Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning

Abstract

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 1800 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 analyse a total of 355027 images, which required 24-36 hours. 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.

Plant phenotyping relevance

大規模カメラ撮像システムとGreenotyper画像解析パイプラインを開発・評価し、植物位置検出とセグメンテーション精度を検証しているため、植物表現型取得法が中心である。

Code and data availability

保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。