采用生物启发的优化算法,利用互不关联的集合划定正交的特定地点管理区

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-12-19 DOI:10.1007/s11119-024-10196-z
Salvador J. Vicencio-Medina, Yasmin A. Rios-Solis, Nestor M. Cid-Garcia
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引用次数: 0

摘要

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,因为它允许土壤测试,然后进行数据分析。在这一阶段,对特定地点管理区的战略性划定获得了特别的兴趣,因为它使特定地点的处理能够通过有效地利用资源投入来提高作物产量。特定场地管理区域的划定问题是确定覆盖整个场地的最小区域数量,以便根据特定的生物,化学或物理土壤性质,每个区域的同质性是重要的。此外,划定的区域应该是正交的,以方便农业机械的使用。这项工作提出了一种新的生物启发算法,特别是分布估计算法,该算法基于严重依赖于Disjoint-Set算法的解码器和检测不可行解的新的反应性惩罚适应度函数。新方法通过使用一种新的搜索引擎,大大减少了类似算法的计算时间,从而改进了文献中提出的解决方案。我们的算法已经用文献基准进行了测试,在适应度函数中考虑了新的反应性惩罚。与最佳文献方法相比,该方法在66.66%的实例基准测试中获得了最佳解决方案。由于算法的有效性,引入了一组新的更大的实例来测试该方法的可扩展性和鲁棒性。其效率为79.3%。
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A bio-inspired optimization algorithm with disjoint sets to delineate orthogonal site-specific management zones

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%.

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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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