Abstract
Vegetable diseases and pests continue to be a major threat to food security. Manual identification is time-consuming and labor-intensive due to the wide variety of diseases and pests, and it is also prone to errors. In this paper, a novel and integrated deep learning framework based on convolutional neural networks was proposed for leek disease identification using an improved Cycle-GAN network, gated recurrent units, and recurrent convolutional auto-encoders. We collected healthy and diseased leek leaves under realistic conditions for the experimental implementation. We concentrated on the identification of five leek diseases: virus, rust, blight, sclerotium rolfsii, and botrytis cinerea. It is discovered that the proposed method’s evaluation metrics produce an accuracy of 99.58%, a specificity of 99.62%, a sensitivity of 97.96%, a precision of 99.77%, and an f1-score of 98.19%, which is significantly higher than other cutting-edge deep learning models such as ShuffleNet-V3, Inception-V4, and Efficient-B7. Many aspects of the proposed model implementation provide competitive advantages, such as high recognition precision, strong adaptive ability, and good generalization. In conclusion, the proposed system can accurately classify leek diseases, which is very beneficial in increasing the efficiency of plant pathology image analysis.
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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
Abbreviations
- CNN:
-
Convolutional Neural Network
- Cycle-GAN:
-
Cycle Generative Adversarial Network
- R-CAE:
-
Recurrent Convolutional Auto-Encoders
- GRU:
-
Gated Recurrent Unit
- SVM:
-
Support Vector Machine
- PCA:
-
Principal Component Analysis
- KNN:
-
K-nearest Neighbor
- DCC:
-
Deep Continuous Clustering
- BN:
-
Batch Normalization
- GNN:
-
Graph Neural Networks
- CoGAN:
-
Coupled Generative Adversarial Network
- ProGAN:
-
Progressive Generative Adversarial Network
- SAGAN:
-
Self-Attention Generative Adversarial Network
- \({D}_{X}\) :
-
It is the discriminator in the X-domain
- \({D}_{Y}\) :
-
It is the discriminator in the Y-domain
- F :
-
It is the generator in the X-domain
- G :
-
It is the generator in the Y-domain
- \(\Omega\) :
-
It is the mapping space of X → Y
- M :
-
It is the mapping space of Y → Z
- Z :
-
It is the set of several clusters
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Xu, L., Ning, S., Zhang, W. et al. Identification of leek diseases based on deep learning algorithms. J Ambient Intell Human Comput 14, 14349–14364 (2023). https://doi.org/10.1007/s12652-023-04674-x
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DOI: https://doi.org/10.1007/s12652-023-04674-x