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

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....

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Published inJournal of agricultural engineering (Pisa, Italy) Vol. 55; no. 3
Main Authors Xiao, Yiming, Wang, Jianhua, Xiong, Hongyi, Xiao, Fangjun, Huang, Renhuan, Hong, Licong, Wu, Bofei, Zhou, Jinfeng, Long, Yongbin, Lan, Yubin
Format Journal Article
LanguageEnglish
Published Bologna PAGEPress Publications 01.01.2024
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ISSN1974-7071
2239-6268
DOI10.4081/jae.2024.1593

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Abstract 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.
AbstractList 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.
Lychee, a key economic crop in southern Chi, 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 origil, achieving a recognition accuracy of 95.8442%, a 5.58 percentage point improvement.
Author Long, Yongbin
Wang, Jianhua
Xiong, Hongyi
Xiao, Fangjun
Wu, Bofei
Huang, Renhuan
Zhou, Jinfeng
Lan, Yubin
Xiao, Yiming
Hong, Licong
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Snippet Lychee, a key economic crop in southern China, has numerous similar-looking varieties. Classifying these can aid farmers in understanding each variety's growth...
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SubjectTerms Attention mechanism
Classification
Cultivars
Image classification
lychee classification
Receptive field
residual network
Transfer learning
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Title Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network
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