PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time

• Published in: Plant methods 2022-12, Vol.18 (1), p.130-130, Article 130
• Main Authors: Daviet, Benoit, Fernandez, Romain, Cabrera-Bosquet, Llorenç, Pradal, Christophe, Fournier, Christian
• Format: Article
• Language: English
• Subjects: Algorithms; Alignment; Analysis; Architecture; Computer Science; Computer vision; Controlled conditions; Corn; Datasets; Deep learning; Genetic aspects; Genotypes; Growth; High-throughput phenotyping; High-throughput screening (Biochemical assaying); Hybrids; Image acquisition; Image reconstruction; Leaves; Machine learning; Maize; Methodology; Methods; Modeling and Simulation; Morphology; Nucleotide sequence; Organs; Phenotype; Phenotyping; Plant physiology; Senescence; Sequence alignment; Tracking
• ISSN: 1746-4811; 1746-4811
• DOI: 10.1186/s13007-022-00961-4

High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE