year, plant’s viral and fungal infections cause up to 30 percent of crop damage and cause spread of deadly diseases such as cancer, autism and asthma due to overuse of pesticides. Therefore, it is important to detect the disease at an early stage in the plant. Current methods involve expert eye examination. It also involves constant monitoring, which is a very expensive and difficult exercise for large farms. Farmers in India are less educated to recognise the disease and its dire consequences. They neglect or use pesticides carelessly to get out of the problem. It not only spreads deadly diseases but also contaminates the soil, also it is not economically viable for them. Therefore, it is important to recognize the diseases and their proper treatment for good crop production and environmental balance.The detection of plant leaf diseases by automated method is useful because it reduces the large effort of observation in large fields and it detects the symptoms of diseases at an early stage. Detection of plant leaf disease involves steps such as image acquisition, image pre-processing, image segmentation, feature extraction and classification. This paper implements a technology for image pre-processing. The image segmentation algorithm is used for automatic detection and research on various plant leaf disease and classification algorithms is used for leaf disease classification.

KNN, Plant diseases detection.

"AN IMPLEMENTATION OF MATLAB BASED SYSTEM FOR DISEASE AND SEVERITY DETECTION OF CROP AND PREDICTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.b707-b714, September-2021, Available :http://www.jetir.org/papers/JETIR2109185.pdf

"AN IMPLEMENTATION OF MATLAB BASED SYSTEM FOR DISEASE AND SEVERITY DETECTION OF CROP AND PREDICTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppb707-b714, September-2021, Available at : http://www.jetir.org/papers/JETIR2109185.pdf