DEVELOPMENT OF NEW MACHINE LEARNING ALGORITHM FOR CUCUMBER AND GRAPE LEAF DISEASE DETECTION USING MULTI SVM WITH CUSTOM KERNELS

In Agricultural production crop disease is a wide spread problem, it effects the productivity and quality of crops. Cucumbers are high water content vegetables and many minerals and vitamins, But Cucumbers are susceptible to several diseases. In wine production grapes are main ingredient, but these are severely attack by brown spot, mites, Anthracnose, downy mildew, black rot, and leaf blight. Identification and detection of crop diseases are at most care for reducing economic loss for the formers also improve productivity. But manual detection takes huge amount of time, sometimes it gives inaccurate results. To overcome these challenges by using advanced artificial intelligence techniques including Alex net, VGG-16,U-Net,ResNet,VGG-19,Fine KNN, Random forest Algorithms, YOLO v5.But these algorithms struggle with irrelevant features, noise and poor performance. To handle these we proposed novel approach using Multi SVM with Custom SVM kernels along with the GLCM features. The primary objective of the new algorithm is to enhance the model’s performance and increase the efficiency of disease detection. Our datasets cucumber and grapes collected from Kaggle and directly from cucumber farms, it contains four different categories: Healthy, Powdery mildew, Downy mildew and Target Leaf Spot. First phase is to apply image augmentation technique for enhancing the image. Second affected area is calculated using K-Means Algorithm. Next from the images GLCM features are extracted. Subsequently classification is performed using Multi SVM algorithm with Custom SVM kernels. Our Novel Approach achieved best identification result with mAP is 90.62% compare with YOLO v5M mAP is 84.6.