  In this research, an integrated detection model, Swin-transformer-YOLOv5 or
Swin-T-YOLOv5, was proposed for real-time wine grape bunch detection to inherit
the advantages from both YOLOv5 and Swin-transformer. The research was
conducted on two different grape varieties of Chardonnay (always white berry
skin) and Merlot (white or white-red mix berry skin when immature; red when
matured) from July to September in 2019. To verify the superiority of
Swin-T-YOLOv5, its performance was compared against several commonly
used/competitive object detectors, including Faster R-CNN, YOLOv3, YOLOv4, and
YOLOv5. All models were assessed under different test conditions, including two
different weather conditions (sunny and cloudy), two different berry maturity
stages (immature and mature), and three different sunlight
directions/intensities (morning, noon, and afternoon) for a comprehensive
comparison. Additionally, the predicted number of grape bunches by
Swin-T-YOLOv5 was further compared with ground truth values, including both
in-field manual counting and manual labeling during the annotation process.
Results showed that the proposed Swin-T-YOLOv5 outperformed all other studied
models for grape bunch detection, with up to 97% of mean Average Precision
(mAP) and 0.89 of F1-score when the weather was cloudy. This mAP was
approximately 44%, 18%, 14%, and 4% greater than Faster R-CNN, YOLOv3, YOLOv4,
and YOLOv5, respectively. Swin-T-YOLOv5 achieved its lowest mAP (90%) and
F1-score (0.82) when detecting immature berries, where the mAP was
approximately 40%, 5%, 3%, and 1% greater than the same. Furthermore,
Swin-T-YOLOv5 performed better on Chardonnay variety with achieved up to 0.91
of R2 and 2.36 root mean square error (RMSE) when comparing the predictions
with ground truth. However, it underperformed on Merlot variety with achieved
only up to 0.70 of R2 and 3.30 of RMSE.
