Early detection and control of anthracnose disease in cashew leaves to improve crop yield using image processing and machine learning techniques

Abstract

Agriculture is one of the primary pillars powering India's economy. It is alarming to note that India's agriculture rate is declining steeply. Climate change, environmental pollution, and soil erosion are well-known factors affecting crop productivity. The increasing prevalence of plant diseases is also a significant factor affecting agriculture. Early disease detection and mitigation actions based on identified conditions in the plants are critical in increasing crop productivity. This study considers a machine learning model for detecting disease in cashew leaves. This work concentrates on Anthracnose disease, which leads to severe yield loss when it affects the cashew plant. In this regard, cashew leaves are collected and used to train various machine learning classifiers to identify and classify the disease. This work focuses on the segmentation and classification of leaves using multiple Machine Learning models. Basic segmentation approaches like Global Threshold, Adaptive Gaussian, Adaptive Mean, Otsu, Canny, Sobel, and K-Means, and Machine Learning models like Random Forest, Decision Tree, KNN, Logistic Regression, Gaussian Naive Bayes Classifiers are employed. The final classification employs a Hard and Soft voting classifier and the Decision Tree, KNN, Logistic Regression, and Gaussian Naive Bayes classifiers. Finally, we observe that K-Means segmentation with Random Forest outperforms other classifiers. The accuracy obtained from the Random Forest classifier is 96.7% for the CCDDB dataset, and the accuracy obtained from the Random Forest classifier is 99.7% for the PDDB dataset.