Abstract
Crop production is one of the most important sources of the Indian economy and one of the income sources for farmers in India. Prediction of crop yield in advance can guide farmers and improve revenue. In Agriculture Section, Maharashtra is one of the leading states from India. One of the principal foods in Maharashtra is Soybean. The proposed method uses a different ensemble model for prediction of Soybean crop yield of Marathwada region from Maharashtra. The crop yield data have been collected from the Indian Government's records. The environmental data like temperature details, rainfall details, and rainy days for the years 2001–2019 have been collected from India Meteorological Department. The dataset of crop yield and dataset of features have been preprocessed and different dominant features are identified using recursive elimination method. Different types of ensemble models have been applied and performance evaluation has been done using measures like mean absolute error, root mean squared error, and mean squared error. After analyzing performance of different ensemble models, we have found AdaBoost Decision Tree Regressor and AdaBoost Linear Regressor performed well as compared to AdaBoost Random Forest Regressor.
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References
Gonzalez-Sanchez, Frausto-Solis, Ojeda-Bustamante (2014) Attribute selection impact on linear and nonlinear regression models for crop yield prediction. Sci World J
Gandhi N, Petkar O, Armstrong LJ (2016) Rice crop yield prediction using artificial neural networks. IEEE international conference on technological innovations in ICT for agriculture and rural development, pp 105–110
Zingade DS, Buchade O, Mehta B, Ghodekar S, Mehta C (2017) Crop prediction system using machine learning. Int J Adv Eng Res Develop 4(5):1–6
Sharma SK, Bhagat DV, Ranjeet PD, Khapedia HL, Mirdha IS, Sikarwar RS (2018) Soybean and wheat crop yield forecasting based on statistical model in Malwa agroclimatic zone. Int J Chem Stud
Oliveira G, Cunha RLF, Silva B, Netto MAS (2018) A Scalable machine learning system for preseason agriculture yield forecast. In: 2018 IEEE 14th international conference on e-science
Yesugade KD, Chudasama H, Kharde A, Mirashi K, Muley K (2019) Crop suggesting system using unsupervised machine learning algorithm. Int J Comput Sci Eng
Dharmaraja S, Jain V, Anjoy P, Chandra H (2020) Empirical analysis for crop yield forecasting in India. Research article. Springer
Kale KS, Patil PS (2019) A machine learning approach to predict crop yield and success rate. In: 2019 IEEE Pune section international conference (PuneCon) MIT World Peace University, Pune, India
Balakrishnan N, Muthukumarasamy G (2016) Crop production ensemble machine learning model for prediction. Int J Comput Sci Softw Eng 5(7):148–153
Priya P, Muthaiah U, Balamurugan M (2018) Predicting yield of the crop using machine learning algorithm. Int J Eng Sci Res Technol 7(1):1–7
Shyamala K, Rajeshwari I (2020) Enhanced gradient boosting regression tree for crop yield prediction. Int J Sci Technol Res 9(03)
Khan R (2020) Crop yield prediction using gradient boosting regression. Int J Innov Technol Exploring Eng 9(3) (IJITEE). ISSN: 2278-3075 (Online)
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Hajgude, J., Sarode, T. (2023). Comparative Study of Ensemble Models for the Prediction of Crop Yield. In: Kumar, S., Hiranwal, S., Purohit, S., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies. ICCCT 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-3485-0_30
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DOI: https://doi.org/10.1007/978-981-99-3485-0_30
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