Paddy disease classification using machine learning technique

Abstract

Early disease detection plays a vital role in protection of paddy crops. In earlier days the detection of disease was done through seeing or by examining in a laboratory. The observation made visually needs experts and it might vary for each individual which leads to error and laboratory testing requires more time and might not be able to deliver the outcome within a time. To get the better of this issue, image processing-based Machine learning approach used to detect the diseases and classify the diseases. We mainly focused on rice (Oryza sativa) diseases. The images contain the leaves and stems which are affected by disease collected from the paddy fields. The dataset contains five different classes of diseases (1) Rice Blast (2) Bacterial Leaf Blight (3) Sheath Blight (4) Healthy leaves. The early detection of diseases will help farmers to increase their yield.