https://doi.org/10.4081/jae.2024.1593
Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network
Published: 17 July 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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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., Wang, J., Xiong, H., Xiao, F., Huang, R., Hong, L., Wu, B., Zhou, J., Long, Y. and Lan, Y. (2024) “Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1593.
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