ArticlePloS one2018
An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Open-source hardware platform for automated, non-invasive plant phenotyping through combined RGB and thermal imaging.HardwareX · 2026Article
- ELMERF: A deep-learning-assisted hydroponic RGB phenotyping framework for rice seedling salt-stress evaluation and genetic mapping.Plant phenomics (Washington, D.C.) · 2026Article
- Beyond the Classics: The Synergy of AI and Genomics Reveals an Expanded Repertoire of Pigmentation Genes.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2026Review
- An intelligent method and platform for obtaining lettuce canopy coverage.Frontiers in plant science · 2026Article
- A Review of CNN Applications in Smart Agriculture Using Multimodal Data.Sensors (Basel, Switzerland) · 2025Review
- Zero-shot instance segmentation for plant phenotyping in vertical farming with foundation models and VC-NMS.Frontiers in plant science · 2025Article
- Plant Stress Detection Using a Three-Dimensional Analysis from a Single RGB Image.Sensors (Basel, Switzerland) · 2024Article
- Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse.Sensors (Basel, Switzerland) · 2024Article
- GRABSEEDS: extraction of plant organ traits through image analysis.Plant methods · 2024Article
- Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.Sensors (Basel, Switzerland) · 2024Article
- RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs.Scientific reports · 2024Article
- Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping.Sensors (Basel, Switzerland) · 2024Article
- Article
- Affordable Phenotyping at the Edge for High-Throughput Detection of Hypersensitive Reaction Involving Cotyledon Loss.Plant phenomics (Washington, D.C.) · 2024Article
- Open-source workflow design and management software to interrogate duckweed growth conditions and stress responses.Plant methods · 2023Article
- The Progression in Developing Genomic Resources for Crop Improvement.Life (Basel, Switzerland) · 2023Review
- Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat.Plants (Basel, Switzerland) · 2023Article
- Convolutional neural networks in the qualitative improvement of sweet potato roots.Scientific reports · 2023Article
- Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.Plants (Basel, Switzerland) · 2023Article
- PhenoWell®-A novel screening system for soil-grown plants.Plant-environment interactions (Hoboken, N.J.) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.
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