MBZUAI has launched the Institute for Agriculture and Artificial Intelligence (IAAI) in collaboration with the UAE Presidential Court and the Gates Foundation. The IAAI will focus on strengthening global food security by providing digital advisory tools to over 43 million smallholder farmers. The institute will develop a new data corpus for agriculture to train AI models and offer localized insights on crops, pests, soils, weather, and markets. Why it matters: This initiative highlights the UAE's commitment to using AI for global good, specifically addressing food security challenges and empowering small-scale farmers through advanced technologies.
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.
KAUST Professor Salim Al-Babili is working to improve crop performance and nutritional value, with a focus on pearl millet. He received a $5 million grant from the Gates Foundation in 2018 to combat the parasitic plant Striga hermonthica, which causes billions in losses annually in Sub-Saharan Africa. His team is developing hormone-based strategies to protect pearl millet from Striga infestation, a project spanning lab research to field trials in Saudi Arabia and Africa. Why it matters: This research addresses critical food security challenges in both Africa and the Middle East by developing practical tools for smallholder farmers, bridging the gap between lab discoveries and real-world applications.
Al-Maha Systems, a startup founded by KAUST students, has developed an IoT system for livestock health tracking. The system uses sensors attached to cows to monitor vital data like heart rate and body temperature, transmitting it to a cloud server. The goal is to detect health problems early and optimize breeding times for dairy farms. Why it matters: This innovation can improve efficiency and productivity in Saudi Arabia's dairy industry by leveraging IoT for animal husbandry.
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.
KAUST researchers are partnering with Saudi farmers and the Ministry of Environment, Water and Agriculture (MEWA) to develop tailored desalination solutions for agriculture. A new KAUST Center of Excellence project aims to integrate controlled environment agriculture (CEA) with desalination of non-conventional water resources for hydroponic farming. The approach focuses on selective ion removal to provide 'clean-enough' water, reducing energy use and costs compared to traditional desalination. Why it matters: This initiative could enable more sustainable and affordable local crop production in Saudi Arabia, potentially shifting the Kingdom from importing to exporting agricultural technologies.
The paper introduces a framework for camel farm monitoring using a combination of automated annotation and fine-tune distillation. The Unified Auto-Annotation framework uses GroundingDINO and SAM to automatically annotate surveillance video data. The Fine-Tune Distillation framework then fine-tunes student models like YOLOv8, transferring knowledge from a larger teacher model, using data from Al-Marmoom Camel Farm in Dubai.
This paper proposes a machine learning method for early detection and classification of date fruit diseases, which are economically important to countries like Saudi Arabia. The method uses a hybrid feature extraction approach combining L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features. Experiments using a dataset of 871 images achieved the highest average accuracy using Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT) classifiers.