Citation

Sonai Muthu Anbananthen, Kalaiarasi and Subbiah, Sridevi and Chelliah, Deisy and Sivakumar, Prithika and Somasundaram, Varsha and Velshankar, Kethaarini Harshana and Khan, M. K. A. Ahamed (2021) An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms. F1000Research, 10 (1143). pp. 1-18. ISSN 2046-1402

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Abstract

In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used.

Item Type:	Article
Uncontrolled Keywords:	Machine Learning, Prediction, Crop, Stacked Generalization, Random Forest, Regression
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	22 Feb 2022 02:45
Last Modified:	27 Apr 2023 13:23
URII:	http://shdl.mmu.edu.my/id/eprint/9965

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