• Jayamala K. Patil
• Vinay S. Mandlik
• Sampada A. Dhole

Plant disease management involves the identification, classification, and grading of plant disease according to disease severity. It is not only a challenging task that involves human resources but also includes expertization and timely decisions to reduce the threat of reduced production and high spared of disease. The efficiency of recently developed systems depends on the selection of image features and accuracy in the segmentation of diseased portions. The research in this paper presents Content-Based Image Retrieval (CBIR) system implemented for the identification and classification of leaf diseases. The system is further upgraded with the extraction of diseased portion of the leaf using K-means clustering technique for grading the disease severity. The retrieval system is realized using of color, shape, and texture features of the leaf. It is observed that the proposed system performs the tasks of disease identification, classification, and severity grading accurately and consistently

CBIR, Disease, Color, Shape, Texture

"Identification, classification, and grading of plant leaf diseases using CBIR and K-means clustering ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.h531-h537, May 2022, Available :http://www.jetir.org/papers/JETIR2205871.pdf

"Identification, classification, and grading of plant leaf diseases using CBIR and K-means clustering ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. pph531-h537, May 2022, Available at : http://www.jetir.org/papers/JETIR2205871.pdf