{"id":13365,"date":"2024-06-10T21:17:42","date_gmt":"2024-06-11T02:17:42","guid":{"rendered":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/?p=13365"},"modified":"2026-04-15T01:09:25","modified_gmt":"2026-04-15T05:09:25","slug":"elicitating-challenges-and-user-needs-associated-with-annotation-software-for-plant-phenotyping","status":"publish","type":"post","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/2024\/06\/10\/elicitating-challenges-and-user-needs-associated-with-annotation-software-for-plant-phenotyping\/","title":{"rendered":"Elicitating Challenges and User Needs Associated with Annotation Software for Plant Phenotyping"},"content":{"rendered":"<h2>Abstract:<\/h2>\n<p>Artificial Intelligence (AI) has been enhancing data analysis efficiency and accuracy during plant phenotyping, which is vital for tackling global agricultural and environmental challenges. Designing a reliable AI system to assist precise plant phenotyping begins with high-quality phenotypic feature annotation, which usually involves collaboration between plant scientists and AI specialists. However, due to the high level of diversity in these researchers\u2019 backgrounds, it is likely that they have differing user needs from a fine-grained plant feature annotation system. We conducted semi-structured interviews with eight experienced annotators from diverse backgrounds, and observed how they interact with their preferred annotation system, to elucidate the challenges faced when annotating plant features and identify user needs. We collected qualitative responses to the interview questions, and conducted a quantitative evaluation of the agreement of their annotations on the given images. By analyzing the participants\u2019 behaviors and the collected data, we identified common user needs and derived implications for the design of an AI-assisted annotation system, including providing a range of annotation options, the flexibility to adapt annotations, and functions to help addressing uncertainty. Our research contributes to the design of systems that make annotations efficient and reliable, not only benefiting plant phenotyping, but also other interdisciplinary fields that rely on user-driven annotations.<\/p>\n<h2>Links:<\/h2>\n<p><a href=\"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3640543.3645178\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-470 size-full\" src=\"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-content\/uploads\/sites\/759\/2016\/09\/arxiv.png\" alt=\"Webpage for the research paper &quot;Elicitating Challenges and User Needs Associated with Annotation Software for Plant Phenotyping&quot;\" width=\"90\" height=\"90\" \/><\/a><\/p>\n<h2>Citation:<\/h2>\n<pre><code>X. Guo, Q. Li, S. Morrison-Smith, L. Anthony, A. Zare, Y. Song, \"Elicitating Challenges and User Needs Associated with Annotation Software for Plant Phenotyping,\" Proceedings of the 29th International Conference on Intelligent User Interfaces, pp. 431-443, 2024, doi: doi.org\/10.1145\/3640543.3645178.<\/code><\/pre>\n<pre class=\"verbatim select-on-click\" title=\"click to copy to clipboard\"><code>@inproceedings{guo2024elicitating,\ntitle={Elicitating Challenges and User Needs Associated with Annotation Software for Plant Phenotyping},\nauthor={Guo, Xiaolei and Li, Qing and Morrison-Smith, Sarah and Anthony, Lisa and Zare, Alina and Song, Yangyang},\nbooktitle={Proceedings of the 29th International Conference on Intelligent User Interfaces},\npages={431--443},\nyear={2024}\n}\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Abstract: Artificial Intelligence (AI) has been enhancing data analysis efficiency and accuracy during plant phenotyping, which is vital for tackling global agricultural and environmental challenges. Designing a reliable AI system to assist precise plant phenotyping begins with high-quality phenotypic feature annotation, which usually involves collaboration between plant scientists and AI specialists. However, due to the [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-templates\/single-sidebar-none.php","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"off","footnotes":"","_links_to":"","_links_to_target":""},"categories":[17,13],"tags":[],"class_list":["post-13365","post","type-post","status-publish","format-standard","hentry","category-conference_paper","category-publication"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/13365","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=13365"}],"version-history":[{"count":2,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/13365\/revisions"}],"predecessor-version":[{"id":16561,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/posts\/13365\/revisions\/16561"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=13365"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/categories?post=13365"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/tags?post=13365"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}