Greenotyper: Image-based plant phenotyping using distributed computing and deep learning

TY - JOUR

T1 - Greenotyper: Image-based plant phenotyping using distributed computing and deep learning

AU - Tausen, Marni

AU - Clausen, Marc Mathias

AU - Moeskjær, Sara

AU - Shihavuddin, ASM

AU - Dahl, Anders Bjorholm

AU - Janss, Luc

AU - Andersen, Stig Uggerhøj

PY - 2020/8

Y1 - 2020/8

N2 - Image-based phenotype data with high temporal resolution offers advantages over end-point measurements in plant quantitative genetics experiments, because growth dynamics can be assessed and analysed for genotype-phenotype association. Recently, network-based camera systems have been deployed as customizable, low-cost phenotyping solutions. Here, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1,800 white clover (Trifolium repens) plants. The camera system proved stable with an average uptime of 96% across all 180 cameras. For analysis of the captured images, we developed the Greenotyper image analysis pipeline. It detected the location of the plants with a bounding box accuracy of 97.98%, and the U-net-based plant segmentation had an intersection over union accuracy of 0.84 and a pixel accuracy of 0.95. We used Greenotyper to analyze a total of 355,027 images, which required 24–36 h. Automated phenotyping using a large number of static cameras and plants thus proved a cost-effective alternative to systems relying on conveyor belts or mobile cameras.

AB - Image-based phenotype data with high temporal resolution offers advantages over end-point measurements in plant quantitative genetics experiments, because growth dynamics can be assessed and analysed for genotype-phenotype association. Recently, network-based camera systems have been deployed as customizable, low-cost phenotyping solutions. Here, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1,800 white clover (Trifolium repens) plants. The camera system proved stable with an average uptime of 96% across all 180 cameras. For analysis of the captured images, we developed the Greenotyper image analysis pipeline. It detected the location of the plants with a bounding box accuracy of 97.98%, and the U-net-based plant segmentation had an intersection over union accuracy of 0.84 and a pixel accuracy of 0.95. We used Greenotyper to analyze a total of 355,027 images, which required 24–36 h. Automated phenotyping using a large number of static cameras and plants thus proved a cost-effective alternative to systems relying on conveyor belts or mobile cameras.

KW - Deep Learning

KW - Greenness measures

KW - Image detection

KW - Object detection and segmentation

KW - Plant Phenotyping

KW - Raspberry Pi

KW - Software

UR - http://www.scopus.com/inward/record.url?scp=85089954008&partnerID=8YFLogxK

U2 - 10.3389/fpls.2020.01181

DO - 10.3389/fpls.2020.01181

M3 - Journal article

C2 - 32849731

SN - 1664-462X

VL - 11

JO - Frontiers in Plant Science

JF - Frontiers in Plant Science

M1 - 1181

ER -