Authors : Spoorthi A. Hunshal; Sanjana R.; Himani Pitta; Shiva Kumar R. Naik

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/mvf82fym

Scribd : https://tinyurl.com/4edzwh3r

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY215

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the recent years ensuring food security plays a major role in the agricultural sector and contributing towards the nations growth. This paper presents a communal system that makes use of elegant machine learning techniques and models to forecast accurate yield of the selected crop and advocates the felicitous fertiliser. By exploiting the agricultural datasets, our system employs the Extra Trees Regressor trees which helps for the prediction of the yield and to analyse the most recommendable fertilizer it makes use of the Gaussian Naïve Bayes algorithm. This dormant system provides the us with powerful insights. Our sight is to reshape the conventional agricultural practices with the help of these powerful insights to redefine the farming practices and to increase the productivity, therefore ensuring the legitimate agricultural practices.

Keywords : Crop Yield Prediction, Fertilizer Recommendation, Machine Learning Algorithms, Extra Trees Regressor (ETR), Gaussian Naïve Bayes (GNB).

References :

1. Elavarasan, D., & Vincent, P. D. (2020). Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE access, 8, 86886-86901.
2. Bang, S., Bishnoi, R., Chauhan, A. S., Dixit, A. K., & Chawla, I. (2019, August). Fuzzy Logic based Crop Yield Prediction using Temperature and Rainfall parameters predicted through ARMA, SARIMA, and ARMAX models. In 2019 Twelfth international conference on contemporary computing (IC3) (pp. 1-6). IEEE.
3. Archana, K., & Saranya, K. G. (2020). Crop yield prediction, forecasting and fertilizer recommendation using Data mining algorithm. International Journal of Computer Science Engineering (IJCSE), 9(1), 76-79.
4. Somwanshi, K., Sonawane, P. R., Lohar, T. S., & Jadhav, M. S. Crop Prediction and Fertilizer Recommendation Using Machine Learning.
5. Bondre, D. A., & Mahagaonkar, S. (2019). Prediction of crop yield and fertilizer recommendation using machine learning algorithms. International Journal of Engineering Applied Sciences and Technology, 4(5), 371-376.
6. Zhang, X., Xu, M., Sun, N., Xiong, W., Huang, S., & Wu, L. (2016). Modelling and predicting crop yield, soil carbon and nitrogen stocks under climate change scenarios with fertiliser management in the North China Plain. Geoderma, 265, 176-186.
7. Ghadge, R., Kulkarni, J., More, P., Nene, S., & Priya, R. L. (2018). Prediction of crop yield using machine learning. Int. Res. J. Eng. Technol.(IRJET), 5, 2237-2239.
8. [8] Filippi, P., Jones, E. J., Wimalathunge, N. S., Somarathna, P. D., Pozza, L. E., Ugbaje, S. U., ... & Bishop, T. F. (2019). An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precision Agriculture, 20, 1015-1029.
9. Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and electronics in agriculture, 151, 61-69.
10. Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., ... & Kim, S. H. (2016). Random forests for global and regional crop yield predictions. PloS one, 11(6), e0156571.