Ai-Based Models Classification for Cotton Health Monitoring

Cotton is essential as a crop, serving food and textile purposes. However, cotton is susceptible to various diseases. Detecting diseases in cotton leaves and plants using the bare eye is difficult. Therefore, numerous research papers have developed machine learning (ML) algorithms to enhance the efficiency of this process and boost cotton production. This paper reviews different types of ML models, such as decision trees (DT), convolutional neural networks (CNN), deep neural networks (DNN), k-nearest neighbors (KNN), random forests (RF), artificial neural networks (ANN), support vector machines (SVM), and transfer learning (TL). The paper discusses the strengths and weaknesses of various models. This review paper aims to provide researchers with a comprehensive overview of the various models used to detect cotton leaf and plant diseases.