A dependency learning approach to delineate site-specific management zones Academic Article in Scopus uri icon

abstract

  • Precision agriculture offers solutions based on scientific knowledge to help farmers make better decisions in the agricultural cycle. In the initial phase of this cycle, it is crucial to identify an adequate delineation of site-specific management zones to optimize inputs and enhance crop yields. Based on a soil property that can be chemical, physical, or biological, the delineation of a site-specific management zone problem involves minimizing the number of zones that reach a homogeneity level according to the selected soil property. Moreover, the solution discussed in this work addresses orthogonal shapes, making delineation tasks easier for farmers with access to modern technology or traditional agricultural machinery. In this work, we propose a novel approach, not previously presented in the literature, for the site-specific management zone problem, based on dependency trees that account for the various interactions between variables. Additionally, to minimize the number of zones, we incorporated into the objective the minimization of the variances within each zone. The proposed new approach was evaluated using two different objective functions: one aimed at minimizing the number of zones, and the other focused on minimizing both the number of zones and the sum of the variances within those zones. The computational results show a 100% improvement in the total number of generated zones for small and medium instances compared with the leading methods in the literature that incorporate orthogonal site-specific management zones. This improvement was statistically validated using the Wilcoxon signed-rank test and confirmed through effect size analysis. © 2025 International Federation of Operational Research Societies.

publication date

  • January 1, 2025