2025
1.
Ranjan Sapkota; Manoj Karkee
In: IFAC-PapersOnLine, vol. 59, no. 23, pp. 239–244, 2025, ISSN: 2405-8963.
Abstract | Links | BibTeX | Tags: agricultural automation, Apple detection, LLM, Synthetic Image Generation, YOLOv10, YOLOv11, YOLOv12, YOLOv12 object detection, You Only Look Once
@article{sapkota_improved_2025,
title = {Improved YOLOv12 with LLM-Generated Synthetic Data for Enhanced Apple Detection and Benchmarking Against YOLOv11 and YOLOv10⁎⁎Sponsor and financial support acknowledgment goes here. Paper titles should be written in uppercase and lowercase letters, not all uppercase.},
author = {Ranjan Sapkota and Manoj Karkee},
url = {https://www.sciencedirect.com/science/article/pii/S2405896325024929},
doi = {https://doi.org/10.1016/j.ifacol.2025.11.793},
issn = {2405-8963},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {IFAC-PapersOnLine},
volume = {59},
number = {23},
pages = {239\textendash244},
abstract = {Accurate fruit detection in orchards remains a major bottleneck in agricultural automation due to the high cost and labor demands of data collection and annotation. This study evaluated the performance of the YOLOv12 object detection model, and compared against the performances YOLOv11 and YOLOv10 for apple detection in commercial orchards based on the model training completed entirely on synthetic images generated by Large Language Models (LLMs). The YOLOv12n configuration achieved the highest precision at 0.916, the highest recall at 0.969, and the highest mean Average Precision (mAP@50) at 0.978. In comparison, the YOLOv11 series was led by YOLO11x, which achieved the highest precision at 0.857, recall at 0.85, and mAP@50 at 0.91. For the YOLOv10 series, YOLOv10b and YOLOv10l both achieved the highest precision at 0.85, with YOLOv10n achieving the highest recall at 0.8 and mAP@50 at 0.89. These findings demonstrated that YOLOv12, when trained on realistic LLM-generated datasets surpassed its predecessors in key performance metrics. The technique also offered a cost-effective solution by reducing the need for extensive manual data collection in the agricultural field. In addition, this study compared the computational efficiency of all versions of YOLOv12, v11 and v10, where YOLOv11n reported the lowest inference time at 4.7 ms, compared to YOLOv12n’s 5.6 ms and YOLOv10n’s 5.9 ms. Although YOLOv12 is new and more accurate than YOLOv11, and YOLOv10, YOLO11n still stays the fastest YOLO model among YOLOv10, YOLOv11 and YOLOv12 series of models.},
keywords = {agricultural automation, Apple detection, LLM, Synthetic Image Generation, YOLOv10, YOLOv11, YOLOv12, YOLOv12 object detection, You Only Look Once},
pubstate = {published},
tppubtype = {article}
}
Accurate fruit detection in orchards remains a major bottleneck in agricultural automation due to the high cost and labor demands of data collection and annotation. This study evaluated the performance of the YOLOv12 object detection model, and compared against the performances YOLOv11 and YOLOv10 for apple detection in commercial orchards based on the model training completed entirely on synthetic images generated by Large Language Models (LLMs). The YOLOv12n configuration achieved the highest precision at 0.916, the highest recall at 0.969, and the highest mean Average Precision (mAP@50) at 0.978. In comparison, the YOLOv11 series was led by YOLO11x, which achieved the highest precision at 0.857, recall at 0.85, and mAP@50 at 0.91. For the YOLOv10 series, YOLOv10b and YOLOv10l both achieved the highest precision at 0.85, with YOLOv10n achieving the highest recall at 0.8 and mAP@50 at 0.89. These findings demonstrated that YOLOv12, when trained on realistic LLM-generated datasets surpassed its predecessors in key performance metrics. The technique also offered a cost-effective solution by reducing the need for extensive manual data collection in the agricultural field. In addition, this study compared the computational efficiency of all versions of YOLOv12, v11 and v10, where YOLOv11n reported the lowest inference time at 4.7 ms, compared to YOLOv12n’s 5.6 ms and YOLOv10n’s 5.9 ms. Although YOLOv12 is new and more accurate than YOLOv11, and YOLOv10, YOLO11n still stays the fastest YOLO model among YOLOv10, YOLOv11 and YOLOv12 series of models.
