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Automatic Root Length Estimation from Images Acquired In Situ without Segmentation.

Faina Khoroshevsky1, Kaining Zhou2,3, Sharon Chemweno4,3

  • 1Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.

Plant Phenomics (Washington, D.C.)
|January 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a fast AI method to estimate total root length from minirhizotron images, aiding precision agriculture. The models accurately quantify root growth without manual segmentation, improving crop research efficiency.

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Area of Science:

  • Plant Science
  • Agricultural Technology
  • Computer Vision

Background:

  • Minirhizotron (MR) imaging is crucial for understanding in situ root responses to environmental changes.
  • Manual analysis of MR images is labor-intensive and limits high-throughput phenotyping.
  • Automated root analysis is needed to accelerate research and support precision agriculture.

Purpose of the Study:

  • To adapt and evaluate convolutional neural network (CNN) models for estimating total root length (TRL) directly from minirhizotron images.
  • To compare the performance of regression-based and detection-based CNN models for TRL estimation.
  • To provide a robust and efficient tool for root phenotyping in diverse crop species.

Main Methods:

  • Developed and adapted CNN models for TRL estimation, bypassing image segmentation.
  • Utilized manual annotations from Rootfly software for training data.
  • Trained and validated models on 4,015 images from four crop species (corn, pepper, melon, tomato) using two MR systems.

Main Results:

  • CNN models achieved high accuracy (R² = 0.929–0.986) in estimating TRL compared to manual measurements.
  • The detection-based model aids in visual inspection of root annotations.
  • Models demonstrated robustness across different MR systems, crop species, and image qualities.

Conclusions:

  • The developed CNN-based tool accurately and efficiently estimates TRL from MR images, overcoming limitations of manual analysis.
  • This technology supports precision agriculture by enabling real-time root growth monitoring.
  • Publicly available datasets and models will facilitate further advancements in root phenotyping.