KAUST has received a $1.5 million grant from the Gates Foundation to research methods for eradicating the Striga hermonthica weed, also known as "witchweed". This parasitic plant devastates crops in sub-Saharan Africa by depleting water and nutrients, with the project led by Dr. Salim Al-Babili focusing on pearl millet. The research will involve collaboration with universities in Burkina Faso, Japan, and the Netherlands to identify biological compounds and low-cost methods for Striga control. Why it matters: Addressing Striga infestations is crucial for enhancing food security and supporting the livelihoods of millions of farmers in Africa and the Middle East who rely on crops like pearl millet.
KAUST researchers have identified a gene, CLAMT1b, in pearl millet that affects its vulnerability to the parasitic weed Striga hermonthica. Pearl millet strains lacking CLAMT1b were found to be resistant to the weed, while those expressing the gene were susceptible. The gene's presence leads to the secretion of strigolactones, promoting interaction with Striga, but its absence does not harm symbiotic relationships with beneficial fungi. Why it matters: This discovery offers new breeding strategies to enhance pearl millet's resistance to parasitic weeds, bolstering food security in arid regions like Saudi Arabia and Africa where the crop is vital.
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.
KAUST postdoctoral fellow Muhammad Jamil won the best poster award at the 15th World Congress on Parasitic Plants for his research on combating the parasitic plant *Striga hermonthica*. His poster outlined his work at KAUST on developing technologies to help farmers in sub-Saharan Africa control this weed, which severely impacts cereal crops. Jamil is part of the Bill & Melinda Gates Foundation-funded project at KAUST focused on controlling *Striga* in pearl millet. Why it matters: This recognition highlights KAUST's contributions to addressing critical food security challenges in Africa through innovative agricultural technologies.
KAUST Associate Professor Salim Al-Babili and his team have been awarded an approximately $5 million grant by the Bill & Melinda Gates Foundation. The grant will support the development of strategies to combat the parasitic purple witchweed (Striga hermonthica), which threatens food security in sub-Saharan Africa. Al-Babili's project will focus on protecting pearl millet production through hormone-based soil cleansing, novel chemistries, and identifying genetic factors for resistance. Why it matters: This grant enables KAUST to contribute significantly to addressing food security challenges in Africa and the Middle East by tackling a pervasive parasitic plant, demonstrating the university's commitment to translating research into real-world impact.
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.
KAUST and the National Center for Wildlife (NCW) are collaborating on research to protect Saudi Arabia's coastal ecosystems and marine economy from invasive species. They are conducting biodiversity surveys along the Red Sea and Arabian Gulf coasts, having surveyed 34 sites and collected over 10,000 samples. So far, 200 species with potential marine invasive traits have been identified, expanding the national knowledge base of marine life. Why it matters: The partnership aims to develop early detection and monitoring systems, fortifying Saudi Arabia's marine biosecurity efforts and supporting its Vision 2030 blue economy goals.
This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.