[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124626-en":3,"doc-seo-124626-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124626,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Estimating individual-level pig growth trajectories from group-level weight time series using machine learning","Advanced data collection systems are increasingly used in pig farms to improve management, but their costs often limit adoption. Individual-level measurements typically require labor- and expense-intensive setups such as RFID tagging. This paper proposes an alternative that uses inexpensive pen-level weight time series not linked to specific identities to estimate individual-level growth trajectories via machine learning. Simulations from individual data enable accuracy verification with root mean squared error evaluation and downstream two-week growth prediction.","Computers and Electronics in Agriculture 208 (2023) 107790  \nContents lists available at ScienceDirect  \nComputers and Electronics in Agriculture  \njournal [homepage:](homepage: www.elsevier.com/locate/compag)[ www.elsevier.com/locate/compag](homepage: www.elsevier.com/locate/compag)  \n| Estimating individual-level pig growth trajectories from group-level weight   time series using machine learning\u003Cbr>Christian Taylor a, *, Jonathan Guy b, Jaume Bacardita\u003Cbr>a Interdisciplinary Computing and Complex Biosystems (ICOS) Research Group, School of Computing, Newcastle University, Urban Sciences Building, Newcastle upon Tyne, Tyne and Wear NE4 5TG, United Kingdom\u003Cbr>b School of Natural and Environmental Sciences, Newcastle University, Agriculture Building, Newcastle upon Tyne, Tyne and Wear NE1 7RU, United Kingdom |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Machine learning Precision livestock farming Sustainable food production |  | Recent years have seen an increase in the use of advanced data collection systems to improve management in pig farms, however, the cost of implementing such systems remains a barrier to many. Some of the most useful data for farm management are on an individual pig level, which requires investment of labour and expense of systems such as RFID tagging. In this paper an alternative solution is proposed, where pen-level pig weight measurements (i.e. measurements not linked to a specific pig identity), which are significantly cheaper to obtain, are used to estimate individual-level growth trajectories using machine learning. We evaluated our method on group-level data that were simulated from individual-level weight data, which thus allowed for the verification of the accuracy of the trajectory predictions using a root mean squared error score. The on-average best performing predictor for this task was based on a Random Forest model which achieved a best-case score of 2.00 Kg per pig and a worst-case score of 2.45 Kg per pig depending upon the simulated conditions that were used, thus showing that these predicted trajectories could be accurate enough for commercial usage. The usefulness of the predicted trajectories was then evaluated through a growth prediction downstream task whereby the future weights of pigs two weeks into the future were predicted using a variety of machine learning models. It was found that, on average, the growth prediction models that utilised the predicted trajectories outperformed a method that utilised group-level weights directly by 1.42 Kg root mean squared error but were still outperformed by the models that utilised RFID growth trajectories by 0.83 Kg on average. These results show the potential of this method asan alternative to both RFID and photo-based systems for individual-level data estimation. |\n\n1. Introduction  \nBeing able to track the growth of individual livestock can be beneficial to farmers in improving farm management such as by allowing farmers to compare the performances of the poorer performing pigs to the better performing (Frost et al., 1997). In addition, individual-level data can be inputted into prediction models for tasks such as individual-level predictions of growth and disease detection. Specifically, growth prediction can be beneficial in supporting farmers to plan housing and feed requirements as well as selecting optimal market weights for their pigs (Emmans and Kyriazakis, 2000), and the early detection of disease allows for farmers to take pre-emptive measures to mitigate their effects (Cowton et al., 2018).  \nThe most common methods of automatically recording data on an individual pig level utilise Radio Frequency Identification (RFID) tag  \nsystems. These tags can be used to automatically associate data on individual pigs by ensuring proximity of the ears of the pigs that the tag is present on, to an RFID tag reader attached to the machine that is recording the data – for instance a weighing platfo","cbCaiphOtmr059V0","https://ap.wps.com/l/cbCaiphOtmr059V0","pdf",956361,1,11,"English","en",105,"# Introduction\n## Individual livestock growth tracking benefits\n## RFID-based individual data collection\n## Image-based alternatives and limitations\n## Growth prediction using individual data","[{\"question\":\"Why are individual-level pig growth measurements valuable for farmers?\",\"answer\":\"They help farmers improve management by comparing pig performance and by feeding growth and disease prediction models with individual data. This supports housing and feed planning as well as early disease detection.\"},{\"question\":\"What existing technologies are commonly used to record individual pig data automatically?\",\"answer\":\"Radio Frequency Identification (RFID) tag systems and camera-based methods such as face recognition or position tracking are used to associate measurements with individual pigs.\"},{\"question\":\"How does the proposed method estimate individual-level growth trajectories without identity-linked measurements?\",\"answer\":\"It uses pen-level pig weight time series that are not tied to specific identities and applies machine learning to infer individual-level growth trajectories. Simulated group-level data from individual measurements is used to evaluate prediction accuracy.\"}]","Estimating individual-level pig growth trajectories from group-level weight time series using machine learning | PDF",1785893389,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"estimating-individual-level-pig-growth-trajectories-from-group-level-weight-time-series-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/estimating-individual-level-pig-growth-trajectories-from-group-level-weight-time-series-using-machine-learning/124626/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are individual-level pig growth measurements valuable for farmers?","Question",{"text":75,"@type":76},"They help farmers improve management by comparing pig performance and by feeding growth and disease prediction models with individual data. This supports housing and feed planning as well as early disease detection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What existing technologies are commonly used to record individual pig data automatically?",{"text":80,"@type":76},"Radio Frequency Identification (RFID) tag systems and camera-based methods such as face recognition or position tracking are used to associate measurements with individual pigs.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method estimate individual-level growth trajectories without identity-linked measurements?",{"text":84,"@type":76},"It uses pen-level pig weight time series that are not tied to specific identities and applies machine learning to infer individual-level growth trajectories. 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