Journal
METHODS IN ECOLOGY AND EVOLUTION
Volume 13, Issue 11, Pages 2372-2378Publisher
WILEY
DOI: 10.1111/2041-210X.13972
Keywords
convolutional neural network; deep learning; fine root dynamics; image processing; image scanner
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Funding
- Japan Society for the Promotion of Science [16H05791, 20H03030]
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In this study, we attempted to automate the analysis of fine root images using convolutional neural network, and we successfully extracted fine roots using our software. This software enables the automatic processing of scanned images, accelerating the study of fine root dynamics.
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.
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