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A novel approach based on a modified mask R-CNN for the weight prediction of live pigs

文献类型： 外文期刊

作者： Xie, Chuanqi 1 ; Cang, Yuji 2 ; Lou, Xizhong 2 ; Xiao, Hua 3 ; Xu, Xing 1 ; Li, Xiangjun 2 ; Zhou, Weidong 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Anim Husb & Vet Sci, State Key Lab Managing Biot & Chem Threats Qual &, Hangzhou 310021, Peoples R China

2.China Jiliang Univ, Coll Informat Engn, Hangzhou 310018, Peoples R China

3.Zhejiang Tongji Vocat Coll Sci & Technol, Coll Hydraul Engn, Hangzhou 311231, Peoples R China

关键词： Deep learning; Modified mask R-CNN; Image processing; Pig weight; Prediction

期刊名称：ARTIFICIAL INTELLIGENCE IN AGRICULTURE （ 影响因子：12.4； 五年影响因子：12.7 ）

ISSN：

年卷期： 2024 年 12 卷

页码：

收录情况： SCI

摘要： Since determining the weight of pigs during large-scale breeding and production is challenging, using noncontact estimation methods is vital. This study proposed a novel pig weight prediction method based on a modified mask region-convolutional neural network (mask R-CNN). The modified approach used ResNeSt as the backbone feature extraction network to enhance the image feature extraction ability. The feature pyramid network (FPN) was added to the backbone feature extraction network for multi-scale feature fusion. The channel attention mechanism (CAM) and spatial attention mechanism (SAM) were introduced in the region proposal network (RPN) for the adaptive integration of local features and their global dependencies to capture global information, ultimately improving image segmentation accuracy. The modified network obtained a precision rate (P), recall rate (R), and mean average precision (MAP) of 90.33%, 89.85%, and 95.21%, respectively, effectively segmenting the pig regions in the images. Five image features, namely the back area, body length, body width, average depth, and eccentricity, were investigated. The pig depth images were used to build five regression algorithms (ordinary least squares (OLS), AdaBoost, CatBoost, XGBoost, and random forest (RF)) for weight value prediction. AdaBoost achieved the best prediction result with a coefficient of determination (R 2 ) of 0.987, a mean absolute error (MAE) of 2.96 kg, a mean square error (MSE) of 12.87 kg 2 , and a mean absolute percentage error (MAPE) of 8.45%. The results demonstrated that the machine learning models effectively predicted the weight values of the pigs, providing technical support for intelligent pig farm management. (c) 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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