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Researchers use AI to predict crops in Africa to help address food crisis

A new artificial intelligence (AI) tool could help African countries better track and predict crop rotations and yields, providing a key tool to help mitigate food crisis across the continent. 

“Relying on conventional analytic techniques alone will not deliver the effective decision-making we need to meet these challenges,” Racine Ly, the director of data management for the project, told Science X.

“Since this is data that researchers and decision-makers most importantly will use to make decisions, we needed to make sure that the data is correct, and the predictions are accurate,” he added.

AKADEMIYA2063, a research organization, said the Africa Agriculture Watch (AAgWa) tool will help prioritize and maximize the production of staple foods such as maize, cassava and sorghum. 

The program utilizes several factors to help it create its predictions: The “Normalized Difference Vegetation Index,” which is the ratio between different wavelengths of light needed by the crop and provided in the region; the daytime surface temperature of land; rainfall data; and the supply and routes for underground water. 

HUMANS STUMPED ON DIFFERENCE BETWEEN REAL OR AI-GENERATED STUDIES, STUDY SHOWS

Africa agriculture data

Workers empty harvested wheat grain from a delivery truck at a government-operated mill in Abu Hammad, Egypt, on Friday, April 28, 2023. (Islam Safwat/Bloomberg via Getty Images)

The program also uses historical data to generate a crop map to determine where they have grown and will likely grow again as well as the general crop calendar.

Farming artificial intelligence

An aerial shot of farmworkers using a tractor to plant rye seeds at Merrydale Farm outside Frankfort, South Africa, on May 10, 2023. (Shiraaz Mohamed/AFP via Getty Images)

“The many weaknesses that hamper access to good quality agricultural statistics can also be overcome using the same digital technologies, from measuring arable land, planted areas, crop yields to the spatial distribution of harvested quantities,” they added. 

The challenge comes from trying to translate these big data tools into information that the average farmer can understand and utilize. 

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