Multifeature Analysis to Detect Cotton Leaf Curl Virus

Abstract

Plant leaf diseases have devastating impacts on yield production, both in terms of quantity and quality. The cotton leaf curl virus (CLCuV) is one of the most destructive diseases that affect cotton crops worldwide. Disease detection based on symptoms is laborious and demands a great deal of experience and knowledge. The purpose of this research study is to design an automated system to detect CLCuV accurately. A dataset of healthy, mildly, and severely infected CLCuV is captured with a digital camera from cotton fields. An image enhancement tool is used to standardize the dataset for image analysis. Histogram, gray level co-occurrence matrix, and run length matrix features are extracted by the image analysis tool. Fisher, Probability of error plus average correlation and Mutual information feature optimization techniques are used to get the most optimal features to reduce computation costs. MultiClass, Bagging, Logistic Model Tree (LMT), and Radom Forest (RF) machine learning (ML) classifiers are deployed to observe the impact of CLCuV. True Positive (TP) rate, False Positive (FP) rate, Precision, Recall, F-measure, Mathews Correlation Coefficient (MCC), Receiver Operating Characteristic (ROC), and Precision Recall Curve (PRC) performance evaluation parameters are calculated to measure the effectiveness of ML classifiers. The RF classifier outperformed and demonstrated 87.542% accuracy, while other ML classifiers also achieved satisfactory results.