A root auto tracing and analysis (ARATA): An automatic analysis software for detecting fine roots in images from flatbed optical scanners

Abstract

Abstract Buried scanners are often used to study fine root dynamics by continuously observing them from the images taken at a fixed point. Accordingly, software have been developed to support operators to quantitatively analyse fine roots from scanned images. However, image processing is still time‐consuming work. Deep learning has achieved impressive results as a method for recognising objects in pixel units. In this study, we attempted to automate the image analysis of fine roots using convolutional neural network. Using a root auto tracing and analysis (ARATA), we succeeded in extracting fine roots from scanned images and calculated projected area of fine roots for long‐term dynamics. Our software enables the automatic processing of scanned images acquired at various study sites and accelerates the study of fine root dynamics over extended time periods.