Abstract
Precise localization and recognition of flowers are crucial for advancing automated agriculture, particularly in plant phenotyping, crop estimation, and yield monitoring. This paper benchmarks several YOLO architectures such as YOLOv5s, YOLOv8n/s/m, and YOLOv12n for flower object detection under two annotation regimes: single-image single-bounding box (SISBB) and single-image multiple-bounding box (SIMBB). The FloralSix dataset, comprising 2,816 high-resolution photos of six different flower species, is also introduced. It is annotated for both dense (clustered) and sparse (isolated) scenarios. The models were evaluated using Precision, Recall, and Mean Average Precision (mAP) at IoU thresholds of 0.5 (mAP@0.5) and 0.5-0.95 (mAP@0.5:0.95). In SISBB, YOLOv8m (SGD) achieved the best results with Precision 0.956, Recall 0.951, mAP@0.5 0.978, and mAP@0.5:0.95 0.865, illustrating strong accuracy in detecting isolated flowers. With mAP@0.5 0.934 and mAP@0.5:0.95 0.752, YOLOv12n (SGD) outperformed the more complicated SIMBB scenario, proving robustness in dense, multi-object detection. Results show how annotation density, IoU thresholds, and model size interact: recall-optimized models perform better in crowded environments, whereas precision-oriented models perform best in sparse scenarios. In both cases, the Stochastic Gradient Descent (SGD) optimizer consistently performed better than alternatives. These density-sensitive sensors are helpful for non-destructive crop analysis, growth tracking, robotic pollination, and stress evaluation.
Abstract (translated)
精确的花朵定位和识别对于推动自动化农业至关重要，尤其是在植物表型分析、作物估算以及产量监测方面。本文评估了多个YOLO架构（如YOLOv5s、YOLOv8n/s/m 和 YOLOv12n），它们在两种标注体系下用于花物体检测：单图单一边界框（SISBB）和单图多边界框（SIMBB）。文中还介绍了FloralSix数据集，该数据集包含6种不同花卉物种的2,816张高分辨率照片，并且这些图片既标有密集（簇状）场景也标有稀疏（孤立）场景。使用精度、召回率以及在IoU阈值为0.5 (mAP@0.5) 和 0.5-0.95 (mAP@0.5:0.95) 下的平均精度均值(mAP)对模型进行评估。 在SISBB中，YOLOv8m（SGD）取得了最佳结果：精确度为0.956，召回率为0.951，mAP@0.5为0.978，mAP@0.5:0.95为0.865。这表明在检测孤立花朵时具有很强的准确性。而在SIMBB中，YOLOv12n（SGD）以mAP@0.5 0.934 和 mAP@0.5:0.95 0.752 的成绩超过了更为复杂的场景，在密集、多目标检测方面表现出了强大的鲁棒性。结果表明了标注密度、IoU阈值和模型大小之间的相互作用：优化召回率的模型在拥挤环境中表现出色，而侧重于精度的模型则在稀疏场景中表现最佳。两种情况下，随机梯度下降（SGD）优化器的表现均优于其他选择。 这些灵敏度不同的传感器对于非破坏性作物分析、生长监测、机器人授粉以及压力评估等非常有用。
URL
https://arxiv.org/abs/2602.18585
