Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measurement with thread or caliper is accurate but infeasible at commercial scale.
arXiv:2608. 06404v1 Announce Type: cross Abstract: Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response.
The paper compares Ultralytics YOLO27, YOLO26, YOLO11, and YOLOv8 for detecting and segmenting small fruit parts in orchard settings. It evaluates five model scales across 30 experiments, finding that YOLO11s-960 and YOLO26s-960 achieve the best mask and box mAP scores while maintaining efficient parameter counts. The study also highlights the difficulty of peduncle detection and provides publicly available code and models for reproducibility.
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...
arXiv:2609.13551v1 Announce Type: new Abstract: Static-image benchmarks do not capture the computational and temporal requirements of practical orchard video analytics. This study presents an end-to-...
3D plant phenotyping is notoriously known to be procedure-complicated and of low throughput due to the extensive multi-view imaging, the fragile 3D reconstruction pipeline, and the additional cost from reconstructed geometry to phenotypic extraction. These limitations are further amplified in low-cost data acquisition, where smartphone videos or sparsely sampled multi-view images provide limited view overlap and self-occlusion.
arXiv:2608.30161v1 Announce Type: new Abstract: Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping...
CALIPER is a model‑free RGB‑D framework that performs fine‑grained recognition of visually similar industrial parts by combining support‑based appearance matching with metric size evidence. Each class is onboarded from a single turntable RGB‑D video and a few labeled real images, enabling 3D reconstruction for appearance support and depth‑aligned size profiling. At inference, a YOLOv8n‑seg model localizes parts, a frozen DINOv2 backbone with an episodically trained embedding head matches support, and margin‑conditioned metric fusion selectively uses size evidence for ambiguous cases, achieving high accuracy on 18 parts and robust enrollment of unseen screws without retraining.
The paper presents the first real‑world 6D pose ground‑truth dataset for red‑stage strawberries, collected from 12,040 images at an actual farm using indirect camera pose recovery and 3D bounding‑box annotation. It also introduces a synthetic dataset rendered in NVIDIA Isaac Sim with scene‑level realism and domain randomization. Experiments show that models trained solely on synthetic data do not transfer well to in‑field images, but adding a small amount of real data significantly improves both translation and rotation accuracy across various backbone encoders.
arXiv:2603.27519v4 Announce Type: replace Abstract: Image-based plant phenotyping depends on dense structural understanding of crops, yet pixel-level annotation remains expensive across species, orga...
The paper introduces a geometry‑aware post‑detection framework that resolves overlapping plant instances in RGB UAV imagery by combining object detection with geometric clustering of plant components. It uses component centroids and radial intersection points (RIPs) to determine whether a detected region contains one or two plants, applying K‑means or Gaussian mixture models and density filtering to improve clustering accuracy. Evaluated on eggplant and tomato crops, the method achieved high F1‑scores (0.89 for eggplant, 0.75 for tomato) and demonstrated that geometric reasoning can enhance plant‑level interpretation without requiring additional sensors or retraining.
The paper presents a deep‑learning perception framework for selective robotic cotton harvesting, evaluated on 1,008 field images captured under diverse lighting and weather conditions. Detection models from YOLOv8 to YOLOv13 were benchmarked, with GELAN‑s achieving the best trade‑off between accuracy and speed. For segmentation, YOLOv12‑m‑seg outperformed other models, and a detection‑prompted segmentation approach using GELAN‑s bounding boxes further improved localization for SAM variants. Field trials with a UR5e robot and ZED2i camera confirmed YOLOv12‑m‑seg’s real‑time performance for cotton boll detection, segmentation, and selective picking.