{"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/deepwheat-estimating-phenotypic-traits-from","title":"DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning","arxiv_id":"1710.00241","date":"2017-09-30","proceeding":null,"authors":["Shubhra Aich","Anique Josuttes","Ilya Ovsyannikov","Keegan Strueby","Imran Ahmed","Hema Sudhakar Duddu","Curtis Pozniak","Steve Shirtliffe","Ian Stavness"],"abstract":"In this paper, we investigate estimating emergence and biomass traits from\ncolor images and elevation maps of wheat field plots. We employ a\nstate-of-the-art deconvolutional network for segmentation and convolutional\narchitectures, with residual and Inception-like layers, to estimate traits via\nhigh dimensional nonlinear regression. Evaluation was performed on two\ndifferent species of wheat, grown in field plots for an experimental plant\nbreeding study. Our framework achieves satisfactory performance with mean and\nstandard deviation of absolute difference of 1.05 and 1.40 counts for emergence\nand 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants\nfrom field images are better than the accuracy reported for the similar, but\narguably less difficult, task of counting leaves from indoor images of rosette\nplants. Our results for biomass estimation, even with a very small dataset,\nimprove upon all previously proposed approaches in the literature.","url_abs":"http://arxiv.org/abs/1710.00241v2","url_pdf":"http://arxiv.org/pdf/1710.00241v2.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":"deepwheat-estimating-phenotypic-traits-from","repo_url":"https://github.com/p2irc/deepwheat_WACV-2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}