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Machine Vision and Applications

, Volume 27, Issue 5, pp 735–749 | Cite as

Multi-modality imagery database for plant phenotyping

  • Jeffrey A. Cruz
  • Xi Yin
  • Xiaoming Liu
  • Saif M. Imran
  • Daniel D. Morris
  • David M. Kramer
  • Jin Chen
Special Issue Paper

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.

Keywords

Plant phenotyping Computer vision Plant image Leaf segmentation Leaf tracking Multiple sensors Arabidopsis Bean 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jeffrey A. Cruz
    • 1
  • Xi Yin
    • 2
  • Xiaoming Liu
    • 2
  • Saif M. Imran
    • 3
  • Daniel D. Morris
    • 3
  • David M. Kramer
    • 1
  • Jin Chen
    • 1
  1. 1.Department of Energy Plant Research LaboratoryMichigan State UniversityEast LansingUSA
  2. 2.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA
  3. 3.Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingUSA

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