High-throughput phenotyping of plant roots in temporal series of images using deep learning

RAFAEL NICOLAS GAGGIÓN ZULPO; FEDERICO ARIEL; MARTIN CRESPI; ENZO FERRANTE; THOMAS ROULE; THOMAS BLEIN

Salta

Simposio; AGRANDA JAIIO (Simposio Argentino de Ciencia de Datos y GRANdes DAtos); 2019

JAIIO

Root segmentation in plant images is a crucial step when performing high-throughput plant phenotyping. This task is usually performed in a manual or semi-automatic way, deliniating the root in pictures of plants growing vertically on the surface of a semisolid agarized medium. Temporal phenotyping is generally avoided due to technical limitations to capture such pictures during time. In this project, we employ a low cost device composed of plastic parts generated using a 3D printer, low-price cameras and infra-red LED lights to obtain a photo-sequence of growing plants. We propose a segmentation algorithm based on convolutional neural networks (CNN) to extract the plant roots, and present a comparative study of three different CNN models for such task. Our goal is to generate a reliable graph representation of the root system architecture, useful to obtain descriptive phenotyping parameters.