KAUST researchers are undertaking a project to improve global date palm production and protection by studying the date palm genome, collecting samples from ancient palms near Madinah. They aim to develop new breeding strategies for faster, healthier, and more pest-resistant palms. The research involves advanced genome sequencing and the creation of molecular tools to improve date palm agriculture, including rapid sex determination methods and gene editing. Why it matters: This research is critical for enhancing date production in arid regions like Saudi Arabia, which is a major global producer, and for ensuring food security amidst climate challenges.
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.
NYU Professor Michael Purugganan presented at KAUST's 2014 Winter Enrichment Program (WEP 2014) on the origins of crop species. He discussed how genome sequencing is improving our understanding of crop evolution, using date fruits collected in Jeddah as an example. His research on rice showed that two varieties, japonica and indica, share a single common ancestor, contrary to previous assumptions. Why it matters: Understanding crop evolution can help adapt crops to changing environments, which is crucial for food security in regions like the Middle East.