Yield Prediction and Recommendation of Crops in the Northeastern Region Using Machine Learning Regression Models

Yıl 2023, , 700 - 708, 31.12.2023

Nisha Sharma Mala Dutta

https://doi.org/10.29133/yyutbd.1321518

Cited By: 2

Öz

Agriculture has a big impact on society because it is essential for a large percentage of our food. The issue of hunger is getting worse by a growing population in many nations, resulting in food shortages or insufficiencies. To meet the world's food needs, it is ever more crucial to provide crop protection, conduct detailed land surveys, and predict crop yields. To calculate the estimated number of crops that are produced in a year, this research focuses on the use of machine learning techniques to predict crop yield and recommend crops with the highest yield in the Northeast region of India. The crop market's fluctuations in prices may be controlled with the aid of this information. To estimate agricultural crop yields, this study accurately evaluates a range of machine learning regression models, such as Linear Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost (eXtreme Gradient Boosting), and AdaBoost. With a 0.98 R2 score for the XGBoost and 0.96 for the Random Forest, they performed better than the other models.

Anahtar Kelimeler

Crop yield prediction, Machine Learning, Ensemble Learning, Recommender system, Regression

Kaynakça

• Agri Farming. (2023). Cabbage Cultivation: Income, Cost, Profit - A Project Report. Agrifarming. in. Retrieved September, 13,2023, from https://www.agrifarming.in/cabbage-cultivation-income- cost-profit-project-report.
• Asia Farming. (2023). Ginger Farming Business Plan: A Comprehensive Guide for Successful, Profitable Cultivation and Harvesting. Asia Farming. Retrieved September, 11, 2023. https://www.asiafarming.com/ginger-farming-business-plan-a-comprehensive-guide-for- successful-profitable-cultivation-and-harvesting.
• Babu, S. (2013). A Software model for precision agriculture and marginal farmers Paper presented at the IEEE Global Humanitarian Technology of Conference: South Asia satellite (GHTC-SAS), Trivandrum, India. http://dx.doi.org/10.1109/GHTC-SAS.2013.6629944.
• Deepa M., Sowmiya, V., Tamizhan, E., Venkat V.M.P., & Ranjani, S. (2023). Crop recommender system Based on Machine Learning. International Journal for Innovative Research in a multidisciplinary field https://doi.org/10.2015/IJIRMF/202303020)
• Everingham, Y., Sexton, J., Skocaj, D., & Bamber, G. I. (2016). Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development, 36(2), 27- 35. http://dx.doi.org/10.1007/s13593-016-0364-z.
• Garanayak, M., Sahu, G., Mohanty, S. N., & Jagadev, A. K. (2021). Agricultural recommendation system for crops using different machine learning regression methods. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 12(1), 1-20. http://doi.org/10.4018/IJAEIS.20210101.oa1.
• Government of India. (2023). Press Information Bureau, Government of India. https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1935899#:~:text=291.975%2Fqtl%20f or%20sugarcane%20in,157%2Fqtl.
• Kale, S. S., & Patil, P.S. (2019). A Machine learning approach to predict crop yield and success rate paper presented at IEEE Pune Section International Conference (PuneCon), Pune, India, 2019, 1-5. https://doi.org/10.1109/PuneCon46936.2019.9105741.
• Kumar, P. (2018). India Crop Production - State wise. https://data.world/thatzprem/agriculture- india. Retrieved March, 04, 2023.
• Kumar, R., Singh, M.P, Kumar, P., & Singh J.P. (2015). Crop selection method to maximize crop yield rate using machine learning techniques Paper presented at International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM). https://doi.org/10.1109/ICSTM.2015.7225403.
• Panigrahi, B., Kathala, K.C.R., & Sujatha, M. (2023). Machine Learning based Comparative Approach to Predict the Crop Yield using Supervised Learning with Regression Models paper Presented at International Conference on Machine Learning and Data Engineering. https://doi.org/10.1016/j.procs.2023.01.241.
• Patowary, M., Kumar, S., & Singh, V. (2022). A Study on Marketing aspects of Banana in Goalpara District of Assam. IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS), 15(5), 01-08. https://doi.org/10.9790/2380-1505010108.
• Paul, M., Vishwakarma, S.K., & Verma, A. (2015). Analysis of Soil behavior and Prediction of Crop Yield using Data Mining Approach Paper presented at International Conference of Computational Intelligence and Communication Networks. https://doi.org/10.1109/CICN.2015.156.
• Potnuru, N. S., Pinapa V. S., Bollu, A.L., & Jabber, B. (2020). Crop Yield Prediction based on Indian Agriculture using Machine Learning Paper presented at 2020 International Conference for Emerging Technology (INCET). Belgaum, India. 1-4. https://doi.org/10.1109/INCET49848.2020.9154036
• Renuka, & Terdal, S. (2019). Evaluation of Machine learning algorithms for Crop Yield Prediction. International journal of engineering and advanced Technology. pp 4082- 4086 8(6). http://www.doi.org/10.35940/ijeat.F8640.088619.
• Savla, A., Dhawan, P., Bhadada H., Israni, N., Mandholia, A., & Bhardwaj, S. (2015). Survey of Classification algorithms for formulating yield prediction accuracy in precision agriculture Paper presented at Innovations in Information, Embedded, and Communication Systems (ICIIECS). Coimbatore, India. 1-7. https://doi.org/10.1109/ICIIECS.2015.7193120.
• Shastry, A., Sanjay, H. A., & Bhanusree, E. (2017). Prediction of crop yield using regression techniques. International Journal of Soft Computing, 12(2), 96-102. DOI: 10.36478/ijscomp.2017.96.102
• Singh, V., Sarwar, A., & Sharma, V. (2017). Analysis of soil and prediction of crop yield (Rice) using machine learning approach. International Journal of Advanced Research in Computer Science, 8(5), 1254-1259.
• Times of India. (2019). Erode tapioca farmers reap profit after price shoots up. Times of India. Retrieved September, 11, 2023. https://timesofindia.indiatimes.com/city/salem/erode- tapioca-farmers-reap-profit-after-price-shoots-up/articleshow/69934516.cms.
• Ung, P. C., & Mittrapiyanuruk, P. (2018). Sugarcane Yield Grade Prediction using Random Forest and Gradient Boosting Tree Techniques Paper presented at 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE), Nakhonpothom, Thailand. 1-6. https://doi.org/10.1109/JCSSE.2018.8457391.
• Yang, L. (2011). Classifiers selection for ensemble learning based on accuracy and diversity. Procedia Engineering, 15, 4266-4270. https://doi.org/10.1016/j.proeng.2011.08.800.