Abstract:

: Object detection is one of the most promising research topics currently, whose application in agriculture, however, can be challenged by the difficulty of annotating complex and crowded scenes. This study presents a brief performance assessment of YOLOv7, the state-of-the-art object detector, in comparison to YOLOv4 for apple flower bud classification using datasets with artificially manipulated image annotation qualities from 100% to 5%. Seven YOLOv7 models were developed and compared to corresponding YOLOv4 models in terms of average precisions (APs) of four apple flower bud growth stages and mean APs (mAPs). Based on the same test dataset, YOLOv7 outperformed YOLOv4 for all growth stages at all training image annotation quality levels. A 0.80 mAP was achieved by YOLOv7 with 100% training image annotation quality, meanwhile a 0.63 mAP was achieved with only 5% training image annotation quality. YOLOv7 improved YOLOv4 APs by 1.52% to 166.48% and mAPs by 3.43% to 53.45%, depending on the apple flower bud growth stage and training image annotation quality. Fewer training instances were required by YOLOv7 than YOLOv4 to achieve the same levels of classification accuracies. The most YOLOv7 AP increase was observed in the training instance number range of roughly 0 to 2000. It was concluded that YOLOv7 is undoubtedly a superior apple flower bud classifier than YOLOv4, especially when training image annotation quality is suboptimal.

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