Unsupervised Bayesian learning for rice panicle segmentation with UAV images

BACKGROUND: In 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. RESULTS: Applying the unsupervised Bayesian learning algorithm on diverse UAV images achieves an average recall, precision and F(1) score of 96.49%, 72.31%, and 82.10%, respectively. These numbers outperform existing supervised learning approaches. CONCLUSIONS: Experimental results demonstrate that the proposed method can accurately identify panicle pixels in UAV images taken under diverse conditions.