A Machine Learning Approach for Classification of Lemon Leaf Diseases

Automated classification of plant leaf diseases is one of the complex concerns in robotics and machine learning fields. Several models have been introduced for detecting and classifying leaf diseases with high classification performance. With the advancement of image processing and machine learning, predicting diseases is the most significant research in recent years. This paper aims to classify lemon leaf diseases by segmenting the diseased part. The lemon leaf dataset is split into training and testing folders. The presented classification method consists of segmentation, feature extraction, and classification steps. For the classification of lemon leaf disease, at first, segmentation is done by using the K-mean clustering algorithm. The infected part of the lemon leaf can be partitioned by the above algorithm. The feature extraction is performed using Gray Level Co-occurrence Matrices (GLCM) method, to extract the texture features from the image. The GLCM method generates the statistical features from the image. The features are passed to the Support Vector Machine (SVM) for the classification of lemon leaf disease. Experiments are conducted for the lemon leaf dataset by taking some samples and the results demonstrate high classification accuracy at a faster speed than other traditional classification methods.