Feasibility of Translating Cassava Brown Steak Disease & Soybean Rust Pathogen Sensor Platforms to Tanzanian Agriculture

Public summary

The cassava crop is cultivated primarily by smallholder farmers, however its production is affected by two common viral diseases: the cassava mosaic and brown streak disease. These can be transmitted plant to plant, threatening food supplies and causing severe economic losses. Climate change and global trade further contribute to the spread of these viruses. The early containment, isolation and suppression of viral outbreaks is critical if they are to be controlled, given limited ability to mutate and ultimately, destroyed.

Now, with funding from the NSF & BBSRC, portable, low-cost field sensors that use AI to detect viral diseases in cassava crops have been developed by a team of electronic engineers from The University of Manchester, working alongside plant science and crop virology researchers at North Carolina State University in the US, and in partnership with the International Institute for Tropical Agriculture in Tanzania. The handheld sensors collect images of growing cassava plant leaves and quickly and efficiently compare these images to a vast database of diseased and healthy crop images to detect early signs of brown streak disease with advanced machine learning techniques. The human eye can only detect the effects of brown streak disease after three or four months, whereas the sensors can detect the disease within the first two weeks of infection, helping to isolate the disease at its source and reduce the spread. This new technology means crop breeders and agricultural workers can easily identify and eradicate disease outbreaks before they spread, and with further study, scientists could find a solution to prevent repeat cases in the future.

Category of impact	Economic, Technological, Environmental
Impact level	Benefit