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

Artificial Intelligence in Agriculture 2024年第2期12卷 19-28页

作者： Chuanqi Xie Yuji Cang Xizhong Lou Hua Xiao Xing Xu Xiangjun Li Weidong Zhou State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products Institute of Animal Husbandry and Veterinary ScienceZhejiang Academy of Agricultural SciencesHangzhou 310021China College of Information Engineering China Jiliang UniversityHangzhou 310018China College of Hydraulic Engineering Zhejiang Tongji Vocational College of Science and TechnologyHangzhou 311231China

Since determining the weight of pigs during large-scale breeding and production is challenging,using non-contact estimation methods is *** study proposed a novel pig weight prediction method based on a mod-ified 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 *** feature pyramid net-work(FPN)was added to the backbone feature extraction network for multi-scale feature *** channel at-tention 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 in-formation,ultimately improving image segmentation *** 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 *** image features,namely the back area,body length,body width,average depth,and eccentricity,were *** pig depth images were used to build five regression algo-rithms(ordinary least squares(OLS),AdaBoost,CatBoost,XGBoost,and random forest(RF))for weight value *** 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.

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

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