Research on egg classification and recognition based on residual neural network
Journal Title: Journal of Henan Agricultural University - Year 2024, Vol 58, Issue 3
Abstract
[Objective] This research was conducted to explore the classification performance of residual 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 optimal 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 realtime 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 excellent 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, attention should be paid to recording deformed eggs and soft shell eggs to provide data support for further optimization.
Authors and Affiliations
Xu LIANG, Ling WANG, Shuhan ZHAO
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