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A Deep Learning-Based Approach for High-Throughput Hypocotyl Phenotyping
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DOI：10.1104/pp.19.00728.png)
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摘要
En 中文
Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has advanced from using rulers and millimeter papers to assessing digitized images but remains a labor-intensive, monotonous, and time-consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep-learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low-quality images produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets not restricted to Arabidopsis (Arabidopsis thaliana). Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at , but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user. A deep learning-based algorithm provides an adaptable tool for determining hypocotyl or coleoptile length of different plant species.
Keyword:
DEETIOLATED MUSTARD SEEDLINGS
B-INDUCED PHOTOMORPHOGENESIS
ARABIDOPSIS MUTANTS
IMAGE-ANALYSIS
GROWTH
LIGHT
PHYTOCHROME
ELONGATION
PHOTORECEPTOR
EXPRESSION
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