Projection of Plant Leaf Disease Using Support Vector Machine Algorithm

Abstract

These worries may be lessened with the use of an automated system. The three most prevalent ailments of turmeric leaves—Leaf blast, Bacterial blight, and Brown spot—are suggested to be automatically diagnosed in this study. Depending on the severity of the sickness, prescriptions for fertilizers and/or insecticides may also be given. K-means clustering is used to separate the damaged area from the image of the turmeric leaf. Color, texture, and form are employed in visual material to differentiate between various illnesses. Using a Support Vector Machine (SVM) classifier, the type of illness affecting turmeric leaves is determined. After identification, it is recommended to implement a prophylactic measure that can help individuals and agricultural groups respond appropriately to these diseases. The accuracy, specificity, and similarity criteria have been used to assess the performance of the experimental decision tree technique findings on turmeric pictures. The suggested approach performs better than the current back propagation method, according to the experimental data.