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.

具有高时间分辨率的基于图像的表型数据比植物定量遗传学实验中的终点测量更具优势，因为可以评估和分析基因型与表型之间的关联关系。最近，基于网络的摄像头系统已被部署为可定制的低成本表型解决方案。在这里，我们使用180个联网的Raspberry Pi单元实施了一个基于分布式计算的大型自动化图像捕获系统，该单元可以同时监视1800个三叶草（白三叶）植物。事实证明，该摄像头系统稳定，所有180部摄像头的平均正常运行时间为96％。为了分析捕获的图像，我们开发了Greenotyper图像分析管道。它以97.98％的边界框精度检测了植物的位置，基于U-net的植物分割的联合精度为0.84，像素精度为0.95，相交。我们使用Greenotyper分析了总共355,027张图像，需要24–36小时。因此，证明了使用大量静态照相机和工厂进行自动表型分析是替代依赖传送带或移动照相机的系统的一种经济高效的选择。