Detection of Maize Disease Using Random Forest Classification Algorithm (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Plant sicknesses are the significant reason for low agrarian profitability. For the most part the farmers experience troubles in controlling and identifying the plant infections. Accordingly, early detection of these diseases will help to increase the productivity of crop. This paper projected early detection of disease in crop using AI methods like Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). In this paper we compare all the method on the basis of accuracy and proposed the best model for the efficient detection of the disease. The Random forest model here achieve the accuracy of 80.68% when compare with other existing model.