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...
By Rishi Gupta, Astha Goyal, Vinay Vishwakarma
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...
By Malika Nisal Ratnayake, Adel N. Toosi, James Cook, Romina Rader, Alan Dorin
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.
By Ik Jae Lee, Hieu D. Nguyen, Mahbubur Meenar, Carlos Morrison Martinez, Cameron Connelly
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.
By Ayman Laaroussi, Peter Hanappe, David Colliaux