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This study evaluates the instance‑segmentation performance of YOLOv11 and YOLOv8 on immature green apples in orchard settings. YOLO11n‑seg achieved the highest mask precision (0.831), while YOLO11m‑seg and YOLO11l‑seg excelled in non‑occluded and occluded fruitlet segmentation. YOLOv8n, however, outperformed the YOLO11 series in inference speed, reaching 3.3 ms compared to 4.8 ms for the fastest YOLO11 model.
The paper introduces the Selective Spatio-Temporal Fruit Inspector (S$^2$FI), a low‑cost, high‑efficiency fruit sorting system that uses a 3D convolutional encoder and a Mamba‑based backbone to extract spatiotemporal features and adaptively weight informative frames during fruit rotation. The hardware design features a dual‑conveyor and curved rail system powered by household electricity, enabling fast deployment and lightweight embedded operation. Experiments on a newly built dataset of 6,460 samples demonstrate that S$^2$FI achieves the best balance of accuracy, speed, and practicality compared to baseline methods.
Albireo is an adaptive, energy‑efficient inference framework for video object detection on edge devices that wraps existing detectors without modification. It uses a 10‑dimensional Kalman filter per active object to decide when to skip detector calls, predicting bounding boxes on skipped frames at near‑zero GPU cost. Evaluated on BDD100K with YOLO and RF‑DETR detectors on NVIDIA Jetson AGX Thor and Orin, Albireo maintains AP@50 within ±1.2 pp of full‑frame inference while reducing energy consumption by 12.1–17.6 % and improving accuracy for some models.
The paper introduces a lightweight multimodal vision‑language framework based on TinyCLIP for fine‑grained classification of early‑stage apple fruitlet anatomy (calyx, fruitlet body, peduncle) in orchard images. Using a dataset of 600 high‑resolution RGB images, the model employs domain‑specific language prompts and a sliding‑window inference strategy to produce interpretable heatmaps for whole‑image localization. Achieving macro‑F1 of 0.93 on an NVIDIA T4 GPU and maintaining accuracy after INT8 quantization, the system is optimized for edge deployment on NVIDIA Jetson hardware with model sizes around 127‑137 MB and millisecond‑level inference.
arXiv:2608.23636v1 Announce Type: new Abstract: Small-object detection and instance segmentation remain challenging in orchard environments because of green-on-green similarity, occlusion, and limite...
RootDown presents an end‑to‑end image‑based pipeline for detecting partially occluded sugar beets on loader conveyors, a key step toward automated quality inspection. The authors introduce a dedicated benchmark and an automatic annotation strategy that uses the SAM 3 foundation model to generate bounding boxes, comparing these labels with manually annotated ones. They also evaluate deployment on edge hardware, showing that a Raspberry Pi 5 with a Hailo accelerator can perform high‑performance, low‑power inference suitable for mobile outdoor use.