Identification of citrus fruit infected with Huanglongbing based on Mixup algorithm and convolutional neural network

LU Jianqiang; LIN Jiahan; HUANG Zhongqiang; WANG Weixing; QIU Hongbin; YANG Ruifan; CHEN Pingfu

doi:10.7671/j.issn.1001-411X.202008041

Journal of South China Agricultural University > 2021 > 42(3): 94-101. > DOI: 10.7671/j.issn.1001-411X.202008041

LU Jianqiang, LIN Jiahan, HUANG Zhongqiang, et al. Identification of citrus fruit infected with Huanglongbing based on Mixup algorithm and convolutional neural network[J]. Journal of South China Agricultural University, 2021, 42(3): 94-101. DOI: 10.7671/j.issn.1001-411X.202008041

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Identification of citrus fruit infected with Huanglongbing based on Mixup algorithm and convolutional neural network

1.
College of Electronic Engineering/College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
2.
Guangdong Provincial Laboratory of Lingnan Modern Agricultural Science and Technology, Guangzhou 510642, China
3.
Guangdong Agricultural Information Monitoring Engineering Technology Research Center, Guangzhou 510642, China

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Received Date: August 29, 2020
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
The traditional image recognition method relies on manual design features, is time-consuming and labor-intensive, has large number of network model parameter and has low recognition accuracy rate. The goal was to solve these problems in traditional method for identifying citrus fruit infected with Huanglongbing.

Method
Firstly, we collected the images of citrus fruit with Huanglongbing, and performed data enhancement modes such as flip, rotation, affine, and Gaussian disturbance. Further, we used the Mixup algorithm to establish a linear relationship between samples to enhance the robustness of the model for identifying data samples. Then, we transfered the prior knowledge on the ImageNet data set of Xception network, and proposed a citrus Huanglongbing fruit recognition model of X-ResNeXt model based on Mixup algorithm and convolutional neural network. Finally, the momentum gradient descent optimization method was used to reduce the impact of shocks and effectively accelerate the convergence of the model to the local optimum.

Result
The accuracy rate of the X-ResNeXt model trained on the data set after data enhancement was 91.38%. After optimization with transfer learning, the training time reduced by 432 s, and the accuracy rate of the model increased to 91.97%. Combined with the enhancement of Mixup mixed data for further training, the accuracy rate of the model improved to 93.74%. Finally, the momentum gradient descent method was used to optimize the model convergence, and the final model accuracy rate reached 94.29%, which was 3.98% and 1.51% higher than Inception-V3 and Xception networks, respectively.

Conclusion
In the case of a small amount of data, reducing the complexity of the model and transfering existing prior knowledge will help to improve the performance of the model. The Mixup mixed data enhancement method is beneficial to improve the adaptability of the model to identify image samples of citrus fruit with Huanglongbing and improve model performance. The X-ResNeXt model is superior to the classic recognition model in terms of accuracy rate and recall rate, and can provide references for the high-precision, rapid and non-destructive identification of citrus Huanglongbing.
- citrus Huanglongbing,
- Mixup algorithm,
- gradient descent,
- convolutional neural network,
- Xception network

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References

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