New research shows how a combination of imagery from mobile phones, drones and satellites can be used to clamp down on banana threats. The images -of varying resolutions- are fed into a platform that has used machine learning to identify banana crops and analyze threats. 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, 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.
"Threats are currently detected by experts in the field using cell phones," said Michael Gomez Selvaraj, a crop physiologist and co-author at the Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT). "But to track and detect diseases across huge tracts of land at country, district or village level, you need a platform that quickly detects threats."
Information about the severity of the specific threat and its spread can be sent to organizations or government authorities who can take immediate measures to clamp down on them. "Otherwise potential threats multiply quickly, for example, farmers may give infected crop stems to others, and, in the case of a virus, spread it around the country or district without knowing until it's too late," said Selvaraj.
Currently, most disease surveillance systems focus on a single-sensor based solution that cannot monitor larger landscapes through mobile phones or drones. This method combines field-level information captured by farmers or extension workers in the field, with satellite data to detect crop area, and drones deployed to analyze the exact threat and its intensity.