An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms [version 1; peer review: 2 approved, 1 approved with reservations]

• Published in: F1000 research 2021, Vol.10, p.1143-1143
• Main Authors: Anbananthen, Kalaiarasi Sonai Muthu, Subbiah, Sridevi, Chelliah, Deisy, Sivakumar, Prithika, Somasundaram, Varsha, Velshankar, Kethaarini Harshana, Khan, M.K.A.Ahamed
• Format: Article
• Language: English
• Subjects: Agricultural production; Algorithms; Crop; Crop yield; Crops; Datasets; Decision making; Decision support systems; eng; Food security; Forecasting; Learning algorithms; Machine Learning; Neural networks; Prediction; Random Forest; Regression; Stacked Generalization; Statistical analysis; Weather forecasting
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.73009.1

Background: 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. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset.  The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.