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2023
Conference Paper
Titel
Tracking Growth and Decay of Plant Roots in Minirhizotron Images
Abstract
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. https://github.com/alexander-g/Root-Tracking
Author(s)
Project(s)
DigIT!
DigIT!
Funder
Mecklenburg-Vorpommern. Ministerium für Wissenschaft, Kultur, Bundes- und Europaangelegenheiten
Tags
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Branche: Bioeconomics and Infrastructure
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Research Line: Computer vision (CV)
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Research Line: Machine learning (ML)
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LTA: Scalable architectures for massive data sets
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LTA: Machine intelligence, algorithms, and data structures (incl. semantics)
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Environmental monitoring
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Environmental problems
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Biological processes