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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning.

Marni Tausen1,2, Marc Clausen2, Sara Moeskjær2

  • 1Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.

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|August 28, 2020
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Summary
This summary is machine-generated.

A novel automated phenotyping system using 180 Raspberry Pi cameras captured high-resolution images of 1,800 white clover plants. This cost-effective approach enables detailed growth dynamics analysis for plant quantitative genetics.

Keywords:
Raspberry Pideep learninggreenness measuresimage detectionobject detectionplant phenotypingsegmentationsoftware

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Area of Science:

  • Plant Science
  • Genetics
  • Computer Science

Background:

  • High temporal resolution image-based phenotyping enhances plant quantitative genetics by revealing growth dynamics.
  • Networked camera systems offer customizable, low-cost solutions for automated plant phenotyping.

Purpose of the Study:

  • To implement a large-scale, automated image-capture system for monitoring numerous plants.
  • To develop an image analysis pipeline for processing high-throughput phenotyping data.
  • To assess the cost-effectiveness of a static camera network for plant phenotyping.

Main Methods:

  • A distributed computing system with 180 networked Raspberry Pi units was deployed to monitor 1,800 white clover plants.
  • The Greenotyper image analysis pipeline was developed for plant detection and segmentation.
  • Image analysis pipeline achieved high accuracy in plant localization (97.98% bounding box) and segmentation (0.84 IoU, 0.95 pixel accuracy).

Main Results:

  • The camera system demonstrated high stability with 96% average uptime across all units.
  • The Greenotyper pipeline successfully analyzed 355,027 images within 24-36 hours.
  • Automated phenotyping with static cameras proved to be a cost-effective alternative to other systems.

Conclusions:

  • A large-scale, automated phenotyping system using networked Raspberry Pi units is feasible and stable.
  • The Greenotyper pipeline provides accurate and efficient analysis of plant images for high-throughput studies.
  • This approach offers a cost-effective solution for plant quantitative genetics research, enabling detailed growth dynamics analysis.