A LITERATURE SURVEY OF CROP RECOMMENDATION IN AGRICULTURE

With over 60% of the workforce employed in the sector and contributing over 17% of the country's GDP, agriculture is vital to the Indian economy. But the agricultural industry deals with a lot of challenges, including a number of difficult tasks like harvesting, threshing, winnowing, the bagging method, shipping, storage, processing, and trading, all of which contribute to significant crop losses at various stages before reaching the market. Indian agriculture encounters challenges such as uncertain water supply, inadequate income generation, and fragmented land holdings. Previous studies have primarily centered on using meteorological data and soil factors to forecast about which crops would grow best on a particular soil. To deal with this kind of problem, numerous researchers have created their own Crop Recommendation System models using a variety of Deep Learning and Machine Learning techniques. This article presents a comprehensive overview of different algorithms for deep learning and machine learning that is utilized in constructing Crop Recommendation Systems, providing valuable insights into the most effective algorithms for this purpose.