Hybrid Model of K-Means Clustering and SVM to Detect Ailments in Vegetables

Agriculture is the backbone of Indian Economy, which is true because 70% of India's population are engaged in agriculture and raw material for industrial sectors like food processing. As the population increased, the demand for food also increased. However, disease infection of agricultural goods such as vegetables leads in a decrease in both quality and quantity. Farmers' livelihoods and public health are directly impacted as a result of this. Because of this, early diagnosis of illnesses in vegetable crops reduces the loss of productivity and product quality. Viral and microbial infections as well as environmental stressors are the primary causes of most vegetable-related disorders. Image processing techniques such as K-means clustering and Support Vector Machines (SVM) will be used to identify and diagnose vegetable illnesses, according to the suggested model (SVM). For categorization, it uses supervised leaning. In this paradigm, there are many steps involved in acquiring and processing images, including image segmentation, feature extraction, classification, and illness classification. Alerts are sent via a buzzer if a sickness has been detected. Farmers would have been able to avoid the problem if they had been alerted early. In other words, if breakouts are caught early and dealt with quickly, the farmer stands to lose very little money.