Fuzzy Logic for Yield Prediction: Enhancing Decision-Making in Agricultural Economics

Rahib ImamguluyevAgil GurbanovAyatulla JabbarovShalala HasanovaGunay RasulovaSevinj KarimovaJeyran KhalilovaReyhan AzizovaLamiya Tahirova

DOI 10.7160/aol.2025.170303
No 3/2025, September
pp. 27-36

Imamguluyev, R., Gurbanov, A., Jabbarov, A., Hasanova, S., Rasulova, G., Karimova, S., Khalilova, J., Azizova, R. and Tahirova, L. (2025) "Fuzzy Logic for Yield Prediction: Enhancing Decision-Making in Agricultural Economics", AGRIS on-line Papers in Economics and Informatics, Vol. 17, No. 3, pp. 27-36. ISSN 1804-1930. DOI 10.7160/aol.2025.170303.

Abstract

Accurate yield prediction is essential for optimizing decision-making in agricultural economics, enabling stakeholders to manage resources efficiently and respond to market demands. Traditional yield prediction models often struggle to handle the uncertainties and complexities inherent in agricultural systems, such as weather variability, soil conditions, and crop characteristics. This study introduces a fuzzy logic-based approach to yield prediction, offering a more flexible and robust method for addressing these uncertainties. By utilizing fuzzy sets and rules, the proposed model captures the intricate relationships between multiple factors influencing crop yield. The research demonstrates how fuzzy logic can enhance the accuracy and reliability of yield predictions, providing valuable insights for farmers, policymakers, and agricultural economists. Results indicate that this approach significantly improves decision-making processes in agricultural planning and risk management, making it a valuable tool for sustainable agricultural practices.

Keywords

Yield prediction, fuzzy logic, agricultural economics, decision-making, crop yield, uncertainty modeling.

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