A New Dissimilarity Metric for Anomaly Detection in Management Zones Delineation Constructed from Time-Varying Satellite Images

Roghayeh Heidari () and Faramarz F. Samavati
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Roghayeh Heidari: 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

Agriculture, 2024, vol. 14, issue 5, 1-20

Abstract: A field’s historical performance data are used for management zone delineation in precision agriculture, but including abnormal data leads to inappropriate zones. This paper introduces a framework incorporating historical performance data and a new Zoning Dissimilarity Metric ( Z D M ) to detect abnormal zoning data automatically. The methodology identifies abnormal zoning data among the field’s performance indicators extracted from satellite images to enhance the accuracy of the delineated zones. We experimented with our framework using Sentinel-2 images on 39 fields across Canada. Our experimental results, which involve both real and synthetic data, clearly demonstrate the importance of Z D M in effectively excluding abnormal data during the zone delineation process.

Keywords: anomaly detection; precision agriculture; satellite imagery; management zone delineation; historical field performance (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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