The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks
Open accessAbstract
In the field of agricultural information, the automatic identification and diagnosis of maize leaf diseases is highly desired. To improve the identification accuracy of maize leaf diseases and reduce the number of network parameters, the improved GoogLeNet and Cifar10 models based on deep learning are proposed for leaf disease recognition in this paper. Two improved models that are used to train and test nine kinds of maize leaf images are obtained by adjusting the parameters, changing the pooling combinations, adding dropout operations and rectified linear unit functions, and reducing the number of classifiers. In addition, the number of parameters of the improved models is significantly smaller than that of the VGG and AlexNet structures. During the recognition of eight kinds of maize leaf diseases, the GoogLeNet model achieves a top - 1 average identification accuracy of 98.9%, and the Cifar10 model achieves an average accuracy of 98.8%. The improved methods are possibly improved the accuracy of maize leaf disease, and reduced the convergence iterations, which can effectively improve the model training and recognition efficiency.
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Cites
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Cited by
Jiang, P., Chen, Y., Liu, B., He, D., & Liang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2914929 · Cited by 892
Sharma, P., Berwal, Y. P. S., & Ghai, W. (2020) · Information Processing in Agriculture · doi:10.1016/j.inpa.2019.11.001 · Cited by 361
Saleem, M. H., Potgieter, J., & Arif, K. M. (2020) · Plants · doi:10.3390/plants9101319 · Cited by 259
Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion
Zhou, G., Zhang, W., Chen, A., He, M., & Ma, X. (2019) · IEEE Access · doi:10.1109/access.2019.2943454 · Cited by 253
Towards lightweight deep neural network for smart agriculture on embedded systems
Du, P., Polonelli, T., Magno, M., & Cheng, Z. (2022) · 2022 IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas54819.2022.9881382 · Cited by 5
Metadata
- Authors
- Zhang, X., Qiao, Y., Meng, F., Fan, C., & Zhang, M.
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 30370-30377
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2805772477
- Cited by
- 656
- References
- 7
- FWCI
- 42.89
- Type
- journal article
- Language
- en
- Open access
- gold
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- Zhang, X., Qiao, Y., Meng, F., Fan, C., & Zhang, M. (2018). Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks. IEEE Access. https://doi.org/10.1109/access.2018.2844405
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