Early Detection of Plant Virus Infection Using Multispectral Imaging and Machine Learning

Abstract

Abstract Climate change-resilient crops like cassava are projected to play a key role in 21st-century food security. However, cassava production in East Africa is limited by RNA viruses that cause cassava brown streak disease (CBSD). CBSD typically causes subtle or no symptoms on stems and leaves, while destroying the root tissue, which means farmers are often unaware their fields are 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. We will use an engineering advancement, our active multispectral imager (MSI), to rapidly determine the infection status of plants in the field in Tanzania. The MSI observes leaves using many different wavelengths, and the resulting light spectra are 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. Our multinational team is studying and modeling the spread of CBSD to assess the efficacy of using the MSI to detect and remove infected cassava plants from fields before CBSD can spread. In addition to improving the food security of people who eat cassava in sub-Saharan Africa, our technology and modeling framework may be useful in diseases of other vegetatively propagated crops such as banana/plantain, potato, sweet potato, and yam. Information © The Authors 2024