• Jitendra Kumar Katariya
• Kunal Kumar
• Sneha Jain
• Rajnish Kumar
• Aman Kumar Singh , Aman Kumar Singh

This research delves into enhancing the modified structure for disease recognition and characterization in horticulture. Over the past few decades, various societies have investigated different aspects of plant pathology. This study particularly focuses on analyzing leaf-related images. Utilizing the K-Means algorithm, the concentration of infections on leaves is determined. The system not only identifies infected and healthy leaves but also distinguishes between them. Disease determination employs a Multi-Class SVM-based classifier, utilizing an information database derived from extensive leaf analysis. The system successfully identifies diseases such as Alternaria Alternata, Anthracnose, Bacterial Blight, and Cercospora Leaf Spot, achieving an impressive accuracy level of 96 to 97 percent, surpassing previous approaches in performance.

Disease recognition, Characterization, Horticulture, K-Means, Multi-Class SVM, Leaf analysis, Alternaria Alternata, Anthracnose, Bacterial Blight, Cercospora Leaf Spot, Accuracy

"Advancements in Disease Recognition and Characterization in Horticulture: A Study Utilizing K-Means and Multi-Class SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.b143-b147, June-2024, Available :http://www.jetir.org/papers/JETIR2406120.pdf

"Advancements in Disease Recognition and Characterization in Horticulture: A Study Utilizing K-Means and Multi-Class SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppb143-b147, June-2024, Available at : http://www.jetir.org/papers/JETIR2406120.pdf