Abstract:
Disease detection is a very important part to protect loss of crop in agriculture.
Symptoms of the plant diseases can be detected by using machine learning
techniques. Machine learning technique can solve for classification and regression
problems. This proposed system presented that mungbean leaf disease detection by
using digital image processing and machine learning techniques. Image preprocessing
state used image enhancement technique to improve the quality of images. This
enhanced image is segmented by using k-means clustering techniques. This technique
is used to segment region of interest in leaf area. Gray Level Co-occurrence Matrix
(GLCM) is used to extract features from preprocessing and cluster images. And also,
mungbean leaf diseases are classified using the k-nearest neighbor algorithm (k-NN).
According to the results of the experiments, the system can successfully detect and
classify healthy and unhealthy or infected leaf areas. In this system, the k-NN
algorithm can classify disease types with 96.7% accuracy and the support vector
machine (SVM) algorithm with 86.7%.
