The first stage in the precision agriculture cycle has been a vital study area in recent years because it allows soil testing followed by data analysis. In this stage, a strategic delineation of site-specific management zones acquires a particular interest because it enables site-specific treatment to improve crop yield by efficiently using the input of resources. The delineation of site-specific management zones problem is to determine the minimum number of zones that cover the entire field so that each zone’s homogeneity is significant according to a specific biological, chemical, or physical soil property. Furthermore, the delineated zones should be orthogonal-shaped to be practical for agricultural machinery. This work has proposed a new bio-inspired algorithm, specifically an Estimation of Distribution Algorithm, based on a decoder that heavily relies on the Disjoint-Set algorithm and a new reactive penalized fitness function that detects unfeasible solutions. The new methodology improves the solutions presented in the literature by using a new search engine that drastically reduces the computational times of similar algorithms. Our algorithm has been tested with the literature benchmark, considering a new reactive penalization in the fitness function. It obtains the best solutions for 66.66% of the instances benchmark compared to the best literature method. Due to the algorithm’s efficiency, a new set of larger instances is introduced to test the scalability and robustness of the method. It obtained an efficiency of 79.3%.

近年来，精准农业周期的第一阶段一直是一个重要的研究领域，因为它允许进行土壤测试，然后进行数据分析。在这个阶段，对特定地点的管理区域进行战略性划定引起了特别的兴趣，因为它能够通过有效利用资源投入来进行特定地点的处理以提高作物产量。特定地点管理区的划定问题是确定覆盖整个田地的最小区域数，以便根据特定的生物、化学或物理土壤特性，每个区域的同质性都很重要。此外，划定的区域应为正交形状，以便于农业机械使用。这项工作提出了一种新的仿生算法，特别是分布估计算法，该算法基于严重依赖 Disjoint-Set 算法的解码器和检测不可行的解的新反应惩罚适应度函数。新方法通过使用新的搜索引擎改进了文献中提出的解决方案，该引擎大大缩短了类似算法的计算时间。我们的算法已经用文献基准进行了测试，考虑了适应度函数中一种新的反应式惩罚。与最佳文献方法相比，它为 66.66% 的实例基准获得了最佳解决方案。由于算法的效率，引入了一组新的较大实例来测试该方法的可扩展性和稳健性。它获得了 79.3% 的效率。