https://doi.org/10.4081/jae.2024.1593

Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network

Vol. 55 No. 3 (2024)
Published: 17 July 2024
Attention mechanism, lychee classification, residual network, transfer learning
Abstract Views: 306
PDF: 115
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Authors

Yiming Xiao
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Jianhua Wang
jhw655@scau.edu.cn
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou; Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou, China.
Hongyi Xiong
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Fangjun Xiao
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Renhuan Huang
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Licong Hong
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Bofei Wu
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Jinfeng Zhou
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou, China.
Yongbin Long
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou; Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou, China.
Yubin Lan
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou; National Center for International collaboration Research on precision Agricultural Aviation Pesticides Spraying Technology, South China Agricultural University, Guangzhou; Guangdong Laboratory for Lingnan Modern Agriculture, South China Agricultural University, Guangzhou, China.

Lychee, a key economic crop in southern China, has numerous similar-looking varieties. Classifying these can aid farmers in understanding each variety's growth and market demand, enhancing agricultural efficiency. However, existing classification techniques are subjective, complex, and costly. This paper proposes a lychee classification method using an improved ResNet-34 residual network for six common varieties. We enhance the CBAM attention mechanism by replacing the large receptive field in the SAM module with a smaller one. Attention mechanisms are added at key network stages, focusing on crucial image information. Transfer learning is employed to apply ImageNet-trained model weights to this task. Test set evaluations demonstrate that our improved ResNet-34 network surpasses the original, achieving a recognition accuracy of 95.8442%, a 5.58 percentage point improvement.

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How to Cite

Xiao, Y. (2024) “Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network”, Journal of Agricultural Engineering, 55(3). doi: 10.4081/jae.2024.1593.
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