Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy
Resente, Giulia (Ernst Moritz Arndt University Greifswald)
Gillert, Alexander (Fraunhofer-Institut für Graphische Datenverarbeitung IGD)
Trouillier, Mario (Ernst Moritz Arndt University Greifswald)
Anadon-Rosell, Alba (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Peters, Richard L. (Swiss Federal Institute for Forest)
von Arx, Georg (University of Bern)
von Lukas, Uwe (University of Rostock)
Wilmking, Martin (Ernst Moritz Arndt University Greifswald)

Date:	2021
Abstract:	The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most common wood anatomical structures: four conifer species; a diffuse-porous species, black alder (Alnus glutinosa L. ); a diffuse to semi-diffuse-porous species, European beech (Fagus sylvatica L. ); and a ring-porous species, sessile oak (Quercus petraea Liebl. ). The DCNN was created in Python with Pytorch, and relies on a Mask-RCNN architecture. The developed algorithm detects and segments cells, and provides information on the measurement accuracy. To evaluate the performance of this tool we compared our Mask-RCNN outputs with U-Net, a model architecture employed in a similar study, and with ROXAS, a program based on traditional image analysis techniques. First, we evaluated how many target cells were correctly recognized. Next, we assessed the cell measurement accuracy by evaluating the number of pixels that were correctly assigned to each target cell. Overall, the "learning process" defining artificial intelligence plays a key role in overcoming the issues that are usually manually solved in QWA analyses. Mask-RCNN is the model that better detects which are the features characterizing a target cell when these issues occur. In general, U-Net did not attain the other algorithms' performance, while ROXAS performed best for conifers, and Mask-RCNN showed the highest accuracy in detecting target cells and segmenting lumen areas of angiosperms. Our research demonstrates that future software tools for QWA analyses would greatly benefit from using DCNNs, saving time during the analysis phase, and providing a flexible approach that allows model retraining.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Artificial intelligence ; Wood anatomy ; Deep learning ; Lumen area ; F1 score ; ROXAS
Published in:	Frontiers in plant science, Vol. 12 (November 2021) , art. 767400, ISSN 1664-462X

DOI: 10.3389/fpls.2021.767400
PMID: 34804101

14 p, 4.3 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Experimental sciences > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Articles > Research articles
Articles > Published articles