A Genetic Algorithm for Site-Specific Management Zone Delineation

Francisco Huguet, Lluís M. Plà-Aragonés (), Víctor M. Albornoz and Mauricio Pohl
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Francisco Huguet: Department de Matemàtica, Universitat de Lleida, c/ Jaume II, 73, 25003 Lleida, Spain
Lluís M. Plà-Aragonés: Department de Matemàtica, Universitat de Lleida, c/ Jaume II, 73, 25003 Lleida, Spain
Víctor M. Albornoz: Departamento de Industrias, Campus Santiago Vitacura, Universidad Técnica Federico Santa María, Av. Santa María 6400, Santiago 7650568, Chile
Mauricio Pohl: Department of Electronics and Informatics, Universidad Centroamericana UCA, Bulevar Los Próceres, Antiguo Cuscatlán, La Libertad 01-168, El Salvador

Mathematics, 2025, vol. 13, issue 7, 1-18

Abstract: This paper presents a genetic algorithm-based methodology to address the Site-Specific Management Zone (SSMZ) delineation problem. A SSMZ is a subregion of a field that is homogeneous with respect to a soil or crop property, enabling farmers to apply customized management strategies for optimizing resource use. The algorithm generates optimized field partitions using rectangular zones, applicable to both regular and irregularly shaped fields. To the best of our knowledge, the Genetic Algorithm for Zone Delineation (GAZD) is the first approach to handle the rectangular SSMZ delineation problem in irregular-shaped lands without introducing non-real data. The algorithm’s performance is compared with an exact solution based on integer linear programming. Experimental tests conducted on real-field and generated irregular-shaped instances show that while the GAZD requires longer execution times than the exact approach, it proves to be functional and robust in solving the SSMZ problem. Furthermore, the GAZD offers a set of “good enough” solutions that can be evaluated for feasibility and practical convenience, making it a valuable tool for decision-making processes. Moreover, strategies such as implementation in a compiled language and parallel processing can be used to improve the execution time performance of the algorithm.

Keywords: genetic algorithms; management zone delineation; operations research; agriculture (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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