Comparative and Comprehensive Analysis of Cotton Crop Taxonomy Classification

India’s economy is built upon agriculture, which offers the common of the country’s inhabitants with an existing and accounts for 40% of the nation’s overall GDP. Agriculture is a major component of an argo economy like India’s. The Indian economy benefits from the agricultural sector as well as the industrial sector and foreign import and export trade. Even while the agricultural sector in India currently employs the most people nationwide, its contribution to the economy is shrinking. Cotton is one of the most important commercial crops grown in India; it accounts for about 25% of all cotton produced globally. It is a major source of income for about 40–50 million workers in sectors like trading and cotton processing, as well as 6 million cotton growers. The objective of this article is to provide a summary of the machine learning techniques used to identify and predict a variety of diseases in cotton crops using machine learning and artificial neural networks. An article has thoroughly examined numerous machine learning algorithms and their uses in the field of agricultural disease for this goal. The study also shows how machine learning methods are used in the subject of agricultural disease identification in a comparative and comprehensively tabular manner. In the field of cotton crop disease identification, the article also covered the potential application of machine learning algorithms in the future.