{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/leaf-counting-with-deep-convolutional-and","title":"Leaf Counting with Deep Convolutional and Deconvolutional Networks","arxiv_id":"1708.07570","date":"2017-08-24","proceeding":null,"authors":["Shubhra Aich","Ian Stavness"],"abstract":"In this paper, we investigate the problem of counting rosette leaves from an\nRGB image, an important task in plant phenotyping. We propose a data-driven\napproach for this task generalized over different plant species and imaging\nsetups. To accomplish this task, we use state-of-the-art deep learning\narchitectures: a deconvolutional network for initial segmentation and a\nconvolutional network for leaf counting. Evaluation is performed on the leaf\ncounting challenge dataset at CVPPP-2017. Despite the small number of training\nsamples in this dataset, as compared to typical deep learning image sets, we\nobtain satisfactory performance on segmenting leaves from the background as a\nwhole and counting the number of leaves using simple data augmentation\nstrategies. Comparative analysis is provided against methods evaluated on the\nprevious competition datasets. Our framework achieves mean and standard\ndeviation of absolute count difference of 1.62 and 2.30 averaged over all five\ntest datasets.","url_abs":"http://arxiv.org/abs/1708.07570v2","url_pdf":"http://arxiv.org/pdf/1708.07570v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"leaf-counting-with-deep-convolutional-and","repo_url":"https://github.com/p2irc/leaf_count_ICCVW-2017","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"plant-phenotyping","task_name":"Plant Phenotyping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.07570","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}