Detection and categorization of diseases affecting plant leaves with the use of machine learning

The use of sensors and various technologies based on machine learning is called “smart agriculture” and has now become an agricultural trend. According to a recent study, foliar disease caused major loss to 56% of the farm. It is important to increase agricultural productivity and monitor the spread of diseases. Early detection and prevention of the disease is important to prevent its spread. Therefore, we can use some research to solve this problem. In this study, we compare two different models: Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). The two models discussed and analyzed in this study can describe eight different diseases. Using the soyabean leaf disease dataset as its training set, the SVM model achieved a 80% accuracy rate, surpassing that of the KNN model, which achieved 64% accuracy rates, respectively.