Maturity detection and counting of blueberries in real orchards using a 1novel STF-YOLO model integrated with
Na Wu1, Jie Wu1, Zhechen Wang1
1School of Artificial Intelligence and Information Engineering, Zhejiang University of Science and Technology, Hangzhou, China.
Introduction:
Blueberries are highly prized for their nutritional value and economic importance. However, their small size, dense clustering, and brief ripening period make them difficult to harvest efficiently. Manual picking is costly and error-prone, so there is an urgent need for automated, high-precision solutions in real orchards.
Methods:
We proposed an integrated framework that combined the STF-YOLO model with the ByteTrack algorithm to detect blueberry maturity and perform counting. Together with ByteTrack, it provided consistent fruit counts in video streams. STF-YOLO replaced the YOLOv8 C2f block with a Detail Situational Awareness Attention (DSAA) module to enable more precise discrimination of maturity. It also incorporated an Adaptive Edge Fusion (AEF) neck to enhance edge cues under leaf occlusion and a Multi-scale Neck Structure (MNS) to aggregate richer context. Additionally, it adopted a Shared Differential Convolution Head (SDCH) to reduce parameters while preserving accuracy.
Results:
On our orchard dataset, the model achieved an mAP50 of 79.7%, representing a 3.5% improvement over YOLOv8. When combined with ByteTrack, it attained an average counting accuracy of 72.49% across blue, purple, and green maturity classes in video sequences. Cross-dataset tests further confirmed its robustness. On the MegaFruit benchmark (close-range images), STF-YOLO achieved the highest mAP50 for peaches (91.6%), strawberries (70.5%), and blueberries (90.6%). On the heterogeneous PASCAL VOC2007 dataset, it achieved 66.3% mAP50, outperforming all lightweight YOLO variants across 20 everyday object categories.
Discussion:
Overall, these results suggest that the STF-YOLO integrated with the ByteTrack framework can accurately detect and count blueberries in orchards. This lays a solid foundation for the future development of automated blueberry harvesting machinery and improvements in harvest efficiency.
