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

Daviet Benoît, Fernandez Romain, Cabrera-Bosquet Llorenç, Pradal Christophe, Fournier Christian. 2022. PhenoTrack3D: An automatic high-throughput phenotyping pipeline to track maize organs over time. Plant Methods, 18 (1):130, 14 p.

Résumé : Background: 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. Results: 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 < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10–355 plants. Conclusions: We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.

Mots-clés Agrovoc : phénotype, feuille, croissance, physiologie végétale, anatomie végétale, morphologie végétale

Mots-clés libres : High-throughput phenotyping, Computer vision, Maize, Tracking, Sequence alignment, Plant physiology

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) European Plant Phenotyping Network 2020

Auteurs et affiliations

• Daviet Benoît, INRAE (FRA)
• Fernandez Romain, CIRAD-BIOS-UMR AGAP (FRA)
• Cabrera-Bosquet Llorenç, INRAE (FRA)
• Pradal Christophe, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0002-2555-761X - auteur correspondant
• Fournier Christian, INRAE (FRA) - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/603121/)

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