FFCR-YOLO: Frequency-Band Fusion and Channel Recalibration for Potato Bud-Eye Detection

Accurate localization of potato bud eyes supports automated seed-tuber cutting, but small target size, low contrast, and cultivar-dependent appearance complicate detection. This study proposes FFCR-YOLO (Frequency Fusion and Channel Recalibration YOLO), a bud-eye detector based on YOLOv8n. A total of 4460 original images from eight cultivars were acquired on a sorting machine, with one image per independent tuber. After partitioning by physical tuber, offline augmentation produced 13,380 derived images, yielding a dataset of 17,840 images. Each original image and its derivatives remained in the same subset. Tuber morphology and estimated bud-eye surface density were analyzed to describe cultivar variation. The model incorporates C2f with Frequency-Band Fusion (C2f-FBF) to combine low- and high-frequency feature components, a Feature-map Transformer Encoder (FTE) to model global context, and Channel-Recalibrated Cross-scale Fusion (CRCF) to fuse features at different resolutions and recalibrate their channel responses. MPDIoU is used for bounding-box regression with corner-distance constraints. On the mixed test set of 446 original and 1338 derived images from 446 independent tubers, FFCR-YOLO achieved 98.4% precision, 92.4% recall, 95.3% F1-score, 97.4% [email protected], and 78.5% [email protected]:0.95. Relative to YOLOv8n, recall and [email protected] increased by 12.3 and 4.4 percentage points, respectively. The model had 4.3 million parameters and a forward-pass time of 3.29 ms/image on an NVIDIA RTX 3090 GPU at a batch size of 1, excluding image preprocessing and NMS postprocessing. In the leave-one-cultivar-out (LOCO) evaluations, training used original images of the other seven cultivars; FFCR-YOLO achieved macro recall of 88.40% and macro [email protected] of 95.33%. These results support FFCR-YOLO as a visual perception component for automated seed-tuber cutting, with improved bud-eye detection across the evaluated cultivars.