MBZUAI Professor Fakhri Karray and colleagues from the University of Waterloo are using AI to forecast crop yields, focusing on the impact of extreme temperatures on California strawberry yields. The research uses historical climate and agricultural data to predict yields, addressing issues from 2023 when unusual weather caused a $100 million loss to the strawberry industry. Better predictions could benefit consumers, farmers, and the agricultural industry by improving pricing and supply chain management. Why it matters: This research can improve understanding of agricultural system vulnerabilities amid climate change and extreme weather.
KAUST researchers reported the full genome sequencing of einkorn wheat in Nature. A new 'cooling score' metric was created to study heat's impact on solar cell performance. KAUST is also optimizing MXenes for lithium batteries and using biomimetic mineralization for smart agriculture. Why it matters: This research demonstrates KAUST's contributions to diverse fields, including genomics, sustainable energy, and smart agriculture, advancing technological innovation in Saudi Arabia.
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.