{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T08:50:35Z","timestamp":1768294235572,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T00:00:00Z","timestamp":1685232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["32072787"],"award-info":[{"award-number":["32072787"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["LBH-Q21070"],"award-info":[{"award-number":["LBH-Q21070"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["ZDZX202102"],"award-info":[{"award-number":["ZDZX202102"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["19YJXG02"],"award-info":[{"award-number":["19YJXG02"]}]},{"name":"Postdoctoral Science Foundation of Heilongjiang Province","award":["32072787"],"award-info":[{"award-number":["32072787"]}]},{"name":"Postdoctoral Science Foundation of Heilongjiang Province","award":["LBH-Q21070"],"award-info":[{"award-number":["LBH-Q21070"]}]},{"name":"Postdoctoral Science Foundation of Heilongjiang Province","award":["ZDZX202102"],"award-info":[{"award-number":["ZDZX202102"]}]},{"name":"Postdoctoral Science Foundation of Heilongjiang Province","award":["19YJXG02"],"award-info":[{"award-number":["19YJXG02"]}]},{"name":"eilongjiang Bayi Agricultural University Support Program","award":["32072787"],"award-info":[{"award-number":["32072787"]}]},{"name":"eilongjiang Bayi Agricultural University Support Program","award":["LBH-Q21070"],"award-info":[{"award-number":["LBH-Q21070"]}]},{"name":"eilongjiang Bayi Agricultural University Support Program","award":["ZDZX202102"],"award-info":[{"award-number":["ZDZX202102"]}]},{"name":"eilongjiang Bayi Agricultural University Support Program","award":["19YJXG02"],"award-info":[{"award-number":["19YJXG02"]}]},{"name":"Scholar Plan at Northeast Agriculture University","award":["32072787"],"award-info":[{"award-number":["32072787"]}]},{"name":"Scholar Plan at Northeast Agriculture University","award":["LBH-Q21070"],"award-info":[{"award-number":["LBH-Q21070"]}]},{"name":"Scholar Plan at Northeast Agriculture University","award":["ZDZX202102"],"award-info":[{"award-number":["ZDZX202102"]}]},{"name":"Scholar Plan at Northeast Agriculture University","award":["19YJXG02"],"award-info":[{"award-number":["19YJXG02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig\u2019s back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig\u2019s back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.<\/jats:p>","DOI":"10.3390\/s23115156","type":"journal-article","created":{"date-parts":[[2023,5,28]],"date-time":"2023-05-28T15:29:52Z","timestamp":1685287792000},"page":"5156","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Individual Pig Identification Using Back Surface Point Clouds in 3D Vision"],"prefix":"10.3390","volume":"23","author":[{"given":"Hong","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}]},{"given":"Qingda","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China"}]},{"given":"Qiuju","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China"},{"name":"Key Laboratory of Swine Facilities Engineering, Ministry of Agriculture, Harbin 150030, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100429","DOI":"10.1016\/j.animal.2021.100429","article-title":"Review: Precision livestock larming technologies in pasture-based livestock systems","volume":"16","author":"Aquilani","year":"2022","journal-title":"Animal"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105826","DOI":"10.1016\/j.compag.2020.105826","article-title":"A systematic literature review on the use of machine learning in precision livestock farming","volume":"179","author":"Aguilar","year":"2020","journal-title":"Comput. 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