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Root identification in minirhizotron imagery with multiple instance learning

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Abstract

In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, and thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron imagery. However, labeling roots for training data (or otherwise) is an extremely tedious and time-consuming task. This paper aims to address this problem by labeling data at the image level (rather than the individual root or root pixel level) and train algorithms to perform individual root pixel level segmentation using MIL strategies. Three MIL methods (multiple instance adaptive cosine coherence estimator, multiple instance support vector machine, multiple instance learning with randomized trees) were applied to root detection and compared to non-MIL approaches. The results show that MIL methods improve root segmentation in challenging minirhizotron imagery and reduce the labeling burden. In our results, multiple instance support vector machine outperformed other methods. The multiple instance adaptive cosine coherence estimator algorithm was a close second with an added advantage that it learned an interpretable root signature which identified the traits used to distinguish roots from soil and did not require parameter selection.

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Acknowledgements

This work was supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research Award Number DE-SC0014156 and by the Advanced Research Projects Agency-Energy Award Number DE-AR0000820.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA

    Guohao Yu, Alina Zare & Hudanyun Sheng

  2. Argonne National Laboratory, Lemont, IL, 60439, USA

    Roser Matamala

  3. Division of Plant Sciences, University of Missouri, Columbia, MO, 65211, USA

    Joel Reyes-Cabrera & Felix B. Fritschi

  4. Department of Integrative Biology, University of Texas at Austin, Austin, TX, 78712, USA

    Thomas E. Juenger

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  1. Guohao Yu
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Correspondence to Guohao Yu or Alina Zare.

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Yu, G., Zare, A., Sheng, H. et al. Root identification in minirhizotron imagery with multiple instance learning. Machine Vision and Applications 31, 43 (2020). https://doi.org/10.1007/s00138-020-01088-z

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