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

Abstract

Highlights: What are the main findings? YOLOX demonstrated superior throughput (107 vs. 45 FPS, p < 0.01) and energy efficiency (0.58 vs. 0.75 J/frame) on the Jetson AGX Orin framework, meeting real-time agricultural automation requirements. Despite its lower overall speed, YOLOv12 exhibited significantly better recall for underrepresented soil clods (0.725 vs. 0.512) and improved detection of small objects (0–3000 pixels). What is the implication of the main finding? This study provides empirically grounded guidelines for selecting object detection models in agricultural automation: YOLOX for high-speed processing, prioritizing overall throughput and efficiency, and YOLOv12 for applications that require high accuracy on challenging, underrepresented classes where speed is less critical. The findings underscore that for edge computing applications, the theoretical architectural advantages of a model and its specific implementation efficiency and resource utilization patterns are critical determinants of real-world performance. 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, p < 0.01) and superior energy efficiency (0.58 vs. 0.75 J/frame) than YOLOv12, meeting real-time processing requirements for agricultural automation. Although both models achieved an equivalent overall detection accuracy (F1-score, 0.97), YOLOv12 demonstrated specialized capabilities for challenging classes, achieving 42% higher recall for underrepresented soil clod objects (0.725 vs. 0.512, p < 0.01) and superior precision for small objects (0–3000 pixels). Architectural analysis identified a YOLOv12 residual efficient layer aggregation network backbone and area attention mechanism as key enablers of balanced precision–recall characteristics, which were particularly valuable for addressing agricultural data imbalance. However, NVIDIA Nsight profiling revealed implementation inefficiencies in the YOLOv12 multiprocess architecture, which prevented the theoretical advantages from being fully realized in edge computing environments. These findings provide empirically grounded guidelines for model selection in agricultural automation systems, highlighting the critical interplay between architectural design, implementation efficiency, and application-specific requirements.