Article(id=1325194211858985128, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194116245619276, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2026.02.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1773072000000, receivedDateStr=2026-03-10, revisedDate=1776182400000, revisedDateStr=2026-04-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1793880521024, onlineDateStr=2026-11-05, pubDate=1778774400000, pubDateStr=2026-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793880521024, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793880521024, creator=13701087609, updateTime=1793880521024, updator=13701087609, issue=Issue{id=1325194116245619276, tenantId=1146029695717560320, journalId=1319690209941618849, year='2026', volume='7', issue='2', pageStart='1', pageEnd='200', issueExtLink='null', onlineDate='null', pubDate='1778774400000', pubDateStr='2026-05-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793880498229, creator='13701087609', updateTime=1793943300582, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325457528724795795, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194116245619276, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325457528724795796, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325194116245619276, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=193, endPage=200, ext={EN=ArticleExt(id=1325194212051923113, articleId=1325194211858985128, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Research on body weight prediction of cage-raised meat ducks based on a tree-based machine learning model, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=
To address difficulties and inefficiencies in monitoring duck weight during the routine inspection of cage-raised meat ducks, this study proposes a precise body weight prediction method utilizing a tree-based machine learning algorithm, aiming to enhance the stability and accuracy of weight prediction in complex scenarios of large-scale meat duck production. Unilateral body size and age data were manually collected from meat ducks at Shuyang Xinfeng Future Farm to develop an extreme gradient boosting model predicting weight. The model integrated morphometric features and chronological age as predictors to evaluate their respective impacts on body weight estimation. The results indicate a distinct growth trajectory: for ducks 1-13 days, data collection is hindered by their small body size, and weight growth is relatively slow, making this period less critical for weight prediction modeling. Conversely, ducks aged 14 to 35 days undergo a rapid growth phase, representing a critical period in the production cycle during which age and unilateral body size serve as pivotal predictors. The Extreme Gradient Boosting Tree model effectively predicted the weight of meat ducks, achieving an optimal R2 of 0.95, a minimum mean squared error (MSE) of 0.563 kg2, and a minimum mean absolute error (MAE) of 0.044 kg. Furthermore, by evaluating the importance of unilateral body size data of meat ducks, it is concluded that body slant length, as the main parameter of meat duck development, contributes the most to the model among body size data, accounting for 20.7% (second only to age) while tibia length exhibited the weakest correlation, contributing only 4.8%. This study not only deepens the understanding of machine learning application in meat duck weight prediction but also provides a valuable method for selection and management in non-contact meat duck weight analysis scenarios. Additionally, it offers technical support for improving model accuracy and promoting the development of intelligent cage-raised meat duck farming, and provides an effective automated solution for weight monitoring via inspection robots.
, authors=Pengjun Wang
1, 4, Yongsheng Chen
1, 2, 4, Lei Chen
3, Aibing Wu
1, 2, 4, Zhizhong Shi
3, Mingjiang Chen
1, 4, authorsList=Pengjun Wang, Yongsheng Chen, Lei Chen, Aibing Wu, Zhizhong Shi, Mingjiang Chen, authorCompany=null, correspAuthors=Yongsheng Chen, authorNote=
, correspAuthorsNote=
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1325194212278415530, articleId=1325194211858985128, tenantId=1146029695717560320, journalId=1319690209941618849, language=CN, title=基于树形机器学习模型的笼养肉鸭体重预测研究, columnId=0, journalTitle=智能化农业装备学报（中英文）, columnName=, runingTitle=null, highlight=null, articleAbstract=
针对笼养肉鸭养殖巡检过程中鸭只体重监测困难且效率低下等问题，研究提出一种基于树形机器学习算法的精准体重预测方法，意在提升规模化肉鸭养殖复杂场景下体重预测的稳定性与准确性。本研究基于人工采集肉鸭单侧体尺数据及日龄信息，应用极端梯度提升树模型预测肉鸭体重，以沭阳鑫丰未来牧场肉鸭体重进行预测，模型采用体尺特征与日龄相结合的预测因子进行开发，探讨不同体尺特征及日龄对体重预测的影响。结果表明，肉鸭在1~13日龄，由于体尺较小，数据不易采集，且此阶段体重增长不快，可不作为体重预测的关键指标。14~35日龄肉鸭体重存在快速增长的过程，此阶段为肉鸭养殖周期内的关键时期，日龄和单侧体尺参数在此期间对体重预测具有十分重要的作用。极端梯度提升树能够有效预测肉鸭体重，其中模型结果R2最优为0.95，均方误差最小为0.563 kg2，平均绝对误差最小为0.044 kg。此外，通过评估肉鸭单侧体尺数据重要性，得出体斜长作为肉鸭发育的主要参数，仅次于日龄参数占比20.7%，在体尺数据中对模型贡献最大，而胫长仅占4.8%，贡献最小。本研究不仅加深了机器学习在肉鸭体重预测中的应用理解，为无接触式肉鸭体重分析情况下选择和管理提供了一种有价值的方法，也为提高模型的准确性及推动笼养肉鸭智能化养殖发展提供了技术支撑，还为巡检机器人肉鸭体重监测的实际应用提供了有效技术方案。
, authors=王鹏军
1, 4, 陈永生
1, 2, 4, *, *, 陈雷
3, 吴爱兵
1, 2, 4, 史志中
3, 陈明江
1, 4, authorsList=王鹏军, 陈永生, 陈雷, 吴爱兵, 史志中, 陈明江, authorCompany=null, correspAuthors=陈永生, authorNote=
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1, 4, address=
1.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
4.Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China, bio={"content":"
Wang Pengjun, E-mail: wangpengjun@caas.cn
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Wang Pengjun, E-mail: wangpengjun@caas.cn
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1, 4, address=
1.农业农村部南京农业机械化研究所，江苏 南京，210014
4.农业农村部现代农业装备重点实验室，江苏 南京，210014, bio={"content":"
王鹏军，男，1986年生，河北唐县人，硕士研究生，助理研究员；研究方向为畜禽智能养殖装备与技术。E-mail：wangpengjun@caas.cn
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王鹏军，男，1986年生，河北唐县人，硕士研究生，助理研究员；研究方向为畜禽智能养殖装备与技术。E-mail：wangpengjun@caas.cn
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1, 2, 4, address=
1.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
2.Western research institute, Chinese Academy of Agricultural Sciences, Changji 831100, China
4.Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1325194214975353027, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194211858985128, authorId=1325194214824358078, language=CN, stringName=陈永生, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
1, 2, 4, *, *, address=
1.农业农村部南京农业机械化研究所，江苏 南京，210014
2.中国农业科学院西部农业研究中心，新疆 昌吉，831100
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1.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China), AuthorCompanyExt(id=1325194212530073773, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194211858985128, companyId=1325194212513296555, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
1.农业农村部南京农业机械化研究所，江苏 南京，210014)]), AuthorCompany(id=1325194212605571246, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194211858985128, xref=2., ext=[AuthorCompanyExt(id=1325194212613959855, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194211858985128, companyId=1325194212605571246, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
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3, address=
3.Jiangsu Provincial Agricultural Machinery Technology Promotion Station, Nanjing 211000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1325194215180873928, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194211858985128, authorId=1325194215038267589, language=CN, stringName=陈雷, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
3, address=
3.江苏省农业机械技术推广站，江苏 南京，211000, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1325194212785926321, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194211858985128, xref=3., ext=[AuthorCompanyExt(id=1325194212798509234, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194211858985128, companyId=1325194212785926321, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
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1, 2, 4, address=
1.Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
2.Western research institute, Chinese Academy of Agricultural Sciences, Changji 831100, China
4.Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1325194215411560655, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325194211858985128, authorId=1325194215247982794, language=CN, stringName=吴爱兵, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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1.农业农村部南京农业机械化研究所，江苏 南京，210014
2.中国农业科学院西部农业研究中心，新疆 昌吉，831100
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