Machine learning for sugarcane disease classification and prediction: A comprehensive survey

Machine learning has become an essential technology in various fields, including agriculture. Farmers can benefit greatly from the insights and recommendations provided by machine learning algorithms, which can help minimize losses and increase crop yields. In this research review essay, we will focus on the use of machine learning for sugarcane disease classification and prediction. Sugarcane is an important cash crop that is cultivated in many regions of the world. However, it is vulnerable to various diseases that can significantly affect its yield. Some of the common sugarcane diseases include smut, leaf scald, and gummosis. We will provide a comprehensive survey of the different techniques used in this field, including data collection and preprocessing, feature selection and extraction, and machine learning algorithms for sugarcane disease classification.