From village to globe: A dynamic real-time map of African fields through PlantVillage

Kehs, A.; McCloskey, P.; Chelal, J.; Morr, D.; Amakove, S.; Plimo, B.; Mayieka, J.; Ntango, G.; Nyongesa, K.; Pamba, L.; Jeptoo, M.; Mugo, J.; Tsuma, M.; Onyango, W.; Hughes, D.

doi:10.1101/2019.12.19.882936

bioRxiv · 10.1101/2019.12.19.882936

From village to globe: A dynamic real-time map of African fields through PlantVillage

Kehs, A.·McCloskey, P.·Chelal, J.·Morr, D.·Amakove, S.·Plimo, B.·Mayieka, J.·Ntango, G.·Nyongesa, K.·Pamba, L.·Jeptoo, M.·Mugo, J.·Tsuma, M.·Onyango, W.·Hughes, D.

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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.

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Kehs, A., McCloskey, P., Chelal, J., Morr, D., Amakove, S., Plimo, B., Mayieka, J., Ntango, G., Nyongesa, K., Pamba, L., Jeptoo, M., Mugo, J., Tsuma, M., Onyango, W., Hughes, D.. 2019-12-20. From village to globe: A dynamic real-time map of African fields through PlantVillage. https://doi.org/10.1101/2019.12.19.882936

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