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Automatic Soil Sampling Site Selection in Management Zones Using a Multi-Objective Optimization Algorithm

Author

Listed:
  • Meysam Kazemi

    (Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada)

  • Faramarz F. Samavati

    (Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada)

Abstract

Precision agriculture hinges on accurate soil condition data obtained through soil testing across the field, which is a foundational step for subsequent processes. Soil testing is expensive, and reducing the number of samples is an important task. One viable approach is to divide the farm fields into homogenous management zones that require only one soil sample. As a result, these sample points must be the best representatives of the management zones and satisfy some other geospatial conditions, such as accessibility and being away from field borders and other test points. In this paper, we introduce an algorithmic method as a framework for automatically determining locations for test points using a constrained multi-objective optimization model. Our implementation of the proposed algorithmic framework is significantly faster than the conventional GIS process. While the conventional process typically takes several days with the involvement of GIS technicians, our framework takes only 14 s for a 200-hectare field to find optimal benchmark sites. To demonstrate our framework, we use time-varying Sentinel-2 satellite imagery to delineate management zones and a digital elevation model (DEM) to avoid steep regions. We define the objectives for a representative area of a management zone. Then, our algorithm optimizes the objectives using a scalarization method while avoiding constraints. We assess our method by testing it on five fields and showing that it generates optimal results. This method is fast, repeatable, and extendable.

Suggested Citation

  • Meysam Kazemi & Faramarz F. Samavati, 2023. "Automatic Soil Sampling Site Selection in Management Zones Using a Multi-Objective Optimization Algorithm," Agriculture, MDPI, vol. 13(10), pages 1-25, October.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:10:p:1993-:d:1259470
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    References listed on IDEAS

    as
    1. Schimmelpfennig, David, 2016. "Farm Profits and Adoption of Precision Agriculture," Economic Research Report 249773, United States Department of Agriculture, Economic Research Service.
    2. Harou, Aurélie P. & Madajewicz, Malgosia & Michelson, Hope & Palm, Cheryl A. & Amuri, Nyambilila & Magomba, Christopher & Semoka, Johnson M. & Tschirhart, Kevin & Weil, Ray, 2022. "The joint effects of information and financing constraints on technology adoption: Evidence from a field experiment in rural Tanzania," Journal of Development Economics, Elsevier, vol. 155(C).
    3. Piotr Mazur & Dariusz Gozdowski & Elżbieta Wójcik-Gront, 2022. "Soil Electrical Conductivity and Satellite-Derived Vegetation Indices for Evaluation of Phosphorus, Potassium and Magnesium Content, pH, and Delineation of Within-Field Management Zones," Agriculture, MDPI, vol. 12(6), pages 1-13, June.
    4. Milics, Gabor & Kauser, Jakab & Kovacs, Attila, 2019. "Profit maximization in soybean (Glycine max (L.) Merr.) using variable rate technology (VRT) in the Sárrét Region, Hungary," Agri-Tech Economics Papers 296767, Harper Adams University, Land, Farm & Agribusiness Management Department.
    5. Milics, Gabor & Kauser, Jakab & Kovacs, Attila, 2019. "Profit maximization in soybean (Glycine max (L.) Merr.) using variable rate technology (VRT) in the Sárrét Region, Hungary," Land, Farm & Agribusiness Management Department 296767, Harper Adams University, Land, Farm & Agribusiness Management Department.
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