A review on Cotton Leaf Disease Detection with Balanced and Imbalanced Data

Abstract

Cotton is a major crop in India, and its production is essential to the country's economy. However, cotton plants are susceptible to a number of diseases, which can cause significant yield losses. Early detection of these diseases is crucial, but manual identification can be challenging. This paper reviews the use of machine learning for cotton leaf disease detection. A number of classification methods and algorithms are discussed, including convolutional neural networks (CNNs), random forests, and SMOTE. The results show that machine learning can be used to achieve high accuracy in detecting cotton leaf diseases, even in the case of imbalanced datasets. The paper also discusses the importance of balancing class distributions in order to improve model performance. A number of data-level and classifier-level techniques are evaluated, and the results show that the Weighted Random Forest algorithm is effective in handling imbalanced datasets. The findings of this paper suggest that machine learning is a promising tool for the early detection of cotton leaf diseases. This could help to reduce yield losses and improve the profitability of cotton farming in India.