摘要
家蚕日龄的准确识别有助于精准饲喂和动物福利,因此为准确识别家蚕生长时期中3龄第1天至5龄第7天,共14个日龄,在实际环境下采集特定家蚕品种,构建以14个日龄为单位的数据集。提出一种基于改进残差神经网络的Moga-ResNet,该方法在经典残差神经网络ResNet50的基础上,引入多阶门控机制以获取日龄图像的显著性特征。通过在同一个家蚕日龄数据集上开展模型训练与测试得到,Moga-ResNet的识别准确率为96.57%,F1值为96.57%,召回率为96.62%,与Swin Transformer、MobileNet v3、CSPNet和DenseNet四个经典模型的评价指标相比,Moga-ResNet在家蚕的日龄识别中具有较强的识别能力,可以为开展家蚕精准饲喂和数字化管理相关工作提供基础。
Accurate identification of silkworm day-age contributes to precise feeding and animal welfare.In order to accurately identify 14 day-age from the first day of age 3 to the seventh day of age 5 in the growth period of silkworm,this paper constructs a dataset with 14 day-age units by collecting specific silkworm species in a real environment.This paper proposes Moga-ResNet method based on an improved residual neural network.This method introduces a multi-order gating mechanism to obtain the saliency features of day-age images based on the classical residual neural network ResNet50.Through model training and testing on the same domestic silkworm day-age dataset,the recognition accuracy of Moga-ResNet is 96.57%,the F 1 value is 96.57%,and the recall rate is 96.62%.Compared with the evaluation indexes of four classic models,including Swin Transformer,MobileNet v3,CSPNet and DenseNet,Moga-ResNe achieved a stronger recognition ability in day-age recognition of domestic silkworm,which can provide a foundation for carrying out work related to precise feeding and digital management.
作者
田丁伊
石洪康
祝诗平
陈肖
张剑飞
Tian Dingyi;Shi Hongkang;Zhu Shiping;Chen Xiao;Zhang Jianfei(College of Engineering and Technology,Southwest University,Chongqing,400700,China;Yibin Academy of Southwest University,Yibin,644000,China;Sericultural Research Institute,Sichuan Academy of Agricultural Sciences,Nanchong,637000,China)
出处
《中国农机化学报》
北大核心
2024年第2期259-266,共8页
Journal of Chinese Agricultural Mechanization
基金
四川省自然科学基金资助项目(2023NSFSC0498)
南充市科技计划项目(22YYJCYJ0009)。
关键词
家蚕
日龄识别
多阶门控机制
残差神经网络
silkworm
day-age identification
multi-order gating mechanism
residual neural network
