Aerial imaging and machine learning to counter banana disease threats in Africa

New research shows how a combination of imagery from mobile phones, drones and satellites can be used to clamp down on threats to banana production. An early warning system can be set up to detect and prevent diseases in banana, a key food security crop in Africa.

The system relies on machine learning and imagery collected by mobile phones, drones and satellites. Collected images -of varying resolutions- are fed into a platform "trained" through machine learning to identify banana crops and analyse threats with 97% overall accuracy. The findings were published in the ISPRS Journal of Photogrammetry and Remote Sensing.

The research case studies, conducted in the Democratic Republic of Congo and Republic of Benin, will have important implications for the 90 million people in East, West and Central Africa who rely on bananas and plantains as a primary food source.

The increasing arrival and spread of serious diseases, fungal infections and viruses, due to climate change and land-use change among other factors, pose a serious food security threat. There are six major and devastating threats to bananas, among them bunchy top disease (BBTD) and Xanthomonas wilt of banana (BXW).

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