AgroSupportAnalytics: Big data recommender system for agricultural farmer complaints in Egypt

Rslan, Esraa; Khafagy, Mohamed H; Ali, Mostafa; Munir, Kamran; Badry, Rasha M

Abstract

The world's agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers' agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and filtering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%.

Citation

Rslan, E., Khafagy, M. H., Ali, M., Munir, K., & Badry, R. M. (2023). AgroSupportAnalytics: Big data recommender system for agricultural farmer complaints in Egypt. International Journal of Electrical and Computer Engineering, 13(1), 746-755. https://doi.org/10.11591/ijece.v13i1.pp746-755

• Journal Article Type: Article
• Acceptance Date: Aug 18, 2022
• Publication Date: Feb 1, 2023
• Deposit Date: Oct 12, 2022
• Publicly Available Date: Mar 2, 2023
• Journal: International Journal of Electrical and Computer Engineering
• Electronic ISSN: 2088-8708
• Publisher: Institute of Advanced Engineering and Science
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 1
• Pages: 746-755
• Series ISSN: 2088-8708
• DOI: https://doi.org/10.11591/ijece.v13i1.pp746-755
• Keywords: Agricultural recommender system; Latent semantic analysis; Semantic textual similarity; Support vector machine classification
• Public URL: https://uwe-repository.worktribe.com/output/10037867
• Publisher URL: https://ijece.iaescore.com/index.php/IJECE/article/view/26531