Trend-Enabled Recommender System with Diversity Enhancer for Crop Recommendation

Iulia Baraian (), Rudolf Erdei (), Rares Tamaian, Daniela Delinschi, Emil Marian Pasca and Oliviu Matei
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Iulia Baraian: Department of Electrical, Electronic and Computer Science, Technical University of Cluj Napoca, 400114 Cluj-Napoca, Romania
Rudolf Erdei: Department of Electrical, Electronic and Computer Science, Technical University of Cluj Napoca, 400114 Cluj-Napoca, Romania
Rares Tamaian: Department of Electrical, Electronic and Computer Science, Technical University of Cluj Napoca, 400114 Cluj-Napoca, Romania
Daniela Delinschi: Department of Electrical, Electronic and Computer Science, Technical University of Cluj Napoca, 400114 Cluj-Napoca, Romania
Emil Marian Pasca: Department of Electrical, Electronic and Computer Science, Technical University of Cluj Napoca, 400114 Cluj-Napoca, Romania
Oliviu Matei: Department of Electrical, Electronic and Computer Science, Technical University of Cluj Napoca, 400114 Cluj-Napoca, Romania

Agriculture, 2025, vol. 15, issue 15, 1-26

Abstract: Achieving optimal agricultural yields and promoting sustainable farming relies on accurate crop recommendations. However, the applicability of many current systems is limited by their considerable computational requirements and dependence on comprehensive datasets, especially in resource-limited contexts. This paper presents HOLISTIQ RS, a novel crop recommendation system explicitly designed for operation on low-specification hardware and in data-scarce regions. HOLISTIQ RS combines collaborative filtering with a Markov model to predict appropriate crop choices, drawing upon user profiles, regional agricultural data, and past crop performance. Results indicate that HOLISTIQ RS provides a significant increase in recommendation accuracy, achieving a MAP@5 of 0.31 and nDCG@5 of 0.41, outperforming standard collaborative filtering methods (the KNN achieved MAP@5 of 0.28 and nDCG@5 of 0.38, and the ANN achieved MAP@5 of 0.25 and nDCG@5 of 0.35). Significantly, the system also demonstrates enhanced recommendation diversity, achieving an Item Variety (IV@5) of 23%, which is absent in deterministic baselines. Significantly, the system is engineered for reduced energy consumption and can be deployed on low-cost hardware. This provides a feasible and adaptable method for encouraging informed decision-making and promoting sustainable agricultural practices in areas where resources are constrained, with an emphasis on lower energy usage.

Keywords: recommender system; markov process; system architecture; recommendation diversity; cold start problem; trend forecasting (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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