arXiv AI
A Swarm-Coordinated Multi-Robot System for Early Stress Detection in Agricultural Rows Using Multimodal Leaf Sensing
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arXiv Computer Vision
Oct 19
ADA 2025: A Multimodal Dataset for Water-Deficit Stress Detection in Sugar Beets
ADA 2025 is a publicly released multimodal dataset exceeding 500 GB, featuring young sugar beet plants under healthy and water‑deficit conditions. It combines thermal, hyperspectral, RGB, and depth imagery with environmental metadata, relative water content, and leaf thickness as ground‑truth labels. The dataset addresses gaps in current AI/ML crop‑management research by providing diverse modalities, accurate metadata, and frequent data points for early stress detection.
arXiv AI
Sep 1
AGRICAM: A Track-Mounted Crop Pollination Monitoring Robot
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...
arXiv Machine Learning
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AgriDefend: A $205 Offline Edge Device that Detects Crop Stress Before Visible Symptoms via Multimodal AI Fusion
AgriDefend is a $205 consumer‑grade edge device that fuses canopy imagery and soil probe data to detect crop stress before visible symptoms appear. It uses a MobileNetV2 CNN for image analysis, a Random Forest for soil trend features, and a logistic‑regression meta‑learner to produce a five‑class crop‑health prediction, all running on a Raspberry Pi 5 without cloud connectivity. In a greenhouse drought trial with wild radish, the fused model achieved 87 % accuracy and its soil signals predicted wilting about four days earlier than visual signs.
arXiv Computer Vision
Sep 14
RoMu4o: A Robotic Manipulation Unit For Orchard Operations Automating Proximal Hyperspectral Leaf Sensing
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 Computer Vision
Oct 7
A Data-Centric Review of Plant Disease Datasets: Taxonomy, Critical Analysis, Environmental Variability, and Implications for Precision Agriculture
arXiv:2610.07087v1 Announce Type: new Abstract: Despite rapid advances in artificial intelligence, reliable real-world plant disease detection remains a persistent challenge. Visual and deep learning...
arXiv Machine Learning
Jul 28
Farm-LightSeek: An Edge-centric Multimodal Agricultural IoT Data Analytics Framework with Lightweight LLMs
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.