Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing

Corn is one of major crops in Indonesia. Diseases outbreak could significantly reduce the maize production, causing millions of rupiah in damages. To reduce the risks of crop failure due to diseases outbreak, machine learning methods can be implemented. Naked eyes inspection for plant diseases usually based on the changes in color or the existence of spots or rotten area in the leaves. Based on these observations, In this paper, we investigate several image processing based features for diseases detection of corn. Various image processing features to detect color such as RGB, local features on images such as scale-invariant feature transform (SIFT), speeded up robust features (SURF), and Oriented FAST and rotated BRIEF (ORB), and object detector such as histogram of oriented gradients (HOG). We evaluate the performance of these features on several machine learning algorithms. They are support vector machines (SVM), Decision Tree (DT), Random forest (RF), and Naive Bayes (NB). Our experimental evaluations indicate that the color may be the most informative features for this task. We find that RGB is the feature with the best accuracy for most classifiers we evaluate.