arXiv:2606. 26121v1 Announce Type: cross Abstract: Global insect population declines necessitate scalable, continuous monitoring systems, yet existing vision-based solutions remain constrained by high hardware costs, energy demands, and reliance on centralized processing or cloud connectivity.
arXiv:2607. 12065v1 Announce Type: cross Abstract: While visual navigation has been extensively studied in agricultural robotics, most existing systems assume daytime conditions.
RoMu4o is a ground robot equipped with a 6‑DOF arm and a vision system that performs real‑time deep‑learning image processing and motion planning for proximal hyperspectral leaf sensing in orchards. The system uses robust perception and manipulation pipelines to identify leaf 3D structure, propose 6‑D poses, and generate collision‑free, constraint‑aware paths for precise leaf grasping and spectroscopy. In lab trials the robot achieved a 95 % success rate for 1‑LPB hyperspectral sampling, while field trials in a pistachio orchard reached 70 % success for autonomous leaf grasping and measurement. whyItMatters":"The system demonstrates a viable robotic solution to automate leaf‑level hyperspectral sensing, addressing labor shortages and enabling precise crop health monitoring in precision agriculture."
arXiv:2505.18930v2 Announce Type: replace-cross Abstract: Early weed identification is crucial for effective management and control, and researchers, agronomists, and technology developers are increa...
arXiv:2506. 03168v2 Announce Type: replace-cross Abstract: Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing.
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.
arXiv:2608. 11053v1 Announce Type: cross Abstract: The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming.
arXiv:2606. 08249v1 Announce Type: cross Abstract: Reliable wildlife monitoring is essential for ecology and conservation, yet many existing methods, such as tagging, capture, and close-range observation, can alter the very behaviors they aim to measure.
AgroBench is a reproducible benchmark that converts U.S. county-level crop yield statistics into weakly supervised pixel‑level crop time series. The data generation pipeline fuses USDA yield data with land cover masks, Sentinel‑2 and Sentinel‑1 imagery, climatic variables, and terrain information to produce multimodal sequences for individual crop pixels across the growing season. The benchmark includes over 13 million observations from 788,654 crop pixels, covering 5,107 county‑year combinations for five major U.S. crops from 2017 to 2024, and establishes a Leave‑One‑Year‑Out evaluation protocol with baseline machine learning results.
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: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-...
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.