US-UK collab: resurrecting a role for roguing: presymptomatic detection with multispectral imaging to quantify and control the transmission of cassava brown streak disease

This $2.18 million Project Grant from the National Science Foundation's Biological Sciences program (CFDA 47.074) will fund research at Rutgers University from September 1, 2023 through August 31, 2027 to test the use of multispectral imaging technology for detecting cassava brown streak disease. The imaging technology will be deployed in agricultural fields in Tanzania to rapidly determine cassava plant infection status. Rutgers University will receive funding to assess the technology's performance in detecting the disease in field settings and comparing spectral data to other plant stresses. The project will measure disease spread in experimental fields and model spread to evaluate removing infected plants before transmission. Rutgers University will determine if the imaging approach can improve clean seed systems for controlling disease spread in East Africa. North Carolina State University will serve as a sub-awardee to contribute expertise to the collaboration between the United States and United Kingdom researchers.

Abstract

This project will test the practical usability of newly-developed multispectral imager (MSI) technology to rapidly determine the infection status of cassava plants in agricultural fields in tanzania. Climate change-resilient crops like cassava are projected to play a key role in 21ST century food security. However, cassava production in east africa IT is limited by RNA viruses that cause cassava brown streak disease (CBSD). CBSD causes subtle or no symptoms on stems and leaves, while destroying the root tissue, which means farmers are often unaware their field is infected until they have a failed harvest. The subtle symptoms of CBSD have made IT difficult to study the spread of the disease in fields. This project will use the MSI to accurately model the local spread of CBSD for the first time. In addition to improving the food security of people who eat cassava in sub-saharan africa, this technology and modeling framework may be useful for diseases of other vegetatively propagated crops such as potato, sweet potato, taro, and YAM. In addition, the multinational team will train undergraduates, graduate students, and post-doctoral scholars to tackle problems in globally-connected agriculture research. The MSI works by observing leaves using many different wavelengths; the resulting light spectra are then interpreted by machine learning models trained on cassava leaf scans. Under laboratory conditions, the MSI detects CBSD infection with 95% accuracy at 28 days post infection, when plants have no visible symptoms. This project will assess and improve the performance of the MSI to detect CBSD infection in the field and to compare those spectral to ones caused by other plant stresses. The project will measure the spread of CBSD in experimental fields, and model its spread to evaluate the efficacy of using the MSI to detect and remove infected cassava plants from fields before CBSD can spread. The project will determine whether the MSI can improve the success of clean-seed systems in east africa. Those systems aim to distribute disease-free planting material, but have had problems controlling the spread of CBSD. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.