Abstract
Agriculture is the preliminary source of income for most the people of numerous counties. Traditional ways of farming process have lots of issues such as lack of knowledge of crops, fertilizers, and pesticide selection and usage. These can reduce crop yield, crop quality, and farmer's profit. Withal, technology is continually being modified and streamlined. The usage of computational methods like machine learning (ML) and recommendation system (RS) can enable farmers to make smart judgments rapidly and precisely which will increase profitability. Presently, a large number of data related to agriculture are available on the Internet. Several learning algorithms and recommendation system approach will be helpful to generate the model using available data and forecast the crops, fertilizers, pesticides, crop yields, and profit. Through this paper, we are doing a detailed review which will elaborate category of machine learning and recommendation systems, use of ML and RS in agriculture, work carried out so far, problems in agriculture with how technology will be helpful, and future possible accretions.
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Patel, K., Patel, H.B. (2023). Machine Learning and Recommendation System in Agriculture: A Survey and Possible Extensions. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, vol 565. Springer, Singapore. https://doi.org/10.1007/978-981-19-7455-7_53
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