Resolution–Accuracy Trade-offs in UAV-Based Semantic Segmentation for Precision Agricultural Imagery

Authors

Mohamed Tawhid Amin
Department of Space Communication, Faculty of Navigation Science & Space Technology, Beni-Suef University, Egypt
Aziza Ibrahim Hussein
Eectrical and Computer Engineering Department, Effat University, Jeddah, Saudi Arabia
Mohamed Mourad Mabrook
Department of Space Communication, Faculty of Navigation Science & Space Technology, Beni-Suef University, Egypt
DOI: https://doi.org/10.52549/ijeei.v14i2.6512
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Published: 30-06-2026 • Issue: Vol. 14 No. 2: June 2026 • Section: Regular paper

Abstract

The paper explores resolution-accuracy trade-offs in UAV-based semantic segmentation for precision agricultural imagery, which is an important challenge in achieving computational efficiency while maintaining satisfactory results for semantic segmentation. It is widely believed that higher resolution imagery leads to more accurate results, but it also raises processing expenses and restricts the ability to deploy in real-time systems with unmanned aerial vehicles (UAV). This study comprehensively analyzes the effect of the spatial resolution (also known as Ground Sampling Distance (GSD)) on segmentation performance on a range of agricultural anomaly types. The agriculture-vision dataset is used in experimental runs of three different GSDs (10 cm, 20 cm and 40 cm per pixel) with UAV-acquired images. The standard semantic segmentation metrics (mean Intersection over Union (mIoU), Dice coefficient, and computational time analysis) are adopted to evaluate several deep learning models such as U-Net, R2U-Net, U-Net3+, Attention U-Net and DeepLabV3+. The results show that there is no clear correlation between achieving higher spatial resolution and achieving better segmentation accuracy. Medium resolution imagery (20 cm/pixel) can produce similar or better results for large scale anomalies, including dryness and nutrient deficiency, and at significantly lower computational costs. On the other hand, fine-grained anomalies require more fine-grained resolution to better display the anomalies, which shows that the best resolution depends on the task. Further, multi-scale feature aggregation models have higher robustness to resolution degradation. The findings offer a practical understanding of designing the resolution-aware model, which could lead to more efficient use of UAV for resolution-aware deployment, flight planning, and the implementation of edge-AI in precision agriculture. The study provides a data-driven tool for optimizing the spatial resolution versus accuracy versus efficiency of real-world monitoring systems in agriculture.
Keywords: Deep learning; Semantic segmentation; Spatial resolution; Precision agriculture; Anomaly detection; Ground Sampling Distance

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Author Biographies

Mohamed Tawhid Amin
He is M.Sc. Student at Institute of Geodesy and Geoinformation, University of Bonn, Germany. Received B.Sc. at Faculty of Navigation Science & Space Technology, Beni-Suef University, Egypt. He is a Teaching Assistant, Department of Space Communication, Faculty of NSST, Beni-Suef University, Egypt from 2024 till now.
Aziza Ibrahim Hussein
received her Ph.D. degree in Electrical & Computer Engineering from Kansas State University, USA in 2001 and the M.Sc. and B.Sc. degrees from Assiut University, Egypt in 1989 and 1983, respectively. She joined Effat University in Saudi Arabia in 2004 and established the first Electrical and Computer Engineering program for women in the country and taught related courses. She was the head of the Electrical and Computer Engineering Department at Effat University from 2007-2010. She was the head of Computer and Systems Engineering Department, Faculty of Engineering, Minia University, Egypt from 2011-2016. She was a professor and chair of the Electrical & Computer Engineering Department and director of the Master of Energy program at Effat University Saudi Arabia from 2016-2021. Currently she is a professor and researcher at the same department. Her research interests include microelectronics, analog/digital VLSI system design, RF circuit design, high-speed analog- to-digital converters design, and wireless communications systems design.
Mohamed Mourad Mabrook
received his B.Sc. and M.Sc. degrees in Electrical and Communication Engineering from Assiut University, Egypt, in 2008 and 2013, respectively. He received Ph.D. degree in communication Dept., Menia University, Egypt in 2017. He is Associate Professor at Space communication Engineering Dept. in faculty of Navigation Science & Space Technology (NSST), Beni-Suef university, Beni-Suef, Egypt. Head of Applied sciences of space and navigation Program, Beni-Suef National University. He is a part time Assistant prof. at faculty of Engineering, Nahda university (NUB), Beni-Suef, Egypt. He Published more than 40 journal and conference papers in the fields of wireless communications, 5G networks, cognitive radio, Artificial intelligence, circuit design and sensors. He is a reviewer in many international journals related to Elsevier, Springer and IEEE Publishers’

How to Cite

[1]
M. T. Amin, A. I. Hussein, and M. M. Mabrook, “Resolution–Accuracy Trade-offs in UAV-Based Semantic Segmentation for Precision Agricultural Imagery”, IJEEI, vol. 14, no. 2, pp. 595–614, Jun. 2026, doi: 10.52549/ijeei.v14i2.6512.