{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T21:38:28Z","timestamp":1777412308584,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T00:00:00Z","timestamp":1694044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Projects of Intergovernmental Cooperation in International Scientific and Technological Innovation","award":["2017YFE0114400"],"award-info":[{"award-number":["2017YFE0114400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig\u2019s back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.<\/jats:p>","DOI":"10.3390\/s23187730","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T10:11:07Z","timestamp":1694081467000},"page":"7730","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Zihao","family":"Liu","sequence":"first","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"},{"name":"Key Laboratory of Breeding Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210031, China"}]},{"given":"Jingyi","family":"Hua","sequence":"additional","affiliation":[{"name":"Key Laboratory of Breeding Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210031, China"},{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Hongxiang","family":"Xue","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"},{"name":"Key Laboratory of Breeding Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210031, China"}]},{"given":"Haonan","family":"Tian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Breeding Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210031, China"},{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China"}]},{"given":"Haowei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.scitotenv.2017.02.186","article-title":"Environmental assessment of the entire pork value chain in catalonia\u2014A strategy to work towards circular economy","volume":"589","author":"Noya","year":"2017","journal-title":"Sci. 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