ArticlePlant methods2018
The use of plant models in deep learning: an application to leaf counting in rosette plants.
Article in Plant methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
75 citing papers in PubMed.
- Plant counting: origins, development, and future paths.Plant phenomics (Washington, D.C.) · 2026Review
- OPAL-Flow: Orientation-aware rice panicle detection and minute-scale anthesis rhythm identification under field conditions.Plant phenomics (Washington, D.C.) · 2026Article
- Synthetic-augmented multimodal deep learning fuses dual-angle RGB images and phenology to unlock genotype-informative canopy structural trait in wheat.Plant phenomics (Washington, D.C.) · 2026Article
- Early Plant Development as a Systems-Level Trait: Integrating Omics, Artificial Intelligence, and Emerging Biotechnologies.Plants (Basel, Switzerland) · 2026Review
- A Systematic Review of Imaging Techniques for the Botanical and Geographical Classification of Coffee.Foods (Basel, Switzerland) · 2026Review
- Deciphering plant stress resilience in different millet crops: from multi-omics discoveries to predictive breeding for climate-resilient crop enhancement.Frontiers in plant science · 2026Review
- APTES: a high-throughput deep learning-based Arabidopsis phenotypic trait estimation system for individual leaves and siliques.aBIOTECH · 2025Article
- Deep learning in plant phenotyping: the first ten years.Plant phenomics (Washington, D.C.) · 2025Review
- Next-generation translational genomics for developing future crops.Functional & integrative genomics · 2025Review
- Generation of labeled leaf point clouds for plants trait estimation.Plant phenomics (Washington, D.C.) · 2025Article
- Edge_MVSFormer: Edge-Aware Multi-View Stereo Plant Reconstruction Based on Transformer Networks.Sensors (Basel, Switzerland) · 2025Article
- Molecular breeding of tomato: Advances and challenges.Journal of integrative plant biology · 2025Review
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
- Evaluation of asphalt mixtures modified with low-density polyethylene and high-density polyethylene using experimental results and machine learning models.Scientific reports · 2024Article
- Leaf rolling detection in maize under complex environments using an improved deep learning method.Plant molecular biology · 2024Article
- A survey of deep learning applications in cryptocurrency.iScience · 2024Review
- A CNN-based model to count the leaves of rosette plants (LC-Net).Scientific reports · 2024Article
- Deep learning and explainable AI for classification of potato leaf diseases.Frontiers in artificial intelligence · 2024Article
- A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants.Journal of imaging · 2023Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.