Geospatial AI for Monitoring Food Security and Climate Resilient Agriculture
Amid the rising food insecurity and climate challenges, there is a need for some urgent and crucial, coordinated actions. Some recent reports from the United Nations have highlighted that in 2023, about 25 million people in the west sub-saharan regions are at a high risk of food insecurity, indicating a sharp rise in hunger within those countries. The east sub-saharan regions also face a very similar scenario. However, in trying to understand the drivers of food insecurity, one quickly realizes the sparseness of the available datasets in some of the most vulnerable regions in the world. As a case study, we will be focusing on these two different geographical regions and use geospatial data along with custom pre-trained models (unsupervised and supervised models) to identify seasonal agricultural activity, the type of crops grown and estimate the yields for the crop of interest. Agricultural productivity is significantly influenced by weather and climate variables, including extreme events like droughts, floods, and severe storms. Climate projections for sub-Saharan Africa indicate an impending shift towards drier conditions, underscoring the urgency of adopting climate-smart agriculture, such as planting drought-tolerant crops and intensifying efforts to optimize crop water productivity. We observe the effects of socio-economic or socio-political situations in farmer behavior and decisions. We will also address in this session the effects of local and global conflicts along with the effects of climate change on production and farmer behaviors.
Speaker:
Praveen Pankajakshan, Vice President, Data Science & AI, Cropin