The paper introduces a concept for a sensor test environment tailored to dusty agricultural conditions, featuring both indoor and outdoor proving grounds. The indoor area includes a laboratory test bench with a closed dust circulation system and a test hall for life‑size objects, while the outdoor area offers dedicated setups that allow reproducible data collection with and without dust during real‑world agricultural tasks. The design and setups are visualized in 3D to aid understanding and implementation.
arXiv:2610.08603v1 Announce Type: cross Abstract: Early stress detection in crops is a necessity today to improve efficiency and reduce waste of time, money, and effort. However, most modern techniqu...
arXiv:2608.29237v1 Announce Type: cross Abstract: Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances i...
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 introduces an autonomous robotic platform that performs targeted chlorophyll fluorescence measurements on plant leaves. It integrates 3D plant reconstruction, geometric analysis, and motion planning to identify suitable leaf surfaces and generate collision‑free trajectories for a robotic manipulator. This system enables automated, repeatable, and spatially resolved physiological measurements that extend beyond passive imaging.
arXiv:2607. 25612v1 Announce Type: new Abstract: Perception tasks for autonomous vehicles need to work satisfactorily in adverse weather conditions.
arXiv:2607. 21881v1 Announce Type: cross Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis.
arXiv:2609.13265v1 Announce Type: cross Abstract: Near-infrared (NIR) imaging does not consistently outperform standard color cameras for daytime agricultural traversability once spatial data leakage...
ALFRED is an open‑source mobile manipulator designed for long‑term plant monitoring, built from commercial parts and featuring a six‑degree‑of‑freedom arm, LiDAR, RGB‑D cameras, RTK GNSS, and an IMU on an Ackermann‑steered base. The platform was iteratively refined over four builds to meet six requirements—durability, modularity, repairability, sensing reach, endurance, and reproducibility—resulting in a 66.1% usable arm reach and clear LiDAR views in the final build. Over a year of monthly forest surveys, ALFRED completed 528 traversals without missing a scheduled collection, demonstrating its reliability despite battery wear and rapid build transitions.
Physical AI Smart Spaces is the first benchmark that offers large‑scale, multi‑class, multi‑camera 3D perception data for indoor smart spaces. It includes over 280 hours of synchronized 1080p footage from nearly 1,800 cameras in warehouses, hospitals, and retail venues, with automatic annotations for identities, 2D and 3D bounding boxes, camera calibration, and depth. The benchmark spans synthetic generation, appearance augmentation, and real‑world Sim2Real evaluation, and introduces a 3D version of Higher Order Tracking Accuracy (HOTA) for evaluating multi‑class 3D box tracking.