Tracking Growth and Decay of Plant Roots in Minirhizotron Images

Plant roots are difficult to monitor and study since they are hidden belowground. Minirhizotrons offer an in-situ monitoring solution but their widespread adoption is still limited by the capabilities of automatic analysis methods. These capabilities so far consist only of estimating a single number (total root length) per image.We propose a method for a more fine-grained analysis which estimates the root turnover, i.e. the amount of root growth and decay between two minirhizotron images. It consists of a neural network that computes which roots are visible in both images and is trained in an unsupervised manner without additional annotations.Our code is available as a part of an analysis tool with a user interface ready to be used by ecologists.
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