Researchers have developed MARTS (Modular Agricultural Robot for Targeted Spraying), a low-cost robotic platform designed for precise disease control in greenhouse tomato production. MARTS integrates a mobile base, a UR5 manipulator, a modular spraying end-effector, and an Intel RealSense RGB-D camera, utilizing a vision-language model for disease detection. Deployed at the SILAL commercial greenhouse in Al Ain, UAE, the robot successfully treated 312 diseased leaves across 12 trials, achieving 92.4% targeting success and a 71% reduction in pesticide volume compared to broadcast spraying. The spraying subsystem costs significantly less than comparable systems, demonstrating the feasibility of accurate leaf-level targeting with commodity hardware. Why it matters: This innovation provides a precise, cost-effective, and environmentally friendly solution for agricultural disease management, reducing pesticide use and worker exposure, with successful application and cost efficiency demonstrated in the UAE.
Researchers developed a force-data-driven robotic harvesting system for cherry tomatoes, integrating six-axis force/torque sensing, a bio-inspired compliant gripper, and a mobile robotic platform. Manual trials informed the design of a novel Finray gripper and a hybrid grip-twist-pull strategy, applying specific forces and torques to detach the fruit gently. Validated at a commercial greenhouse in Abu Dhabi, UAE, the system achieved an 86.0% picking success rate with only 2.3% fruit damage and an 8.4-second cycle time per fruit. Why it matters: This development demonstrates a practical and efficient solution for delicate robotic harvesting in controlled agricultural environments, with direct relevance to food security initiatives in the Middle East.
Red Sea Farms, a KAUST startup, is advancing its saltwater greenhouse technology with a new 21,000 square foot pilot facility at the KAUST Research & Technology Park. Their greenhouse technology allows for growing crops on marginal land, using 90% less freshwater than traditional methods. The system uses saltwater in greenhouse-cooling and climate control, resulting in a lower environmental footprint. Why it matters: This technology addresses critical food and water security challenges in arid regions by enabling local food production with minimal freshwater resources and reduced energy consumption.
YOLO26-RipeLoc Lite is a new lightweight deep learning architecture designed for simultaneous detection, ripeness classification, and center-point localization of greenhouse tomatoes for robotic harvesting. The model incorporates a Lightweight Feature Pyramid Network, a Ripeness-Aware Attention Module, and a Compact Detection Head for efficient and precise operation. Evaluated on a custom dataset from the SILAL greenhouse in Abu Dhabi, UAE, it achieved a [email protected] of 92.9% with only 2.38 million parameters, outperforming existing YOLO models in accuracy-efficiency. Why it matters: This research provides an efficient and accurate solution for automating a critical agricultural process, enhancing food security and technological capabilities in the region's greenhouse farming.