2019/08/05 by Chloe He, He, Chloe, Gerard Glowacki +3
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Bee Products Chemical Analysis #Computer Vision and Pattern Recognition (cs.CV) #FOS: Biological sciences #FOS: Computer and information sciences #Insect and Arachnid Ecology and Behavior #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Plant and animal studies #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1908.01866
openalex publication_date 2019/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present the first unsupervised deep learning method for pollen analysis using bright-field microscopy. Using a modest dataset of 650 images of pollen grains collected from honey, we achieve family level identification of pollen. We embed images of pollen grains into a low-dimensional latent space and compare Euclidean and Riemannian metrics on these spaces for clustering. We propose this system for automated analysis of pollen and other microscopic biological structures which have only small or unlabelled datasets available.