; Théroux-Rancourt et al., 2020 ). This will allow researchers to gain novel insights into the role that 3d architecture of soil and plant samples plays in a variety of important processes. Data availability statement The datasets presented in this study can be found on the National Agricultural Library Ag Data Commons website https://doi.org/10.15482/USDA.ADC/1524793 . Author contributions AM, DR, JE, PR, EF, and DP contributed to the conception and design of the study. MM, FD, and KS annotated images. PR, DR, JE, JN, and AB wrote code for image segmentation and data extraction. DR wrote the first draft of the manuscript. MM helped write the “Materials and Methods” section of the manuscript
Open resource ↗National Agricultural Library Ag Data Commons · 10.15482/USDA.ADC/1524793 · lines:106-157Unverified paper record
A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory
Frontiers in Plant Science · 13 Sept 2022 · 10.3389/fpls.2022.893140
Abstract
X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google's Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.
Plant phenotyping relevance
植物試料のX線μCT画像を対象に、CNNによるセグメンテーション workflow を開発・最適化しており、植物画像からの表現型情報抽出法が中心である。
abstractwe developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google's Colaboratory web application.
abstractHere we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate.
Code and data availability
The paper's X-ray μCT training/annotation datasets (walnut leaf, almond flower bud, soil aggregate scans and annotations) are publicly deposited on USDA Ag Data Commons. The workflow code is stated to be on GitHub (Rippner et al., 2022b), but no authors' public URL for it appears in the supplied text or allowed URLs,so
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