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Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine Learning.

Kyosuke Yamamoto1,2, Wei Guo3, Seishi Ninomiya4

  • 1Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Midori-cho, Nishi-Tokyo, Tokyo 188-0002, Japan. kyosuke.yamamoto@15.alumni.u-tokyo.ac.jp.

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|July 12, 2016
PubMed
Summary

This study introduces an image processing method to accurately measure tomato seedling internode length. This technique aids in quickly evaluating seedling vigor, crucial for fruit quality and plant productivity.

Keywords:
BoVWsaffinity propagationimage analysisinternode length estimationmachine learningnode detection

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Area of Science:

  • Plant Science
  • Agricultural Technology
  • Computer Vision

Background:

  • Seedling vigor is critical for tomato fruit quality, plant growth, and overall productivity.
  • Environmental stresses significantly impact plant development, often manifesting in internode length variations.

Purpose of the Study:

  • To develop an automated image processing method for estimating tomato seedling internode length.
  • To enhance the evaluation of seedling vigor through accurate and rapid measurements.

Main Methods:

  • A three-step method involving node detection, node order estimation, and internode length estimation.
  • Utilized machine learning for robust node detection across varied imaging conditions.
  • Employed affinity propagation for flexible node order estimation, independent of node count.

Main Results:

  • Achieved 72% recall and 78% precision in node detection across time-series imaging.
  • Incorporated Bag of Visual Words (BoVWs) to minimize false positives from leaf detection.
  • Obtained internode length estimations with a relative error below 15.4%.

Conclusions:

  • The developed image processing method accurately and efficiently assesses tomato seedling vigor.
  • This technology offers a valuable tool for rapid phenotyping in plant science and agriculture.