ArticleSensors (Basel, Switzerland)2025
Real-Time Object Detection for Edge Computing-Based Agricultural Automation: A Case Study Comparing the YOLOX and YOLOv12 Architectures and Their Performance in Potato Harvesting Systems.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Deep Learning-Based Intelligent Sorting of Potato Tubers and Mineral Impurities: System Development and Experimental Evaluation.Foods (Basel, Switzerland) · 2026Article
- Class merging degrades learned representations in potato-harvesting object detection despite operational equivalence.Frontiers in plant science · 2026Article
- Feature enhancement and fusion-optimized defect detection model for Sanhua plums.Frontiers in plant science · 2026Article
- AI-Assisted surgical vision: evaluating YOLOv8 and YOLOv12 for real-time detection in colon cancer surgery.Frontiers in surgery · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
In this paper, we presents a case study involving the implementation experience and a methodological framework through a comprehensive comparative analysis of the YOLOX and YOLOv12 object detection models for agricultural automation systems deployed in the Jetson AGX Orin edge computing platform. We examined the architectural differences between the models and their impact on detection capabilities in data-imbalanced potato-harvesting environments. Both models were trained on identical datasets with images capturing potatoes, soil clods, and stones, and their performances were evaluated through 30 independent trials under controlled conditions. Statistical analysis confirmed that YOLOX achieved a significantly higher throughput (107 vs. 45 FPS,
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