Leaf Disease Detection and Prevention Using Machine Learning (International Journal of Communication Systems and Network Technologies, 2021)
Plant illnesses are often brought on by pests, insects, and pathogens, and if they are not promptly handled, they significantly reduce yield. Farmers are losing money as a result of different crop diseases. When the cultivated area is enormous, measured in acres, the cultivators find it tiresome to routinely check on the crops. The suggested approach offers a way to automatically diagnose diseases using photos from remote sensing while also offering a solution for routinely monitoring the agricultural area. The suggested approach alerts the farmer about crop illnesses so they may take additional action. The suggested technology aims to identify infections early, as soon as they begin to spread to the leaf’s outer layer. The two phases of the proposed system’s operation start with training data sets. This involves using training sets with both healthy and sick data. The second stage involves crop monitoring and disease identification using Canny’s edge detection technology.
Keywords: Leaf Disease Detection; Machine Learning; Image Processing; Canny Edge Detection; K-Means Clustering; Remote Sensing