ArticleFrontiers in plant science2022
A Segmentation-Guided Deep Learning Framework for Leaf Counting.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Plant counting: origins, development, and future paths.Plant phenomics (Washington, D.C.) · 2026Review
- A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation.Scientific data · 2026Article
- Data-efficient and accurate rapeseed leaf area estimation by self-supervised vision transformer for germplasms early evaluation.Plant methods · 2025Article
- Lightweight Corn Leaf Detection and Counting Using Improved YOLOv8.Sensors (Basel, Switzerland) · 2024Article
- Transforming Poultry Farming: A Pyramid Vision Transformer Approach for Accurate Chicken Counting in Smart Farm Environments.Sensors (Basel, Switzerland) · 2024Article
- Completing the picture of field-grown cereal crops: a new method for detailed leaf surface models in wheat.Plant methods · 2024Article
- Multi-classification of disease induced in plant leaf using chronological Flamingo search optimization with transfer learning.PeerJ. Computer science · 2024Article
- Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in matureFrontiers in plant science · 2024Article
- A CNN-LSTM-att hybrid model for classification and evaluation of growth status under drought and heat stress in chinese fir (Cunninghamia lanceolata).Plant methods · 2023Article
- Local refinement mechanism for improved plant leaf segmentation in cluttered backgrounds.Frontiers in plant science · 2023Article
- To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status?Plant phenomics (Washington, D.C.) · 2023Article
- Enhancing plant morphological trait identification in herbarium collections through deep learning-based segmentation.Applications in plant sciencesArticle
- PlantSAM: An object detection-driven segmentation pipeline for herbarium specimens.Applications in plant sciencesArticle
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5 authors.
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Abstract
Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.
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