数字农科院2.0

Research on the Classification Method of Fresh Tobacco Leaf Maturity Based on Transfer Learning and Multi-Feature Fusion

文献类型： 外文期刊

作者： Zhao, Panzhen;Dai, Yingpeng

作者机构：

关键词： Transfer Learning;Feature Selection;Feature Fusion;Classification Model;Smart Agriculture

期刊名称： 2025 40TH YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION, YAC

ISSN： 2837-8601

年卷期： 2025 年

页码：

收录情况： EI（2025版） ; ; CSCD（2025-2026年度） ; ; ESCI（2025版）

摘要： Addressing the challenges of diversity, variability, and insufficient generalization in identifying the maturity of fresh tobacco leaves in field conditions, this study proposes a classification method based on transfer learning and multi feature fusion. Initially, the EfficientNetBO model is employed for transfer learning, efficiently extracting deep features of fresh tobacco leaves by freezing certain layers and fine-tuning the higher-level network. Subsequently, traditional features such as color, texture, and pixel characteristics are extracted, and features significantly correlated with maturity are selected through correlation analysis. Finally, a dynamic feature weighting mechanism is introduced to adaptively fuse deep features with traditional features, optimizing weight allocation to enhance model performance. The results demonstrate that the model achieves a 91.87% accuracy in fresh tobacco leaf maturity recognition, significantly improving the accuracy and generalization ability of maturity identification. This method provides an efficient and robust solution for recognizing tobacco leaf maturity in complex field environments and explores its application in automatic harvesting systems, promoting the advancement of intelligent agricultural technology.

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