ArticleFrontiers in plant science2024
DFMA: an improved DeepLabv3+ based on FasterNet, multi-receptive field, and attention mechanism for high-throughput phenotyping of seedlings.
Article in Frontiers in plant science, 2024. 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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5 citing papers in PubMed.
- RiceDetect-Net: a lightweight real-time detection framework for rice diseases.BMC plant biology · 2026Article
- VGDS-PointNet++ for organ segmentation and phenotypic trait estimation in greenhouse tomato seedlings.Frontiers in plant science · 2026Article
- Estimating Radicle Length of Germinating Elm Seeds via Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- RP-DETR: end-to-end rice pests detection using a transformer.Plant methods · 2025Article
- Lightweight rice leaf spot segmentation model based on improved DeepLabv3.Frontiers in plant science · 2025Article
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7 authors.
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Abstract
With the rapid advancement of plant phenotyping research, understanding plant genetic information and growth trends has become crucial. Measuring seedling length is a key criterion for assessing seed viability, but traditional ruler-based methods are time-consuming and labor-intensive. To address these limitations, we propose an efficient deep learning approach to enhance plant seedling phenotyping analysis. We improved the DeepLabv3+ model, naming it DFMA, and introduced a novel ASPP structure, PSPA-ASPP. On our self-constructed rice seedling dataset, the model achieved a mean Intersection over Union (mIoU) of 81.72%. On publicly available datasets, including Arabidopsis thaliana, Brachypodium distachyon, and Sinapis alba, detection scores reached 87.69%, 91.07%, and 66.44%, respectively, outperforming existing models. The model generates detailed segmentation masks, capturing structures such as the embryonic shoot, axis, and root, while a seedling length measurement algorithm provides precise parameters for component development. This approach offers a comprehensive, automated solution, improving phenotyping analysis efficiency and addressing the challenges of traditional methods.
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