  X-ray micro-computed tomography (X-ray microCT) 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 microCT
images, using low-cost resources in Googles 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.
