• Prof. Sahana Shetty
• Prof Shilpa KS
• Dr Narayana Swamy Ramaiah

Plant disease detection in cultivation is quite challenging. When a diagnosis is wrong, the seed yield and the commercial profit of the company fail tremendously. Detection of leaf diseases calls for a great deal of research, the experience of plant disorders and much more time to process. This paper may also use picture analysis in MATLAB to classify leaf disease. Disease detection continues steps such as loading the file, enhancing contrast, converting RGB to HSI, extracting characteristics and SVM. Seed farming in the agriculture sector has a vital role to play. The lack of food is primarily attributed to contaminated seeds, which decrease development levels reflexively. Plant diseases are not yet investigated at an early stage to classify them. The biggest problem is the decline in pesticide uses in farming and the increase in output levels of quality and abundance. The segmented area is identified and positioned in its correct classes as a color-based segmentation model. A trial study of the time sensitivity and field of the contaminated zone was conducted on sample images. The picture processing methodology can identify plant disorders.

Images pre-processing, Disease analysis, k mean cluster, Features extractions, Segmentations, SVM.

"Leaf Diseases Prediction by Image Processing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.114-118, January-2019, Available :http://www.jetir.org/papers/JETIREU06022.pdf

"Leaf Diseases Prediction by Image Processing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp114-118, January-2019, Available at : http://www.jetir.org/papers/JETIREU06022.pdf