{"688391":{"#nid":"688391","#data":{"type":"news","title":"Robot Pollinator Could Produce More, Better Crops for Indoor Farms","body":[{"value":"\u003Cp\u003EA new robot could solve one of the biggest challenges facing indoor farmers: manual pollination.\u003C\/p\u003E\u003Cp\u003EIndoor farms, also known as vertical farms, are popular among agricultural researchers and are expanding across the agricultural industry. Some benefits they have over outdoor farms include:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EYear-round production of food crops\u003C\/li\u003E\u003Cli\u003ELess water and land requirements\u003C\/li\u003E\u003Cli\u003ENot needing pesticides\u003C\/li\u003E\u003Cli\u003EReducing carbon emissions from shipping\u003C\/li\u003E\u003Cli\u003EReducing food waste\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EAdditionally,\u0026nbsp;\u003Ca href=\u0022https:\/\/www.agritecture.com\/blog\/2021\/7\/20\/5-ways-vertical-farming-is-improving-nutrition\u0022\u003E\u003Cstrong\u003Esome studies\u003C\/strong\u003E\u003C\/a\u003E indicate that indoor farms produce more nutritious food for urban communities.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EHowever, these farms are often inaccessible to birds, bees, and other natural pollinators, leaving the pollination process to humans. The tedious process must be completed by hand for each flower to ensure the indoor crop flourishes.\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/research.gatech.edu\/people\/ai-ping-hu\u0022\u003E\u003Cstrong\u003EAi-Ping Hu\u003C\/strong\u003E\u003C\/a\u003E, a principal research engineer at the Georgia Tech Research Institute (GTRI), has spent years exploring methods to efficiently pollinate flowering plants and food crops in indoor farms to find a way to efficiently pollinate flower plants and food crops in indoor farms.\u003C\/p\u003E\u003Cp\u003EHu,\u0026nbsp;\u003Ca href=\u0022https:\/\/research.gatech.edu\/people\/shreyas-kousik\u0022\u003E\u003Cstrong\u003EAssistant Professor Shreyas Kousik of the George W. Woodruff School of Mechanical Engineering\u003C\/strong\u003E\u003C\/a\u003E, and a rotating group of student interns have developed a robot prototype that may be up to the task.\u003C\/p\u003E\u003Cp\u003EThe robot can efficiently pollinate plants that have both male and female reproductive parts. These plants only require pollen to be transferred from one part to the other rather than externally from another flower.\u003C\/p\u003E\u003Cp\u003ENatural pollinators perform this task outdoors, but Hu said indoor farmers often use a paintbrush or electric tootbrush to ensure these flowers are pollinated.\u0026nbsp;\u003C\/p\u003E\u003Ch4\u003E\u003Cstrong\u003EKnowing the Pose\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003EAn early challenge the research team addressed was teaching the robot to identify the \u201cpose\u201d of each flower. Pose refers to a flower\u2019s orientation, shape, and symmetry. Knowing these details ensures precise delivery of the pollen to maximize reproductive success.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cIt\u2019s crucial to know exactly which way the flowers are facing,\u201d Hu said.\u003C\/p\u003E\u003Cp\u003E\u201cYou want to approach the flower from the front because that\u2019s where all the biological structures are. Knowing the pose tells you where the stem is. Our device grasps the stem and shakes it to dislodge the pollen.\u003C\/p\u003E\u003Cp\u003E\u201cEvery flower is going to have its own pose, and you need to know what that is within at least 10 degrees.\u201d\u003C\/p\u003E\u003Ch4\u003E\u003Cstrong\u003EComputer Vision Breakthrough\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003E\u003Cstrong\u003EHarsh Muriki\u003C\/strong\u003E is a robotics master\u2019s student at Georgia Tech\u2019s School of Interactive Computing, who used computer vision to solve the pose problem while interning for Hu and GTRI.\u003C\/p\u003E\u003Cp\u003EMuriki attached a camera to a FarmBot to capture images of strawberry plants from dozens of angles in a small garden in front of Georgia Tech\u2019s Food Processing Technology Building. The\u0026nbsp;\u003Ca href=\u0022https:\/\/farm.bot\/?srsltid=AfmBOoqh1Z8vSs3WflZisgw5DsOUSo8shD4VtY0Y8_VmVpVyt0Iwalxo\u0022\u003E\u003Cstrong\u003EFarmBot\u003C\/strong\u003E\u003C\/a\u003E is an XYZ-axis robot that waters and sprays pesticides on outdoor gardens, though it is not capable of pollination.\u003C\/p\u003E\u003Cp\u003E\u201cWe reconstruct the images of the flower into a 3D model and use a technique that converts the 3D model into multiple 2D images with depth information,\u201d Muriki said. \u201cThis enables us to send them to object detectors.\u201d\u003C\/p\u003E\u003Cp\u003EMuriki said he used a real-time object detection system called YOLO (You Only Look Once) to classify objects. YOLO is known for identifying and classifying objects in a single pass.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EVed Sengupta\u003C\/strong\u003E, a computer engineering major who interned with Muriki, fine-tuned the algorithms that converted 3D images into 2D.\u003C\/p\u003E\u003Cp\u003E\u201cThis was a crucial part of making robot pollination possible,\u201d Sengupta said. \u201cThere is a big gap between 3D and 2D image processing.\u003C\/p\u003E\u003Cp\u003E\u201cThere\u2019s not a lot of data on the internet for 3D object detection, but there\u2019s a ton for 2D. We were able to get great results from the converted images, and I think any sector of technology can take advantage of that.\u201d\u003C\/p\u003E\u003Cp\u003ESengupta, Muriki, and Hu co-authored a paper about their work that was accepted to the 2025 International Conference on Robotics and Automation (ICRA) in Atlanta.\u003C\/p\u003E\u003Ch4\u003E\u003Cstrong\u003EMeasuring Success\u003C\/strong\u003E\u003C\/h4\u003E\u003Cp\u003EThe pollination robot, built in Kousik\u2019s Safe Robotics Lab, is now in the prototype phase.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EHu said the robot can do more than pollinate. It can also analyze each flower to determine how well it was pollinated and whether the chances for reproduction are high.\u003C\/p\u003E\u003Cp\u003E\u201cIt has an additional capability of microscopic inspection,\u201d Hu said. \u201cIt\u2019s the first device we know of that provides visual feedback on how well a flower was pollinated.\u201d\u003C\/p\u003E\u003Cp\u003EFor more information about the robot, visit the\u0026nbsp;\u003Ca href=\u0022https:\/\/saferoboticslab.me.gatech.edu\/research\/towards-robotic-pollination\/\u0022\u003E\u003Cstrong\u003ESafe Robotics Lab project page\u003C\/strong\u003E\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EManual pollination is one of the biggest challenges for indoor farmers. These farms are often inaccessible to birds, bees, and other natural pollinators, leaving the pollination process to humans. The tedious process must be completed by hand for each flower to ensure the indoor crop flourishes.\u003C\/p\u003E\u003Cp\u003EA Georgia Tech research led by Ai-Ping Hu and Shreyas Kousik team is working to solve that. A robot they\u0027ve developed can efficiently pollinate plants that have both male and female reproductive parts. These plants only require pollen to be transferred from one part to the other rather than externally from another flower.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"A research team that expands GTRI, the College of Engineering, and the College of Computing have developed a robot capable of pollinating flowers in indoor farms."}],"uid":"36530","created_gmt":"2026-02-19 18:58:12","changed_gmt":"2026-03-20 12:54:01","author":"Nathan Deen","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2026-02-19T00:00:00-05:00","iso_date":"2026-02-19T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"hg_media":{"679370":{"id":"679370","type":"image","title":"Harsh-Muriki_86A0006.jpg","body":null,"created":"1771527500","gmt_created":"2026-02-19 18:58:20","changed":"1771527500","gmt_changed":"2026-02-19 18:58:20","alt":"Harsh Muriki","file":{"fid":"263520","name":"Harsh-Muriki_86A0006.jpg","image_path":"\/sites\/default\/files\/2026\/02\/19\/Harsh-Muriki_86A0006.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/19\/Harsh-Muriki_86A0006.jpg","mime":"image\/jpeg","size":140654,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/19\/Harsh-Muriki_86A0006.jpg?itok=rd0rv1Yt"}}},"media_ids":["679370"],"groups":[{"id":"47223","name":"College of Computing"},{"id":"1188","name":"Research Horizons"},{"id":"50876","name":"School of Interactive Computing"}],"categories":[{"id":"194606","name":"Artificial Intelligence"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"145","name":"Engineering"},{"id":"135","name":"Research"},{"id":"152","name":"Robotics"}],"keywords":[{"id":"9153","name":"Research Horizons"},{"id":"187991","name":"go-robotics"},{"id":"192863","name":"go-ai"},{"id":"11506","name":"computer vision"},{"id":"180840","name":"computer vision systems"},{"id":"669","name":"agriculture"},{"id":"194392","name":"AI in Agriculture"},{"id":"170254","name":"urban gardening"},{"id":"94111","name":"farming"},{"id":"14913","name":"urban farming"},{"id":"23911","name":"bees"},{"id":"6660","name":"flowers"},{"id":"187915","name":"go-researchnews"}],"core_research_areas":[{"id":"193655","name":"Artificial Intelligence at Georgia Tech"},{"id":"193653","name":"Georgia Tech Research Institute"},{"id":"39521","name":"Robotics"}],"news_room_topics":[{"id":"71911","name":"Earth and Environment"}],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Ca href=\u0022mailto:ndeen6@gatech.edu\u0022\u003ENathan Deen\u003C\/a\u003E\u003Cbr\u003ECollege of Computing\u003Cbr\u003EGeorgia Tech\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}},"686843":{"#nid":"686843","#data":{"type":"news","title":"NSF Grant Funds Protein Research for Drug Discovery and Personalized Medicine","body":[{"value":"\u003Cp\u003EProteins, including antibodies, hemoglobin, and insulin, power nearly every vital aspect of life. Breakthroughs in protein research are producing vaccines, resilient crops, bioenergy sources, and other innovative technologies.\u003C\/p\u003E\u003Cp\u003EDespite their importance, most of what scientists know about proteins only comes from a small sample size. This stands in the way of fully understanding how most proteins work and unlocking their full potential.\u003C\/p\u003E\u003Cp\u003EGeorgia Tech\u2019s \u003Ca href=\u0022https:\/\/faculty.cc.gatech.edu\/~yunan\/\u0022\u003EYunan Luo\u003C\/a\u003E believes artificial intelligence (AI) could fill this knowledge gap. The National Science Foundation agrees. Luo is the recipient of an NSF Faculty Early Career Development (\u003Ca href=\u0022https:\/\/www.nsf.gov\/funding\/opportunities\/career-faculty-early-career-development-program\u0022\u003ECAREER\u003C\/a\u003E) award.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cSo much of biology depends on knowing what proteins do, but decades of research have concentrated on a relatively small set of well-studied proteins. This imbalance in scientific attention leads to a distorted view of the biological landscape that\u0026nbsp;quietly shapes our data and our algorithms,\u201d Luo said.\u003C\/p\u003E\u003Cp\u003E\u201cMy group\u2019s goal is to build machine learning (ML) models that actively close this gap by generating trustworthy\u0026nbsp;function predictions for the many proteins that remain understudied.\u201d\u003C\/p\u003E\u003Cp\u003E[Related: \u003Ca href=\u0022https:\/\/www.cc.gatech.edu\/news\/faculty-use-ai-protein-design-and-discovery-support-18-million-nih-grant\u0022\u003EYunan Luo to use AI for Protein Design and Discovery with Support of $1.8 Million NIH Grant\u003C\/a\u003E]\u003C\/p\u003E\u003Cp\u003EIn his \u003Ca href=\u0022https:\/\/www.nsf.gov\/awardsearch\/show-award\/?AWD_ID=2442063\u0026amp;HistoricalAwards=false\u0022\u003Eproposal to NSF\u003C\/a\u003E, Luo coined this rich-get-richer effect \u201cannotation inequality.\u201d\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EOne problem of annotation inequality is that it slows progress in disease prognosis, drug discovery, and other critical biomedical areas. It is challenging to innovate the few proteins that scientists already know so much about.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EA cascading effect of annotation inequality is that it diminishes the effectiveness of studying proteins with\u0026nbsp;AI. \u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAI methods learn from existing experimental data. Datasets skewed toward well-known proteins propagate and become entrenched in models. Over time, this makes it harder for computers to research understudied proteins.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cProtein annotation inequality creates an effect analogous to a vast library where 95% of patrons only read the top 5% popular books, leaving the rest of the collection to gather dust,\u201d Luo said.\u003C\/p\u003E\u003Cp\u003E\u201cThis has resulted in knowledge disparities across proteins in current literature and databases, biasing our understanding of protein functions.\u201d\u003C\/p\u003E\u003Cp\u003EThe NSF CAREER award will fund Luo with over $770,000 for the next five years to tackle head-on the problem of protein annotation inequality.\u003C\/p\u003E\u003Cp\u003ELuo will use the grant to build an accurate, unbiased protein function prediction framework at scale. His project aims to:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EReveal how annotation inequality affects protein function prediction systems\u003C\/li\u003E\u003Cli\u003ECreate ML techniques suited for biological data, which is often noisy, incomplete, and imbalanced \u0026nbsp;\u003C\/li\u003E\u003Cli\u003EIntegrate data and ML models into a scalable framework to accelerate discoveries involving understudied proteins\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EMore enduring than the ML framework, Luo will leverage the NSF award to support educational and outreach programs. His goal is to groom the next generation of researchers to study other challenges in computational biology, not just the annotation inequality problem.\u003C\/p\u003E\u003Cp\u003ELuo teaches graduate and undergraduate courses focused on computational biology and ML. Problems and methods developed through the CAREER project can be used as course material in his classes.\u003C\/p\u003E\u003Cp\u003ELuo also championed collaboration with Georgia Tech\u2019s Center for Education Integrating Science, Mathematics, and Computing (\u003Ca href=\u0022https:\/\/www.ceismc.gatech.edu\/\u0022\u003ECEISMC\u003C\/a\u003E) in his proposal.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThrough this partnership, local high school teachers and students would gain access to his data and models. This promotes deeper learning of biology and data science through hands-on experience with real-world tools. \u0026nbsp;\u003C\/p\u003E\u003Cp\u003ELuo sees reaching students and the community as a way of paying forward the support he received from Georgia Tech colleagues.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cI am incredibly grateful for this recognition from the NSF,\u201d said Luo, an assistant professor in the \u003Ca href=\u0022https:\/\/cse.gatech.edu\/\u0022\u003ESchool of Computational Science and Engineering\u003C\/a\u003E (CSE).\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cThis would not have been possible without my students and collaborators, whose hard work laid the groundwork for this proposal.\u201d\u003C\/p\u003E\u003Cp\u003ELuo praised CSE faculty members \u003Ca href=\u0022https:\/\/faculty.cc.gatech.edu\/~badityap\/\u0022\u003EB. Aditya Prakash\u003C\/a\u003E, \u003Ca href=\u0022https:\/\/xiuweizhang.wordpress.com\/\u0022\u003EXiuwei Zhang\u003C\/a\u003E, and \u003Ca href=\u0022http:\/\/chaozhang.org\/\u0022\u003EChao Zhang\u003C\/a\u003E for their guidance. All three study \u003Ca href=\u0022https:\/\/cse.gatech.edu\/artificial-intelligence-and-machine-learning\u0022\u003Emachine learning\u003C\/a\u003E and \u003Ca href=\u0022https:\/\/cse.gatech.edu\/computational-bioscience-and-biomedicine\u0022\u003Ecomputational bioscience\u003C\/a\u003E, two of \u003Ca href=\u0022https:\/\/cse.gatech.edu\/research\u0022\u003ECSE\u2019s five core research areas\u003C\/a\u003E.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ELuo also thanked \u003Ca href=\u0022https:\/\/faculty.cc.gatech.edu\/~hpark\/\u0022\u003EHaesun Park\u003C\/a\u003E for her support and recommendation for the CAREER award. Park is a Regents\u2019 Professor and the chair of the School of CSE.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EProteins, including antibodies, hemoglobin, and insulin, power nearly every vital aspect of life. Breakthroughs in protein research are producing vaccines, resilient crops, bioenergy sources, and other innovative technologies.\u003C\/p\u003E\u003Cp\u003EDespite their importance, most of what scientists know about proteins only comes from a small sample size. This stands in the way of fully understanding how most proteins work and unlocking their full potential.\u003C\/p\u003E\u003Cp\u003EGeorgia Tech\u2019s \u003Ca href=\u0022https:\/\/faculty.cc.gatech.edu\/~yunan\/\u0022\u003EYunan Luo\u003C\/a\u003E believes artificial intelligence (AI) could fill this knowledge gap. The National Science Foundation agrees. Luo is the recipient of an NSF Faculty Early Career Development (\u003Ca href=\u0022https:\/\/www.nsf.gov\/funding\/opportunities\/career-faculty-early-career-development-program\u0022\u003ECAREER\u003C\/a\u003E) award.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Yunan Luo is the recipient of an NSF Faculty Early Career Development (CAREER) award to use artificial intelligence to solve the protein annotation inequality problem."}],"uid":"36319","created_gmt":"2025-12-10 16:57:22","changed_gmt":"2026-01-09 13:37:31","author":"Bryant Wine","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2025-12-10T00:00:00-05:00","iso_date":"2025-12-10T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"hg_media":{"678817":{"id":"678817","type":"image","title":"Yunan-Luo-NSF-CAREER_1.jpg","body":null,"created":"1765385865","gmt_created":"2025-12-10 16:57:45","changed":"1765385865","gmt_changed":"2025-12-10 16:57:45","alt":"Yunan Luo NSF CAREER Award","file":{"fid":"262902","name":"Yunan-Luo-NSF-CAREER_1.jpg","image_path":"\/sites\/default\/files\/2025\/12\/10\/Yunan-Luo-NSF-CAREER_1.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/12\/10\/Yunan-Luo-NSF-CAREER_1.jpg","mime":"image\/jpeg","size":108350,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/12\/10\/Yunan-Luo-NSF-CAREER_1.jpg?itok=j83dW4Sn"}},"678818":{"id":"678818","type":"image","title":"Yunan-Luo-NSF-CAREER_2.jpg","body":null,"created":"1765385967","gmt_created":"2025-12-10 16:59:27","changed":"1765385967","gmt_changed":"2025-12-10 16:59:27","alt":"Yunan Luo NSF CAREER Award","file":{"fid":"262903","name":"Yunan-Luo-NSF-CAREER_2.jpg","image_path":"\/sites\/default\/files\/2025\/12\/10\/Yunan-Luo-NSF-CAREER_2.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/12\/10\/Yunan-Luo-NSF-CAREER_2.jpg","mime":"image\/jpeg","size":100260,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/12\/10\/Yunan-Luo-NSF-CAREER_2.jpg?itok=CShGR6nJ"}}},"media_ids":["678817","678818"],"related_links":[{"url":"https:\/\/www.cc.gatech.edu\/news\/nsf-grant-funds-protein-research-drug-discovery-and-personalized-medicine","title":"NSF Grant Funds Protein Research for Drug Discovery and Personalized Medicine"}],"groups":[{"id":"47223","name":"College of Computing"},{"id":"1188","name":"Research Horizons"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[{"id":"194606","name":"Artificial Intelligence"},{"id":"138","name":"Biotechnology, Health, Bioengineering, Genetics"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"146","name":"Life Sciences and Biology"},{"id":"135","name":"Research"}],"keywords":[{"id":"654","name":"College of Computing"},{"id":"166983","name":"School of Computational Science and Engineering"},{"id":"9153","name":"Research Horizons"},{"id":"187915","name":"go-researchnews"},{"id":"10199","name":"Daily Digest"},{"id":"181991","name":"Georgia Tech News Center"},{"id":"9167","name":"machine learning"},{"id":"187812","name":"artificial intelligence (AI)"},{"id":"2556","name":"artificial intelligence"},{"id":"362","name":"National Science Foundation"},{"id":"191934","name":"National Science Foundation (NSF)"},{"id":"170447","name":"Institute for Data Engineering and Science"},{"id":"176858","name":"machine learning center"},{"id":"173894","name":"ML@GT"}],"core_research_areas":[{"id":"193655","name":"Artificial Intelligence at Georgia Tech"},{"id":"39441","name":"Bioengineering and Bioscience"},{"id":"39431","name":"Data Engineering and Science"}],"news_room_topics":[{"id":"71871","name":"Campus and Community"}],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EBryant Wine, Communications Officer\u003Cbr\u003E\u003Ca href=\u0022mailto:bryant.wine@cc.gatech.edu\u0022\u003Ebryant.wine@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}