Plant Leaf Disease Detection using Novel Machine Learning Approach

Plant leaf disease detection is a critical component of modern agriculture, aimed at early and accurate identification of diseases that can impact crop yields and food security. But the existing detection models attain insufficient and low-quality when utilizing the large amount of data. To overcomes this issue, a novel Machine Learning (ML) algorithm namely, Logistic Regression based Ensemble Classifier (LREC) is proposed for detecting the plant leaf disease. The data are gathered from the leaf image dataset and collected images are pre-processed to remove noise and Histogram equalization is utilized to enhance the quality of the image. Principal Component Analysis (PCA) is performed in feature extraction process and classified through Logistic regression integrated with ensemble classifier. When compared with existing method, the proposed method attains the better performance in detection of plant leaf diseases. The results are outperformed with evaluation metrics such as precision, recall, accuracy and f-measure as respectively.