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 have developed a hybrid cooling technology combining nanotech plastic and biodegradable mulch that significantly enhances crop yields in arid regions. The technology lowers greenhouse temperatures by 25 degrees Celsius and doubles crop yields in tests with Chinese cabbage. The nanotech plastic coating absorbs infrared light, while the biodegradable mulch reflects sunlight to keep the soil cooler. Why it matters: This innovation promises to improve food security in arid regions like Saudi Arabia while reducing energy consumption and plastic waste associated with traditional greenhouse cooling methods.
Dr. John Bedbrook of DiCE Molecules LLC spoke at KAUST about the challenges of feeding a growing population with increasingly stressed arable land. He noted the increasing demand for meat in emerging economies exacerbates the problem. Bedbrook emphasized the role of genetics and hybridization in improving crop yields and quality to address food security. Why it matters: Investments in agricultural biotechnology are crucial for the GCC region to enhance food security and reduce reliance on imports amid changing climate conditions.
KAUST's Center of Excellence for Sustainable Food Security (CoE-SFS) has launched 12 translation projects focused on plant growth and water security, establishing partnerships with public and private entities to scale up research. Mark Tester's team developed stress-tolerant rootstocks, grafted onto crops like tomatoes, that thrive in hot, dry conditions with increased yields. Through his start-up Iyris, Tester is conducting commercial field trials in over 12 countries. Why it matters: These efforts to adapt agriculture to environmental change are crucial for ensuring food security in Saudi Arabia, the region, and globally, especially in the face of climate change and limited water resources.
MBZUAI students Mugariya Farooq and Sarah Al Barri created a machine learning framework that classifies plant diseases from images and predicts yield using data inputs. Their project won second place at the Agritech Hackathon organized by the Abu Dhabi Agriculture and Food Security Authority (ADAFSA). The algorithm boasts accuracy above 99% when tested against agricultural scientists. Why it matters: This work showcases AI's potential to revolutionize agriculture in the UAE and the broader MENA region by improving food security, reducing waste, and optimizing resource allocation.
Researchers introduce TomFormer, a transformer-based model for accurate and early detection of tomato leaf diseases, with the goal of deployment on the Hello Stretch robot for real-time diagnosis. TomFormer combines a visual transformer and CNN, achieving state-of-the-art results on KUTomaDATA, PlantDoc, and PlantVillage datasets. KUTomaDATA was collected from a greenhouse in Abu Dhabi, UAE.
KAUST's Salt Lab, led by Professor Mark Tester, is researching how salt-tolerant plants survive in harsh environments. The lab aims to improve plant yields in suboptimal conditions, focusing on naturally occurring variability in plants to enhance salinity tolerance. With 70% of global water used for agriculture and increasing water scarcity, the research seeks to unlock the potential of seawater for irrigation. Why it matters: Enhancing the salinity tolerance of crops is crucial for addressing food security challenges exacerbated by climate change and the growing global population, particularly in arid regions like the Middle East.
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.