Abstract
Agriculture is extremely vital to our economy and boosting the development of this sector always adds up to the economic & political value of our country. Health of all the crops grown is affected by various aspects including technological, biological, and environmental factors. The environmental facet particularly has been drastically changing, posing challenges in front of the peasants. They face a significant difficulty in determining the optimal crop for their farming region to maximize productivity and profit. For Indian farmers, there is no existing reliable recommendation mechanism. Giving an address to this issue, the study proposes a crop recommendation system based on a multi-label classification model which considers the location of peasants, composition of soil, and weather characteristics, and provides a ranked list of suggested crop seed to be produced for greater yield. Researchers compare many algorithms based upon the performance criteria and capabilities to develop the best recommendation model for crops. With a precision of 82.74%, a recall of 80.92%, and an F1 score of 78.67%, the most optimal model was revealed to be an RF Technique. The trained model proved advantageous in catering the farmers with a ranked list of crops deployed along with an interface for better user experience.
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Arora, D., Sakshi, Drall, S., Singh, S., Choudhary, M. (2022). Farmright – A Crop Recommendation System. In: Rajagopal, S., Faruki, P., Popat, K. (eds) Advancements in Smart Computing and Information Security. ASCIS 2022. Communications in Computer and Information Science, vol 1759. Springer, Cham. https://doi.org/10.1007/978-3-031-23092-9_27
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