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A Machine Learning-Based Method for Pig Weight Estimation and the PIGRGB-Weight Dataset

文献类型： 外文期刊

作者： Ji, Xintong 1 ; Li, Qifeng 2 ; Guo, Kaijun 1 ; Ma, Weihong 2 ; Li, Mingyu 2 ; Xu, Zhankang 2 ; Yang, Simon X. 4 ; Ren, Zhiyu 2 ;

作者机构： 1.Beijing Univ Agr, Coll Anim Sci & Technol, Beijing 100096, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Natl Innovat Ctr Digital Technol Anim Husb, Beijing 100097, Peoples R China

4.Univ Guelph, Sch Engn, Adv Robot & Intelligent Syst Lab, Guelph, ON N1G 2W1, Canada

关键词： machine learning; pig weight estimation; pig dataset

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 8 期

页码：

收录情况： SCI

摘要： Traditional pig weighing methods are costly, require driving pigs onto electronic scales, and cannot collect real-time data without interference. Pig weight estimation using deep learning often demands significant computational resources and lacks real-time capabilities, highlighting the need for a more efficient method. To overcome these challenges, this study proposes a machine learning-based approach for real-time pig weight estimation by extracting image features. The method reduces computational demands while maintaining high accuracy. The SAM2-Pig model is employed for instant segmentation of pig RGB images to extract features such as relative projection area, body length, and body width, which are crucial for accurate weight prediction. Regression models, including the BPNN with Trainlm, are used to predict pig weight based on the extracted features, achieving the best performance in our experiments. This study demonstrates that machine learning methods using RGB image features provide accurate and adaptable results, offering a viable solution for real-time pig weight estimation. This study also publicly releases the PIGRGB-Weight dataset, consisting of 9579 RGB images of pigs in a free-moving state, annotated with weight information, enabling future research and model testing. The method demonstrates remarkable stability, low computational demand, and practical applicability, making it a lightweight and effective approach for estimating pig weight in real time.

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