| 基于深度卷积网络的育肥猪体重估测 |
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| 引用本文： | 张建龙,冀横溢,滕光辉.基于深度卷积网络的育肥猪体重估测[J].中国农业大学学报,2021,26(8):111-119. |
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| 作者姓名： | 张建龙 冀横溢 滕光辉 |
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| 作者单位： | 中国农业大学 水利与土木工程学院, 北京 100083; 农业农村部设施农业工程重点实验室, 北京 10083;中国农业大学 水利与土木工程学院, 北京 100083; 农业农村部设施农业工程重点实验室, 北京 10083; 北京市畜禽健康养殖环境工程技术研究中心, 北京 100083 |
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| 基金项目： | “十三五”国家重点研发计划(2016YFD0700204)；重庆市技术创新与应用发展专项重点项目(cstc2019jscx-gksbX0093) |
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| 摘 要： | 为快速、无应激、准确地获取育肥猪体重数据,采用深度卷积网络对育肥猪体重进行了估测.结果表明:1)在改造后的Xception、MobileNetV2、DenseNet201和ResNet152V2 4种模型中,DenseNet201模型体重估测效果最好,在验证集上估测的相关系数为0.993 9,均方根误差为1.85 kg...
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| 关 键 词： | 育肥猪 体重估测 深度学习 卷积神经网络 |
| 收稿时间： | 2020/10/17 0:00:00 |
Weight estimation of fattening pigs based on deep convolutional network |
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| ZHANG Jianlong,JI Hengyi,TENG Guanghui.Weight estimation of fattening pigs based on deep convolutional network[J].Journal of China Agricultural University,2021,26(8):111-119. |
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| Authors: | ZHANG Jianlong JI Hengyi TENG Guanghui |
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| Institution: | College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Engineering in Structure and Environment of Ministry of Agriculture and Rural Affairs, Beijing 100083, China; College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China; Key Laboratory of Agricultural Engineering in Structure and Environment of Ministry of Agriculture and Rural Affairs, Beijing 100083, China; Beijing Engineering Research Center on Animal Healthy Environment, Beijing 100083, China |
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| Abstract: | In order to obtain the weight data of fattening pigs quickly, accurately and without stress, deep convolutional networks were applied to estimate the weight of fattening pigs. The results showed that: 1)Among the transformed Xception, MobileNetV2, DenseNet201 and ResNet152V2, the pre-trained DenseNet201 model had the best weight estimation effect. On the validation set, the estimated correlation coefficient of the model was 0. 9939, the root mean square error was 1. 85 kg, the mean absolute error was 1. 10 kg, and the mean relative error was 1. 57%. The Densenet201 model was then selected as the fattening pig weight estimation model in this study; 2)The generalization effect of the model was investigated based on the test data. The estimated correlation coefficient of 0. 976 7, the root mean square error of 2. 75 kg, the mean absolute error of 2. 10 kg, and the mean relative error of 3. 03% showed that the performance of the model was good. 3)The mean estimated time of pre-trained DenseNet201 was 0. 16 s, which was much faster than that of the traditional methods. Therefore, the Densenet201 model was more suitable for sorting systems, sow feeding stations and other occasions where the speed of pig weight acquisition was strict. In conclusion, the deep convolution network can be used to quickly estimate the weight of fattening pigs and provides basis for automated, intelligent and unmanned management of pig farms. |
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| Keywords: | fattening pigs weight estimation deep learning convolutional neural network |
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