Abstract
Indian Agriculture is primarily dependent on rainfall distribution throughout the year. There have been several instances where crops have failed due to inadequate rainfall. This study aims at predicting rainfall considering those factors which have been correlated against precipitation, across various crop growing regions in India by using regression analysis on historical rainfall data. Additionally, we’ve used season-wise rainfall data to classify different states into crop suitability for growing major crops. We’ve divided the four seasons of rainfall as winter, pre-monsoon, monsoon, and post-monsoon. Finally, a bipartite cover is used to determine the optimal set of states that are required to produce all the major crops in India, by selecting a specific set of crops to be grown in every state, and selecting the least number of states to achieve this. The data used in this paper is taken from the Indian Meteorological Department (IMD) and Open Government Data (OGD) Platform India published by the Government of India.
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Rao, P., Sachdev, R., Pradhan, T. (2016). A Hybrid Approach to Rainfall Classification and Prediction for Crop Sustainability. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_39
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DOI: https://doi.org/10.1007/978-3-319-28658-7_39
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