ArticlePlant methods2017
A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform.
Article in Plant methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed, 105 citations in OpenAlex.
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- LiDAR Platform for Acquisition of 3D Plant Phenotyping Database.Plants (Basel, Switzerland) · 2022Article
- A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping.Phenomics (Cham, Switzerland) · 2022Review
- A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data.Scientific reports · 2022Article
- Inter-tissue and inter-organ signaling in drought stress response and phenotyping of drought tolerance.The Plant journal : for cell and molecular biology · 2022Review
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- High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1-7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses.
resultsWe have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole-plant side views, those best suited for detecting ear position. Images are segmented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth.
conclusionsThe pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large-scale genetic analyses of the control of reproductive growth to changes in environmental conditions in a non-invasive and automatized way. It is available as Open Source software in the OpenAlea platform.
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