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RootTracer: An intuitive solution for root image annotation
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AI
DOI：10.1016/j.atech.2024.100705.png)
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Abstract
En 中文
Plant phenotyping is essential in agricultural research for identifying resilient traits critical for global food security. Analyzing root growth quantitatively is increasingly vital for evaluating a plant's resilience to abiotic stresses and its efficiency in nutrient and water absorption. However, extracting features from root images poses significant challenges due to the complexity of root structures, variations in size, background noise, occlusions, clutter, and inconsistent lighting conditions. In this study, we introduce RootTracer a software tool that offers a variety of functionalities. RootTracer enables users to quickly and easily create RSML files that capture the structure of a root system by inputting the image to be analyzed and marking or modifying key points within the image. Additionally, it allows for the modification of previously created RSML files (using any state-of-theart creation tool) through an intuitive and user-friendly interface. The program also provides the capability to automatically extract various plant and root measurements from the RSML file. Furthermore, we present a new image dataset, named TILLMore CDC (Compact Disk Case), that includes ground truth annotations manually generated with the support of RootTracer, designed to advance the development of automated root recognition systems. This dataset, which will be publicly released, can be used by researchers to train machine learning models for accurate root image analysis, helping to overcome the challenges posed by complex root structures and varied imaging conditions. By leveraging this dataset, we aim to enhance the accuracy and robustness of root phenotyping methods, thereby contributing to the broader field of plant phenotyping and agricultural research. The RootTracer tool and the TILLMore CDC dataset are available on GitHub.
Keywords:
Plant phenotyping
Root segmentation
Root system architecture
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