Detection of Disease on Tomato Leaf Using SVM and K-Means Clustering Method

Agriculture ensures that everyone has access to food even in situations where the population is growing rapidly. Avoiding food shortages is dependent on taking preventative measures against plant diseases. Unfortunately, anticipating diseases in immature crops is a common occurrence. The report aims to educate farmers on new methods of protecting plants from leaf diseases using cutting-edge technologies.Since tomatoes are so widely consumed, it was necessary to find a way to accurately detect leaf diseases in tomato plants using solely machine learning and image processing techniques. In this study, we collect samples of sick tomato leaves to analyse. There is a flaw in these tomato leaves that helps growers detect diseases early on. To sort information, K-means clustering is used. Use of many descriptors allows for maximum information extraction from the leaf samples. The collected features are then sorted into groups using machine learning methods like Support Vector Machine (SVM). Experimental analyses are performed using the benchmark dataset.