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.
KAUST and the Marine Technology Society (MTS) have formed the MTS Red Sea Section, the first MTS Section in the Middle East. KAUST hosted the MATE ROV Red Sea Regional competition and RobotoKAUST, sponsored by the MTS Red Sea Section. Saudi Arabia is investing heavily in the Blue Economy, and KAUST is positioning itself at the forefront of marine science and technology for the Red Sea. Why it matters: This partnership and KAUST's activities will foster marine technology development and talent growth in the region, aligning with Saudi Arabia's Blue Economy initiatives.
Researchers introduce MATRIX, a vision-centric agent tuning framework for robust tool-use reasoning in VLMs. The framework includes M-TRACE, a dataset of 28.5K multimodal tasks with 177K verified trajectories, and Pref-X, a set of 11K automatically generated preference pairs. Experiments show MATRIX consistently outperforms open- and closed-source VLMs across three benchmarks.
Artists from Switzerland collaborated with researchers at KAUST's Red Sea Research Center to photograph autonomous reef monitoring structures (ARMS). ARMS are artificial towers that capture small critters colonizing coral reefs, developed to measure marine biodiversity. KAUST has deployed and retrieved over 180 ARMS units since 2013 to study cryptobenthic biodiversity, which represents up to 70% of a reef's biodiversity. Why it matters: This collaboration highlights the innovative approaches being used to study marine ecosystems in the Red Sea and underscores the importance of interdisciplinary collaborations in advancing scientific understanding.
Researchers from MBZUAI have developed MMRINet, a Mamba-based neural network for efficient brain tumor segmentation in MRI scans. The model uses Dual-Path Feature Refinement and Progressive Feature Aggregation to achieve high accuracy with only 2.5M parameters, making it suitable for low-resource clinical environments. MMRINet achieves a Dice score of 0.752 and HD95 of 12.23 on the BraTS-Lighthouse SSA 2025 benchmark.