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Multi-modality imagery database for plant phenotyping

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Abstract

Among many applications of machine vision, plant image analysis has recently began to gain more attention due to its potential impact on plant visual phenotyping, particularly in understanding plant growth, assessing the quality/performance of crop plants, and improving crop yield. Despite its importance, the lack of publicly available research databases containing plant imagery has substantially hindered the advancement of plant image analysis. To alleviate this issue, this paper presents a new multi-modality plant imagery database named “MSU-PID,” with two distinct properties. First, MSU-PID is captured using four types of imaging sensors, fluorescence, infrared, RGB color, and depth. Second, the imaging setup and the variety of manual labels allow MSU-PID to be suitable for a diverse set of plant image analysis applications, such as leaf segmentation, leaf counting, leaf alignment, and leaf tracking. We provide detailed information on the plants, imaging sensors, calibration, labeling, and baseline performances of this new database.

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    Authors and Affiliations

    1. Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA

      Jeffrey A. Cruz, David M. Kramer & Jin Chen

    2. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA

      Xi Yin & Xiaoming Liu

    3. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA

      Saif M. Imran & Daniel D. Morris

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    1. Jeffrey A. Cruz
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    2. Xi Yin
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    3. Xiaoming Liu
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    4. Saif M. Imran
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    5. Daniel D. Morris
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    6. David M. Kramer
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    7. Jin Chen
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    Corresponding author

    Correspondence to Xiaoming Liu.

    Additional information

    J. A. Cruz and X. Yin equally contributed.

    This research was supported by Chemical Sciences, Geosciences, and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U.S. Department of Energy (award number DE-FG02-91ER20021), and by Center for Advanced Algal and Plant Phenotyping (CAAPP), Michigan State University.

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    Cruz, J.A., Yin, X., Liu, X. et al. Multi-modality imagery database for plant phenotyping. Machine Vision and Applications 27, 735–749 (2016). https://doi.org/10.1007/s00138-015-0734-6

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    • DOI: https://doi.org/10.1007/s00138-015-0734-6

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