{"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/viewpoint-optimization-for-autonomous","title":"Viewpoint Optimization for Autonomous Strawberry Harvesting with Deep Reinforcement Learning","arxiv_id":"1903.02074","date":"2019-03-05","proceeding":null,"authors":["Jonathon Sather","Xiaozheng Jane Zhang"],"abstract":"Autonomous harvesting may provide a viable solution to mounting labor\npressures in the United States's strawberry industry. However, due to\nbottlenecks in machine perception and economic viability, a profitable and\ncommercially adopted strawberry harvesting system remains elusive. In this\nresearch, we explore the feasibility of using deep reinforcement learning to\novercome these bottlenecks and develop a practical algorithm to address the\nsub-objective of viewpoint optimization, or the development of a control policy\nto direct a camera to favorable vantage points for autonomous harvesting. We\nevaluate the algorithm's performance in a custom, open-source simulated\nenvironment and observe encouraging results. Our trained agent yields 8.7 times\nhigher returns than random actions and 8.8 percent faster exploration than our\nbest baseline policy, which uses visual servoing. Visual investigation shows\nthe agent is able to fixate on favorable viewpoints, despite having no explicit\nmeans to propagate information through time. Overall, we conclude that deep\nreinforcement learning is a promising area of research to advance the state of\nthe art in autonomous strawberry harvesting.","url_abs":"http://arxiv.org/abs/1903.02074v2","url_pdf":"http://arxiv.org/pdf/1903.02074v2.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":"viewpoint-optimization-for-autonomous","repo_url":"https://github.com/jsather/harvester-sim","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-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}