International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Crop Yield Prediction and Recommendation System Using Machine Learning

Abstract

About half of the population of India depends on agriculture for its livelihood, but its contribution towards the GDP of India is only 14 per cent. One possible reason for this is the lack of adequate crop planning by farmers. There is no system in place to advice farmers what crops to grow. In this paper we present an attempt to predict crop yield and price that a farmer can obtain from his land, by analysing patterns in past data. We make use of a sliding window non-linear regression technique to predict based on different factors affecting agricultural production such as rainfall, temperature, market prices, area of land and past yield of a crop. The analysis is done for several districts of the state of Tamilnadu, India. Our system intends to suggest the best crop choices for a farmer to adapt to the demand of the prevailing social crisis facing many farmers today.

Country : India

1 Kamalesh M2 Dr. Ragaventhiran J

  1. M.Tech. Artificial Intelligence, Department of CSE, School of Engineering, Presidency University, Bangalore, India
  2. Professor, School of Engineering, Presidency University, Bangalore, India

IRJIET, Volume 7, Issue 5, May 2023 pp. 214-217

doi.org/10.47001/IRJIET/2023.705026

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References

  1. G. Banerjee, U. Sarkar, I. Ghosh, A Fuzzy Logic-Based Crop Recommendation System, Springer Singapore, 2021. Doi:10.1007/978-981-15-7834-2 6.
  2. A.Karwande, M. Wyawahare, T. Kolhe, S. Kamble, R. Magar, L. Maheshwari, Prediction of the Most Productive Crop in a Geographical Area Using Machine Learning, Lecture Notes in Networks and Systems 141 (2021) 433–441. Doi:10.1007/978-981-15-7106-0 43.
  3. F. Sari, F. Koyuncu, Multi Criteria Decision Analysis to Determine the Suitability of Agricultural Crops for Land Consolidation Areas, International Journal of Engineering and Geosciences 6 (2) (2021) 64–73. doi:10.26833/ijeg.683754.
  4. A.Liu, T. Lu, B. Wang, C. Chen, Crop Recommendation via Clustering Center Optimized Algorithm for Imbalanced Soil Data, in: 5th International Conference on Control, Robotics and Cybernetics (CRC), IEEE, 2020, pp. 31–35. doi:10.1109/CRC51253.2020.9253457.
  5. S. Jain, D. Ramesh, Machine Learning convergence for weather-based crop selection, IEEE International Students’ Conference on Electrical, Electronics and Computer Science (2020). doi:10.1109/SCEECS48394.2020.75.
  6. K. Patel, H. B. Patel, A state-of-the-art survey on recommendation system and prospective extensions, Computers and Electronics in Agriculture 178 (2020). doi:10.1016/j.compag.2020.105779.
  7. A.M. Rajeswari, A. S. Anushiya, K. S. A. Fathima, S. S. Priya, N. Mathumithaa. Fuzzy Decision Support System for Recommendation of Crop Cultivation based on Soil Type, Proceedings of the 4th International Conference on Trends in Electronics and Informatics (2020). doi:10.1109/ICOEI48184.2020.9142899.
  8. R. M.-E. M. Cadenas, M. Carmen, Development of an application to make knowledge available to the farmer: Detection of the most suitable crops for a more sustainable agriculture, Journal of Ambient Intelligence and Smart Environments 12 (5) (2020) 419–432. Doi: 10.3233/AIS-200575.
  9. R. Aarthi, D. Sivakumar, Modeling the Hierarchical Fuzzy System for Suitable Crop Recommendation, in: Lecture Notes in Electrical Engineering, Vol. 686, Springer Science and Business Media Deutschland GmbH, 2020, pp. 199–209. Doi:10.1007/978-981-15-7031-5 19.
  10. K. Patel, H. B. Patel, A state-of-the-art survey on recommendation system and prospective extensions, Computers and Electronics in Agriculture 178 (2020). doi:10.1016/j.compag.2020.105779.

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