KAUST researchers are studying the chemical signals in pearl millet that trigger the germination of Striga seeds, a parasitic plant. The research aims to understand the biological compounds involved in Striga infestation. The goal is to induce Striga germination without host plants, reducing Striga seed banks in infested soils. Why it matters: Addressing Striga infestation can improve crop yields and food security, especially in regions relying on pearl millet.
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.
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.
KAUST researchers have discovered the first molecular events that trigger wheat's immunity to stem rust, a devastating fungal disease. The study, published in Science, identifies that tandem kinases are bound together and inactive until a pathogen binds, initiating an immune response that kills the infected cell. This prevents the pathogen from spreading and causing widespread crop damage. Why it matters: Understanding these molecular mechanisms could lead to engineering wheat with stronger and more durable resistance to stem rust and other diseases, safeguarding a crucial food source in the face of climate change and emerging pathogens.
KAUST's Center for Desert Agriculture led Saudi Arabia to observe Fascination of Plants Day (FOPD) for the first time in the GCC. The global event, initiated by the European Plant Science Organization (EPSO), aims to raise awareness about the importance of plants and plant science. KAUST's research focuses on food, water, and the environment, addressing challenges of growing plants in extreme conditions. Why it matters: This highlights KAUST's role in advancing agricultural research and promoting environmental awareness in the region, crucial for addressing food security challenges in arid climates.
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 Center for Desert Agriculture is holding an international conference on November 3-5, 2014, focusing on desert rhizosphere microbes for sustainable agriculture. Researchers aim to understand how plants survive in extreme conditions by studying microbes that help them tolerate heat, drought, and salt. They will explore genetic engineering and natural microbe usage to improve crop performance under heightened stress conditions. Why it matters: This research is critical for adapting agricultural systems to global warming and meeting future food production challenges in arid regions like the Middle East.
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.