Non-invasive, automated, and continuous 3D plant imaging is important for studying plant development, performing digital phenotyping, and detection of plant diseases. In this study, we reconstructed 3D plant structural and fluorescence plant images using an automated monocular vision-based structure from motion technique requiring only 2 RGB images. By using different exposure durations and RGB spectral filters we are able to acquire both white light structural information and fluorescence functional information in a single acquisition. The combined structural and function information enables us to observe and locate the plant disease of autofluorescing downy mildew lettuce plants in 3D. We demonstrate the effect of important parameters such as exposure duration and sampling frequency on the 3D reconstruction quality. We believe that our work will enable plant biologists and plant breeders to aid in understanding plant-pathogen interactions, plant development, and to utilize this for breeding more disease resistant crops.
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