Abstract
Agriculture is one of the most important sectors that affect a country’s economic development. Agriculture employs about 65% of the Indian population and accounts for about 22% of gross domestic product (GDP), yet they earn only a few percent of the country’s GDP. This is due to the lack of knowledge and awareness about which seeds to sow and how to deal with pests, both of which can have a significant impact on productivity. Hence, this paper proposes a method for combining AI with agriculture to develop a smart agricultural system that makes it easier for farmers to produce and maximize their output. Therefore, FarmEasy, a website that makes it simple for farmers to make the greatest decisions at the right time has been developed. The proposed method implements four major features, namely, crop recommendation based on nutrients present in soil and weather in that area, fertilizer recommendation based on nutrients in soil and crop, disease prediction based on a picture of the affected crop, and a Chatbot for farmers’ assistance to clear their queries and inquiries. According to the observations, FarmEasy has the ability to effectively satisfy the information demands of farmers on a large scale.
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Acknowledgements
This project would not have been feasible without the help and involvement of many individuals. We’d like to appreciate everyone who contributed to the project’s implementation, both directly and indirectly. First and foremost, we would like to express our gratitude to Dr. Remya S., Assistant Professor, Department of CSE, Amrita School of Engineering, Amritapuri, for her great guidance, encouragement, and timely assistance during the entire Project Phase. We appreciate our project coordinators/panel members, Assistant Professor Deepthi L. R., Assistant Professor, and Assistant Professor Sandhya Harikumar, for their active engagement and supervision.
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Vidya Sree, K., Sandeep Kumar Reddy, G., Dileep Varma, R., Mihira, P., Remya, S. (2023). A One-Stop Service Provider for Farmers Using Machine Learning. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_61
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