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

TL;DR

This paper presents a modular, accessible workflow using deep learning in Google Colaboratory for segmenting X-ray micro-CT images of soil and plants, aiming to improve speed and accuracy in agricultural imaging analysis.

Contribution

It introduces a low-cost, user-friendly workflow for applying convolutional neural networks to complex environmental and agricultural X-ray images, bridging the gap between AI tools and end users.

Findings

Optimized parameters improve segmentation accuracy.

Workflow successfully applied to walnut leaves, almond buds, and soil samples.

Accessible approach facilitates adoption in plant and soil research.

Abstract

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.