Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250600127-8.doi: 10.11896/jsjkx.250600127

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

LitchiNet:Lightweight Litchi Variety Recognition Network with Fused Multi-scale Gated Attention and Class Imbalance Awareness

SU Ye1,2, XU Xin3, ZHAO Longlong1, LI Xiaoli1, CHEN Pan1, CHEN Jinsong1   

  1. 1 Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,Guangdong 518055,China
    2 School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 101407,China
    3 School of Computer Science,Central China Normal University,Hubei 430000,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:SU Ye,born in 2001,postgraduate,is a member of CCF(No.K5190G).His main research interests include AutoML,feature selection,ensemble lear-ning,smart agriculture,and intelligent remote sensing interpretation and applications.
    ZHAO Longlong,born in 1988,Ph.D,associate researcher.Her main research interests include artificial intelligence,machine learning,smart agriculture,and intelligent remote sensing interpretation and applications.
  • Supported by:
    National Natural Science Foundation of China(42171323) and Research Foundation of Shenzhen Science and Technology Innovation Bureau(KCXFZ20240903093800002).

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Abstract: Accurate and efficient recognition of litchi varieties is essential for intelligent postharvest quality assessment.How-ever,existing deep learning models face several challenges in this task,including fine-grained feature discrimination,limited sample numbers,class imbalance,and constraints on deployment resources.To tackle these challenges,a lightweight litchi variety recognition model,named LitchiNet is proposed.LitchiNet adopts a pretrained SqueezeNet1.0 as its backbone and integrates a novel Multi-Scale Gated Attention(MSGA) module.By combining multi-scale convolutional branches,channel attention,and a lightweight gating mechanism within a residual framework,MSGA enhances the model's ability to capture subtle inter-class diffe-rences and emphasize key feature regions.In the final stage of LitchiNet,a computationally efficient classifier structure is designed to ensure high inference speed and deployment friendliness.To further tackle class imbalance,LitchiNet introduces a Class Imba-lance Awareness Loss(CIA Loss) that incorporates both class weighting and a difficulty-aware modulation term,enabling more robust learning from minority classes.Experiments on a public litchi variety dataset demonstrate that LitchiNet achieves excellent performance,reaching a recall of 99.40% and outperforming four state-of-the-art lightweight models across all metrics.With only 3.210×106 parameters,the model is well-suited for edge deployment.Comparative experiments with four state-of-the-art attention modules further reveal that the inclusion of MSGA leads to faster convergence,lower final loss,and better recognition accuracy.Moreover,the modular design of LitchiNet ensures compatibility with various backbone networks,offering strong generalizability and scalability.LitchiNet provides a practical and effective solution for fine-grained litchi variety recognition,and contri-butes a novel approach to lightweight agricultural AI applications.

Key words: Litchi, Variety recognition, Image processing, Deep learning, Class imbalance

CLC Number: 

  • TP391
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