河南农业大学学报2024，Vol.58Issue(3)：456-466,11.DOI:10.16445/j.cnki.1000-2340.20240424.002
基于残差神经网络的鸡蛋分类识别研究
Research on egg classification and recognition based on residual neural network
摘要
Abstract
[Objective]This research was conducted to explore the classification performance of residu-al neural networks(ResNet)on different types of eggs,clarify the feasibility of applying deep learning in intelligent egg inspection devices,provide new ideas for the intelligent process of poultry farming,and provide data support for egg classification research.[Method]Field sampling was conducted in the chicken coop,and an adaptive moment estimation optimizer(Adam)was used to train three transfer learning strategies:fine-tuning the last layer,fine-tuning all layers,and retraining all layers.The opti-mal classification model was trained by adjusting the model weight parameters and changing the learning rate.[Result]An egg classification model was obtained with a recognition accuracy of up to 98.971％.The various evaluation indicators of the model on the dataset was calculated,and confusion matrix and semantic feature dimensionality reduction visualization was used to analyze the categories and semantics that are prone to misjudgment in egg classification recognition.The model has good real-time performance after deployment and can meet practical needs.[Conclusion]The lighting conditions are a key influencing factor in the classification and recognition of eggs,and the lighting in the chicken coop should be kept stable and balanced as much as possible.For six types of eggs,the best model can be obtained by fine-tuning all layers and adjusting the learning rate parameter to 0.6.It has excel-lent classification performance in chicken coop scenes,especially in color semantics.When applied to intelligent egg inspection devices,it can effectively reduce labor costs.In subsequent research,atten-tion should be paid to recording deformed eggs and soft shell eggs to provide data support for further optimization.关键词
鸡蛋分类/家禽养殖/残差神经网络/学习率/智慧农业/迁移学习Key words
egg classification/poultry farming/residual neural network/learning rate/smart agricul-ture/transfer learning分类
农业科技引用本文复制引用
梁旭,王玲,赵书涵..基于残差神经网络的鸡蛋分类识别研究[J].河南农业大学学报,2024,58(3):456-466,11.基金项目
河南省科技攻关项目(232102321022,232102110284) （232102321022,232102110284）
河南省高等学校青年骨干教师培养计划(2020GGJS046) （2020GGJS046）
