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Highlights: AI enhances aquaculture efficiency, boosting food production sustainably. Precision aquaculture with AI optimizes resource usage for higher yields. Automated monitoring ensures...

Highlights: Determine the prevalence of malnutrition among women in Bangladesh. Identification of the risk factors of malnutrition using logistic regression. Prediction of malnutrition based on machine learning approach...

Background Most of the 10 million Kenyans lacking food security lived in the arid and semi-arid northern part of the country in a climatic condition of high temperatures and very little...

The WFSO database encompasses historical, preliminary, and forecast data concerning severe food insecurity on a global scale. The primary goal of this dataset is to offer more timely and comprehensive...

Motivated by the deterioration in global food security conditions, this paper develops a parsimonious machine learning model to derive a multi-year outlook of global severe food insecurity from macro-economic...

Using machine learning to empower policymakers with a deeper comprehension of citizens' values and aspirations concerning policy matters.

This paper explains the collaboration of the World Food Programme (WFP) and esteemed partners, including the University of Oxford, IGAD Climate Prediction and Applications Centre (ICPAC), Kenya Meteorological...

Highlights: A Food Security Identification (FSI) roadmap is proposed for the development of a multi-dimensional food security index. The roadmap employs a machine learning approach towards identifying regions...

Accurate and economically sound soil fertility recommendations are critical for ensuring profitable food production for smallholder farmers. However, such recommendations are lacking in many...

High crop yield variation between years—caused by extreme shocks on the food production system such as extreme weather—can have substantial effects on food production. This in turn...