Crop Recommendation using XG Boost Algorithm for Sustainable Agrarian Application

Authors

  • Durai Arumugam S. S. L. Assistant Professor, Department of Information Technology, Easwari Engineering College, Ramapuram, Chennai,, Tamil Nadu.
  • Praveen Kumar R. Assistant Professor, Department of Electronics and Communication Engineerng , Easwari Engineering College, Ramapuram, Chennai,, Tamil Nadu.
  • Mahadevan B. Student, Department of Information Technology, Easwari Engineering College, Ramapuram, Chennai,, Tamil Nadu.
  • Akash V. Student, Department of Information Technology, Easwari Engineering College, Ramapuram, Chennai, Tamil Nadu.

Keywords:

CNN, Crop pattern, Decision tree, Extreme Gradient (XG) Boost, Random Forest, Recommendation System, Weather information

Abstract

India is primarily an agrarian nation, where agriculture holds significant sway over both the country's economy and the livelihoods of its population. Most of the Indian farmers face a common challenge of not selecting the most appropriate crop for their land in accordance with the environmental requirements. As a result, they will see a major decline in their total level of productivity. So to overcome this problem crops are suggested based on soil and crop pattern, weather information which includes humidity, rainfall, and temperature which plays a vital role in attaining quantity and quality of crops massively. So our paper’s aim is to analyse all these factors and match it with the required parameters of each crops and provide the best possible option to the farmers. This leads to a decrease in crop selection errors and an increase in yield. To achieve our aim we will use a recommendation system using an Extreme Gradient (XG) Boost and Random Forest, Decision tree and CNN algorithms to Suggesting a suitable crop based on the specific site parameters involves considering the unique conditions and characteristics of the area.

Downloads

Download data is not yet available.

References

Gupta, R., Sharma, A. K., Garg, O., Modi, K., Kasim, S., Baharum, Z., ... & Mostafa, S. A. (2021) “WB-CPI: Weather based crop prediction in India using big data analytics,” in IEEE access, 9, 137869-137885.

Dhivya Elavarasan and P. M. Durairaj Vincent (2020) “Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications,” in IEEE access, 8, 86886-86901.

R. Priya, D. Ramesh, and E. Khosla, ‘‘Crop prediction on the region belts of india: A Naïve Bayes MapReduce precision agricultural model,’’ in Proc. Int. Conf. Adv. Comput., Commun. Informat. (ICACCI), Sep. 2018, pp. 99–104.

W. Fan, C. Chong, G. Xiaoling, Y. Hua, and W. Juyun, ‘‘Prediction of crop yield using big data,’’ in Proc. 8th Int. Symp. Comput. Intell. Design (ISCID), Dec. 2015, pp. 255–260, doi: 10.1109/ISCID.2015.191.

M. Ramya, C. Balaji, and L. Girish, ‘‘Environment change prediction to adapt climate-smart agriculture using big data,’’ Int. J. Adv. Res. Comput. Eng. Technol., vol. 4, no. 5, pp. 1995–2000, 2015.

P. C. Reddy and A. S. D. Babu, ‘‘Survey on weather prediction using big data analystics,’’ in Proc. 2nd IEEE Int. Conf. Electr., Comput. Commun. Technol., Feb. 2017, pp. 1–6.

P. S. Vijayabaskar, R. Sreemathi, and E. Keertanaa, ‘‘Crop prediction using predictive analytics,’’ in Proc. Int. Conf. Comput. Power, Energy Inf. Commun. (ICCPEIC), Mar. 2017, pp. 370–373.

S. Rajeswari, R. Scholar, K. Suthendran, and K. Rajakumar, ‘‘A smart agricultural model by integrating IoT, mobile and cloud-based big data analytics,’’ in Proc. Int. Conf. Intell. Comput. Control, 2017, pp. 1–5.

K. Charvat, K. C. Junior, T. Reznik, V. Lukas, K. Jedlicka, R. Palma, and R. Berzins, ‘‘Advanced visualisation of big data for agriculture as part of databio development,’’ in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), Jul. 2018, pp. 415–418.

P. Shah, D. Hiremath, and S. Chaudhary, ‘‘Towards development of spark based agricultural information system including geo-spatial data,’’ in Proc. IEEE Int. Conf. Big Data (Big Data), Dec. 2017, pp. 3476–3481.

M. P. Darji, V. K. Dabhi, and H. B. Prajapati, ‘‘Rainfall forecasting using neural network: A survey,’’ in Proc. Int. Conf. Adv. Comput. Eng. Appl., Mar. 2015, pp. 706–713, doi: 10.1109/ICACEA.2015.7164782.

M. Motlhabi, P. Pantsi, and R. Netshiya, ‘‘A machine learning deepdive analysis into network logs,’’ in Proc. ICCWS, 2021, p. 213, doi: 10.34190/IWS.21.019.

Doshi, Z., Nadkarni, S., Agrawal, R., & Shah, N. (2018, August). AgroConsultant: intelligent crop recommendation system using machine learning algorithms. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (pp. 1-6). IEEE

Reddy, D.A.; Dadore, B.; Watekar, A. Crop Recommendation System to Maximize Crop Yield in Ramtek region using MachineLearning. Int. J. Sci. Res. Sci. Technol. 2019,6, 485–489.

P. S. Nishant, P. Sai Venkat, B. L. Avinash, and B. Jabber, ”Crop Yield Prediction based on Indian Agriculture using Machine Learning,” 2020 International Conference for Emerging Technology (INCET), 2020, pp. 1-4, DOI: 10.1109/INCET49848.2020.9154036.

G. E. O. Ogutu, W. H. P. Franssen, I. Supit, P. Omondi, and R. W. Hutjes, ‘‘Probabilistic maize yield prediction over East Africa using dynamic ensemble seasonal climate forecasts,’’ Agricult. Forest Meteorol., vols. 250–251, pp. 243–261, Mar. 2018.

K. Ryan, P. Agrawal, and S. Franklin, ‘‘The pattern theory of self in artificial general intelligence: A theoretical framework for modeling self in biologically inspired cognitive architectures,’’ Cognit. Syst. Res., to be published.

M. E. Holzman, F. Carmona, R. Rivas, and R. Niclòs, ‘‘Early assessment of crop yield from remotely sensed water stress and solar radiation data,’’ ISPRS J. Photogramm. Remote Sens., vol. 145, pp. 297–308, Nov. 2018.

M. M. Rahman, D. Hagare, and B. Maheshwari, ‘‘Bayesian belief network analysis of soil salinity in a peri-urban agricultural field irrigated with recycled water,’’ Agricult. Water Manage., vol. 176, pp. 280–296, Oct. 2016.

Y. Dash, S. K. Mishra, and B. K. Panigrahi, ‘‘Rainfall prediction for the Kerala state of India using artificial intelligence approaches,’’ Comput. Elect. Eng., vol. 70, pp. 66–73, Aug. 2018.

P. S. Dutta and H. Tahbilder, ‘‘Prediction of rainfall using data mining technique over Assam,’’ Indian J. Comput. Sci. Eng., vol. 5, no. 2, pp. 85–90, Apr./May 2014

W. Wieder, S. Shoop, L. Barna, T. Franz, and C. Finkenbiner, ‘‘Comparison of soil strength measurements of agricultural soils in Nebraska,’’ J. Terramech., vol. 77, pp. 31–48, Jun. 2018.

Y. Cai, K. Guan, J. Peng, S. Wang, C. Seifert, B. Wardlow, and Z. Li, ‘‘A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach,’’ Remote Sens. Environ., vol. 210, pp. 35–47, Jun. 2018.

A. Kaya, A. S. Keceli, C. Catal, H. Y. Yalic, H. Temucin, and B. Tekinerdogan, ‘‘Analysis of transfer learning for deep neural network based plant classification models,’’ Comput. Electron. Agricult., vol. 158, pp. 20–29, Mar. 2019.

K. Abrougui, K. Gabsi, B. Mercatoris, C. Khemis, R. Amami, and S. Chehaibi, ‘‘Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR),’’ Soil Tillage Res., vol. 190, pp. 202–208, Jul. 2019.

Praveenkumar R, Kirthika, Durai Arumugam and Dinesh (2023), “Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection”. IJEER 11(2), 426-433.

Praveen Kumar, R, Jennifer S Raj & Smys, S 2022, “Performance Analysis of Hybrid Optimization Algorithm for Virtual Head Selection in Wireless Sensor Networks”, Wireless Personal Communications.

Praveen Kumar, R, Jennifer S Raj & Smys, S 2021, “Analysis of dynamic topology wireless sensor network for the internet of things”, International Journal of Communication Systems, vol. 34, no. 17.

SSL, D. A., Praveenkumar, R., & Balaji, V. (2023). An Intelligent Crop Recommendation System using Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 423-428.

Downloads

Published

24.03.2024

How to Cite

Arumugam S. S. L., D. ., R., P. K. ., B., M. ., & V., A. (2024). Crop Recommendation using XG Boost Algorithm for Sustainable Agrarian Application. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 306–311. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5253

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.