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BECA-D provides images across multiple beef breeds, while BECA-L provides annotated images from cattle tracked for up to five months.",{"name":69,"@type":60,"acceptedAnswer":70},"What additional annotation subsets does BECA provide beyond recognition?",{"text":71,"@type":63},"BECA offers annotation subsets for key pipeline tasks, including object detection and pose estimation, to support the development of models for long-term cattle recognition.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},450198,1790903718,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":30,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","[www. nature.com/scientificdata](www. nature.com/scientificdata)  \nopEN  \nDATA DESCripTor  \nBECa: a Computer Vision Dataset for Long-term Recognition In Beef Cattle  \nYuqi Zhang1,3, Longxiang Li1,3, Chunyang Li1, Sen Wang1, Yue Rong2, Kai Niu1 & Zhiqiang He1 ✉  \nDespite significant progress in computer vision for precision livestock farming, a critical challenge persists: the lack of effective long-term cattle re-identification datasets under real-world conditions, primarily due to substantial phenotypic changes. To address this challenge, we present the Beef Cattle dataset (BECA), a novel, large-scale dataset specifically designed to support long-term and diverse cattle recognition from dorsal views. BECA encompasses two sub-datasets: the Beef Cattle dataset for Diversity (BECA-D), containing 16,889 images from 5,661 beef cattle across multiple breeds, designed to capture visual diversity for recognition; and the Beef Cattle dataset for Long-term recognition (BECA-L), comprising 12,172 annotated images from 103 cattle tracked over a continuous period of up to five months—representing a notably long duration for cattle recognition datasets. Additionally, we provide annotation subsets for key pipeline tasks such as object detection and pose estimation, supporting the development of diverse models for long-term recognition. Collectively, BECA establishes a comprehensive benchmark for vision-based livestock management, facilitating research in cattle identification, behavior analysis, and welfare monitoring.  \nBackground & Summary  \nLarge-scale and centralized growing and fattening of beef cattle is essential for societal development 1. Large-scale farming can reduce costs and save space while meeting the growing demands for increased production2. Precision livestock farming, which leverages modern technologies to improve efficiency, animal welfare, and sustainability, is crucial in this context. As the dairy industry becomes more intelligent and streamlined3–6, similar advancements in the beef cattle industry deserve attention.  \nThe dairy industry offers valuable reference and comparison points. Large-scale dairy farming is characterized by high profit, numerous metrics, and uniform breeds. The main product is milk, and by-products include dairy bulls and culled cows. The focus in dairy farming is on managing the estrus and lactation periods9, 10, with dairy cows requiring nearly 300 days of milking each year, using specialized lanes. These specialized facilities also enable systematic dorsal data collection, as evidenced by datasets like OpenCows202021 and Cows202122. This is significantly different from the focus of the beef cattle industry. Beef cattle breeds mainly include Simmental and Angus11, and fattening primarily involves bulls. Once grouped, the cattle are fed and fattened in confined areas under group management, with the main by-product being the fattened beef cattle12, 13.  \nThe beef cattle industry presents several challenges for implementation of precision livestock farming implementation. These challenges include longer profit cycles with relatively lower returns, fewer metrics, and a high proportion of crossbred cattle. This leads to several issues during the fattening process: Firstly, beef cattle breeds are predominantly hybrid, with a variety of common breeds including Simmental and Angus14, 15, with some cattle showing minimal weight gain despite consistent feeding. Secondly, the strong aggression and fighting tendencies of bulls increase the difficulty of external tagging16, 17, such as higher probabilities of ear tag loss and increased difficulty in re-tagging. Thirdly, the stress response caused by ear tags is amplified. Dairy cattle have a lifespan of 3-5 years, whereas beef cattle are only kept for 18 months, magnifying the stress response from pain. As a result, ear tags are not ideal for beef cattle management, which demands lower-cost and less intrusive solutions.  \n1 Key Laboratory of Universal Wi","cbCaibR0vCXnZF3M","https://ap.wps.com/l/cbCaibR0vCXnZF3M","pdf",8824359,15,"English","# Background & Summary\n## Precision livestock farming context\n## Dairy vs. beef cattle differences\n## Challenges in beef cattle implementation\n## Dataset overview and benchmarks","[{\"question\":\"What problem does BECA aim to solve in cattle recognition?\",\"answer\":\"BECA targets the lack of effective long-term cattle re-identification datasets under real-world conditions, where phenotypic changes significantly hinder consistent matching over time.\"},{\"question\":\"What are the two sub-datasets included in BECA?\",\"answer\":\"BECA contains BECA-D for diversity and BECA-L for long-term recognition. BECA-D provides images across multiple beef breeds, while BECA-L provides annotated images from cattle tracked for up to five months.\"},{\"question\":\"What additional annotation subsets does BECA provide beyond recognition?\",\"answer\":\"BECA offers annotation subsets for key pipeline tasks, including object detection and pose estimation, to support the development of models for long-term cattle recognition.\"}]","BECa: a Computer Vision Dataset for Long-term Recognition In Beef Cattle | PDF",1790732406,38]