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X-ray micro-computed tomography (X-ray microCT) has enabled thecharacterization of the properties and processes that take place in plants andsoils at the micron scale. Despite the widespread use of this advancedtechnique, major limitations in both hardware and software limit the speed andaccuracy of image processing and data analysis. Recent advances in machinelearning, specifically the application of convolutional neural networks toimage analysis, have enabled rapid and accurate segmentation of image data.Yet, challenges remain in applying convolutional neural networks to theanalysis of environmentally and agriculturally relevant images. Specifically,there is a disconnect between the computer scientists and engineers, who buildthese AI/ML tools, and the potential end users in agricultural research, whomay be unsure of how to apply these tools in their work. Additionally, thecomputing resources required for training and applying deep learning models areunique, more common to computer gaming systems or graphics design work, than totraditional computational systems. To navigate these challenges, we developed amodular workflow for applying convolutional neural networks to X-ray microCTimages, using low-cost resources in Googles Colaboratory web application. Herewe present the results of the workflow, illustrating how parameters can beoptimized to achieve best results using example scans from walnut leaves,almond flower buds, and a soil aggregate. We expect that this framework willaccelerate the adoption and use of emerging deep learning techniques within theplant and soil sciences.

A workflow for segmenting soil and plant X-ray CT images with deep learning in Googles Colaboratory