Collaborating Authors

crop loss

Combining Artificial Intelligence With Urban Farming Can Be A Game Changer for Developing Countries


An Israeli agtech company called Seedo might have the solution for the challenges of urban agriculture in vulnerable areas such as the Caribbean, that struggle with environmental and climate factors that lead to crop loss. Latin and America and the Caribbean is the most urbanised region in the world with up to 80% of the region's population residing in cities (UN-Habitat 2012). While urbanization is an important element of economic growth and modernization, the diminishing ratio of food producers to food consumers in urban settings negatively impacts local food systems, causing populations to be more susceptible to non-communicable diseases, obesity and undernourishment. Urban farming practices such as rooftop gardens, community greenhouses and vertical farms have provided an alternative to rural agriculture, but given the high cost of urban land, space and size limitations, non-conducive environmental conditions and limited human resources, these methods have not been without their challenges. Vertical farming's "closed and controlled" approach has been successful in eliminating the risk of insects, pests and diseases that are prevalent in traditional agricultural systems but the infrastructure required has typically been cost-prohibitive and highly reliant on fossil fuels (solar power is typically not enough).

Predicting Crop Losses using Machine Learning CGIAR Platform for Big Data in Agriculture


Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method--combining remotely sensed data with agricultural survey data--that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid.