• Jyoti Raghvendra Pal
• Ankit Singhania
• Ishita Mukherjee
• Prof. S.H. Darekar

Agriculture is critical to India's economic growth. Farmers struggle to choose the right fruit and vegetable crop. Disease management by hand is a difficult job. The majority of diseases affect the plant's leaves or stems. As a result, agriculturists must develop effective techniques. To predict the disease, we combined two techniques in this paper. The proposed work's aim is to diagnose disease using image processing and clustering techniques on a photo of diseased plant leaves. Preprocessing begins with the input image. The RGB to L*a*b conversion of the input image of leaves is then performed. Following that, hierarchical clustering is used to segment leaf disease. The predominantly green color pixels are masked after segmentation depending on specific threshold values. Finally, the proposed method's precision was compared to that of other approaches. The suggested procedure was shown to be 92% accurate.

Image Processing, Data mining, RGB, Clustering, Segmentation

"Plant Leaf Disease Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.b947-b951, May-2021, Available :http://www.jetir.org/papers/JETIR2105246.pdf

"Plant Leaf Disease Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppb947-b951, May-2021, Available at : http://www.jetir.org/papers/JETIR2105246.pdf