{"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/active-learning-with-gaussian-processes-for","title":"Active Learning with Gaussian Processes for High Throughput Phenotyping","arxiv_id":"1901.06803","date":"2019-01-21","proceeding":null,"authors":["Sumit Kumar","Wenhao Luo","George Kantor","Katia Sycara"],"abstract":"A looming question that must be solved before robotic plant phenotyping\ncapabilities can have significant impact to crop improvement programs is\nscalability. High Throughput Phenotyping (HTP) uses robotic technologies to\nanalyze crops in order to determine species with favorable traits, however, the\ncurrent practices rely on exhaustive coverage and data collection from the\nentire crop field being monitored under the breeding experiment. This works\nwell in relatively small agricultural fields but can not be scaled to the\nlarger ones, thus limiting the progress of genetics research. In this work, we\npropose an active learning algorithm to enable an autonomous system to collect\nthe most informative samples in order to accurately learn the distribution of\nphenotypes in the field with the help of a Gaussian Process model. We\ndemonstrate the superior performance of our proposed algorithm compared to the\ncurrent practices on sorghum phenotype data collection.","url_abs":"http://arxiv.org/abs/1901.06803v1","url_pdf":"http://arxiv.org/pdf/1901.06803v1.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":"active-learning-with-gaussian-processes-for","repo_url":"https://github.com/sumitsk/algp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"plant-phenotyping","task_name":"Plant Phenotyping"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}