ArticleFrontiers in plant science2023
High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits.Plant methods · 2025Article
- Deep learning-based methods for phenotypic trait extraction in rice panicles.Frontiers in plant science · 2025Article
- A High-Throughput Method for Accurate Extraction of Intact Rice Panicle Traits.Plant phenomics (Washington, D.C.) · 2024Article
- PanicleNeRF: Low-Cost, High-Precision In-Field Phenotyping of Rice Panicles with Smartphone.Plant phenomics (Washington, D.C.) · 2024Article
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Authors and funding
5 authors.
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Abstract
Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R
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