Efficient plant leaf disease detection using support vector machine algorithm and compare its features with Naive Bayes classification

Abstract

Aim: This research work carried out to determine the accuracy in leaf disease detection using Naive Bayes (NB) classification algorithm and comparing its accuracy features with Support Vector Machine (SVM) algorithm for improving the accuracy of the prediction. Methods: In this proposed paper, the plant leaf disease detection has been carried out as an experiment using machine learning techniques such as SVM (N=10) and NB classification (N=10). The accuracy is evaluated for leaf disease detection. Result: The NB classification and the SVM algorithms were implemented and compared accuracy results. The SVM appears to be more significant with 95% accuracy than NB with 91% with significant value (0.081). Conclusion: The result shows that SVM algorithm's accuracy appeared to be better than other machine learning algorithms for leaf disease detection.