From village to globe: A dynamic real-time map of African fields through PlantVillage
A. Kehs, P. McCloskey, J. Chelal, D. Morr, S. Amakove, B. Plimo, J. Mayieka, G. Ntango, K. Nyongesa, L. Pamba, M. Jeptoo, J. Mugo, M. Tsuma, W. Onyango, D. Hughes
Abstract
A major bottleneck to the application of machine learning tools to satellite data of African farms is the lack of high-quality ground truth data. Here we describe a high throughput method using youth in Kenya that results in a cost-effective method for high-quality data in near real-time. This data is presented to the global community, as a public good, on the day it is collected and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.
02 /Specification & Classification
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