ArticlePlant methods2020
Unsupervised Bayesian learning for rice panicle segmentation with UAV images.
Article in Plant methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Soil Region Segmentation and Visual Whiteness Analysis in Cold-Region Rice Seedbeds Based on Improved DAC-UNet.Plants (Basel, Switzerland) · 2026Article
- CVRP: A rice image dataset with high-quality annotations for image segmentation and plant phenomics research.Plant phenomics (Washington, D.C.) · 2025Article
- Rice tiller number estimation based on an improved Swin-UNet model and multi-feature fusion.Frontiers in plant science · 2025Article
- Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery.Plant phenomics (Washington, D.C.) · 2024Article
- A High-Throughput Method for Accurate Extraction of Intact Rice Panicle Traits.Plant phenomics (Washington, D.C.) · 2024Article
- TasselLFANet: a novel lightweight multi-branch feature aggregation neural network for high-throughput image-based maize tassels detection and counting.Frontiers in plant science · 2023Article
- Image-Based Phenotyping for Non-Destructive In Situ Rice (Sensors (Basel, Switzerland) · 2022Article
- Field rice panicle detection and counting based on deep learning.Frontiers in plant science · 2022Article
- Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model.Frontiers in plant science · 2022Article
- Estimating and evaluating the rice cluster distribution uniformity with UAV-based images.Scientific reports · 2021Article
- Maize Tassel Detection From UAV Imagery Using Deep Learning.Frontiers in robotics and AI · 2021Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundIn this paper, an unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV) over paddy fields. Unlike existing supervised learning methods that require a large amount of labeled training data, the unsupervised learning approach detects panicle pixels in UAV images by analyzing statistical properties of pixels in an image without a training phase. Under the Bayesian framework, the distributions of pixel intensities are assumed to follow a multivariate Gaussian mixture model (GMM), with different components in the GMM corresponding to different categories, such as panicle, leaves, or background. The prevalence of each category is characterized by the weights associated with each component in the GMM. The model parameters are iteratively learned by using the Markov chain Monte Carlo (MCMC) method with Gibbs sampling, without the need of labeled training data.
resultsApplying the unsupervised Bayesian learning algorithm on diverse UAV images achieves an average recall, precision and
conclusionsExperimental results demonstrate that the proposed method can accurately identify panicle pixels in UAV images taken under diverse conditions.
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