Abstract
Agriculture is the backbone of any developing country. Currently, there is no single model that can provide accurate yield predictions at a pan-India level. Thus, in this paper, a yield prediction model has been proposed, which can predict the annual yield for 36 crops, grown in 542 districts of India. The proposed method makes use of various machine learning algorithms, linear regression, support vector machines, and artificial neural networks, to achieve an average Root Mean Square Error of 1.065 (quintals per 10 acres). The proposed model, in addition to predicting yield for all the major districts with an average accuracy of over 90%, also covers more crops as compared to existing works.
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Timbadia, D.H., Sudhanvan, S., Shah, P.J., Agrawal, S. (2021). Crop Yield Prediction for India Using Regression Algorithms. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-030-88244-0_23
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