Abstract
Crop disease image recognition is crucial for early detection and prevention of diseases in agriculture. With the development of deep learning technology, accurate identification can be achieved with large datasets. However, obtaining images of crop leaf diseases is challenging, and the use of Siamese networks can achieve good results with a small number of labeled samples, making it a suitable alternative to other deep learning algorithms. This paper explores the effectiveness of Siamese networks in crop disease image recognition of Northern Shaanxi area. We focuses on leaf diseases of three representative crops, including millet, apple, and jujube. By comparing Siamese network models based on ResNet34 and EfficientNetB0 with six traditional convolutional neural networks, such as AlexNet, VGGNet, GoogleNet, ResNet, DenseNet and MobileNet. The experimental results show that the Siamese network with ResNet34 as the backbone achieved highest accuracy on the millet leaf, apple leaf, and jujube leaf datasets, with accuracies of 99.2%, 99.8%, and 100% respectively.The results confirmed the effectiveness of the proposed method in improving disease recognition accuracies of crops in northern Shaanxi, which can have significant implications for agricultural production in the region.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zong, C.: Influence of green prevention and control technology on tobacco pests and insect pests and economic benefits (2021)
Su, T.: Research on Image Recognition of Crop Leaf Diseases Based on Transfer Learning. Shandong Agricultural University (2020). https://doi.org/10.27277/d.cnki.gsdnu.2020.000557
Yue, Y., Li, X., Zhao, H., Wang, H.: Image recognition of crop diseases based on improved VGG network. J. Agricult. Mechaniz. Res. 44(06), 18–24 (2022). https://doi.org/10.13427/j.cnki.njyi.2022.06.003
Wu, Z., Zhao, J., Hu, X.: Plant disease detection based on feature fusion and SVM. Comput. Program. Skills Mainten. 2022(02), 39–41 (2022). https://doi.org/10.16184/j.cnki.comprg.2022.02.052
Tian, Y., Li, T., Li, C., Park, Z., Sun, G., Wang, B.: Image recognition method of grape disease based on support vector machine. Trans. Chin. Soc. Agricult. Eng. 2007(06), 175–180 (2007)
Jin, H., Song, K.: Application of decision tree algorithm in agricultural disease diagnosis. Contemp. Agricult. Machin. 202(05), 76–77 (2007)
Sun, J., Tan, W., Mao, H., Wu, X., Chen, Y., Wang, L.: Identification of multiple plant leaf diseases based on improved convolutional neural network. Trans. Chin. Soc. Agricult. Eng. 33(19), 209–215 (2017)
Tie, J., Luo, J., Zheng, L., Mo, H., Long, J.: J. South-Central Univ. Nat. (Nat. Sci. Edn.) 40(06), 621–630 (2021)
Wang, L., Zhang, B, Yao, J., Yang, Z., Zhang, J., Fan, X.: Potato leaf disease identification and spot detection based on convolutional neural network. Chinese J. Agricult. Mechan. 42(11), 122–129 (2021). https://doi.org/10.13733/j.jcam.issn.2095-5553.2021.11.19
Liu, Y., Lei, Y., Fan, J., Wang, F., Gong, Y., Tian, Q.: Review of image classification technology based on small sample learning. Acta Automat. Sinica 47(02), 297–315 (2021). https://doi.org/10.16383/j.aas.c190720
Huo, S.: Face recognition and effect analysis using siamese network. Mod. Comput. 28(20), 71–74, 83 (2022). https://doi.org/10.3969/j.issn.1007-1423.2022.20.015
You, Q., Li, G.: Offline handwritten signature authentication algorithm based on Siamese network. Comput. Appl. 1–5. http://kns.cnki.net/kcms/detail/51.1307.tp.20230320.1633.004.html. Accessed 21 Apr 2023
Heavy, Y., Lu, T., Du, Y., Shi, D.: Deepfake video detection method based on i_ResNet34 model and data enhancement. Comput. Sci. 48(07), 77–85 (2021)
Chen, X., et al.: Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions. Cancer Medicine (2023)
Li, Y., Zhu, Y., Liu, P., Zhang, L.: Research on millet disease based on image recognition. Food Sci. Technol. Econ. 44(03), 94–96 (2019). https://doi.org/10.16465/j.gste.cn431252ts.20190324
He, J., Shi, Y., Liu, B., He, D.: Research on external quality grading method of Fuji apple based on DXNet model. Trans. Chinese Soc. Agricult. Mach. 52(07), 379–385 (2021)
Zhang, S., Huang, W.S., You, S.: Disease identification method of winter jujube based on Internet of Things and deep convolutional neural network. Zhejiang J. Agricult. Sci. 29(11), 1868–1874 (2017)
Rubén, A., Fernando, H., Christoph, S.: ReLU neural network Galerkin BEM. J. Sci. Comput. 95(2) (2023)
Liu, W., Meng, R., Qu, H., Liu, L.: Research on music genre recognition based on enhanced AlexNet. J. Intell. Syst. 15(4), 8 (2020)
Kawakura, S., Shibasaki, R.: Suggestions of a deep learning based automatic text annotation system for agricultural sites using GoogLeNet inception and MS-COCO. J. Image Graph. 8(4), 120–125 (2020)
Ji, X., et al.: Research and evaluation of the allosteric protein-specific force field based on a pre-training deep learning model. J. Chem. Inf. Model. (2023)
Acknowledgement
This work is supported in part by National Natural Science Foundation of China under grant No. 61902339, by the Natural Science Basic Research Plan in Shaanxi Province of China under grants No. 2021JM-418, by Yan’an Special Foundation for Science and Technology (2019-01, 2019-13).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
He, J., Zhang, J., Sun, Y., Yang, L. (2024). Disease Recognition of Plants Leaves in Northern Shaanxi Based on Siamese Networks. In: Li, J., Zhang, B., Ying, Y. (eds) 6GN for Future Wireless Networks. 6GN 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-031-53404-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-53404-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53403-4
Online ISBN: 978-3-031-53404-1
eBook Packages: Computer ScienceComputer Science (R0)