Abstract
To improve the accuracy of plant leaf disease image recognition, a CVT-based image classification algorithm is proposed. The algorithm utilizes Convolutional and Transformer networks for feature extraction and encoding, integrating global and local image features. By introducing the self-attention mechanism of Transformer, the algorithm achieves weather image data classification. Experimental results demonstrate that the CVT-based deep learning algorithm effectively enhances model prediction accuracy, showing promising results in plant leaf disease recognition. The algorithm achieves accurate recognition of five different classes of data, with an accuracy rate as high as 97.78%.
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References
Chen Z, Wu R, Lin Y et al (2022) Plant disease recognition model based on improved YOLOv5. Agronomy 12(2):365
Albattah W, Nawaz M, Javed A et al (2022) A novel deep learning method for detection and classification of plant diseases. Complex Intell Syst 1–18
Vishnoi VK, Kumar K, Kumar B (2021) Plant disease detection using computational intelligence and image processing. J Plant Dis Prot 128:19–53
Bedi P, Gole P (2021) Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network. Artif Intell Agricult 5:90–101
Jadhav SB, Udupi VR, Patil SB (2021) Identification of plant diseases using convolutional neural networks. Int J Inf Technol 13(6):2461–2470
Chowdhury MEH, Rahman T, Khandakar A et al (2021) Automatic and reliable leaf disease detection using deep learning techniques. AgriEngineering 3(2):294–312
Upadhyay SK, Kumar A (2022) A novel approach for rice plant diseases classification with deep convolutional neural network. Int J Inf Technol 1–15
Narmadha RP, Sengottaiyan N, Kavitha RJ (2022) Deep transfer learning based rice plant disease detection model. Intell Autom Soft Comput 31(2)
Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. Advances in neural information processing systems, 30
Chen X, Kamata SI, Zhou W (2021) Hyperspectral image classification based on multi-stage vision transformer with stacked samples. In: TENCON 2021–2021 IEEE region 10 conference (TENCON). IEEE, pp 441–446
Qing Y, Liu W, Feng L (2021) Improved transformer net for hyperspectral image classification. Remote Sens 13(11):2216
Dosovitskiy A, Beyer L, Kolesnikov A et al (2021) An image is worth 16x16 words: transformers for image recognition at scale. In: International conference on learning representations
Wu H, Xiao B, Codella N et al (2021) Cvt: introducing convolutions to vision transformers. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 22–31
Acknowledgements
This work was supported by Domestic Visiting Program for Outstanding Young Teachers in Colleges and Universities (gxgnfx2021154); The Key Project of Natural Science Research in Universities of Anhui Province(2022AH051372); The Key Project of Natural Science Research in Universities of Anhui Province(2023AH052236); Scientific research platform open project of Suzhou University(2022ykf03); Key Scientific Research Project of Suzhou University(2023yzd07); Open Project of Scientific Research Platform of Suzhou University(2022ykf24); The University Synergy Innovation Program of Anhui Province(GXXT-2022-047); The Scientific Research Projects Funded by Suzhou University(2021XJPT50, 2022xhx004, 2022xhx099); The Quality Engineering Project of Colleges and Universities in Anhui Province(2021sx162); Natural Research Science Institute of Anhui Provincial Department of Education(2022AH051379).
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Tian, W., Li, S., Liu, W., Lu, B., Tan, C. (2024). Integrating Global and Local Image Features for Plant Leaf Disease Recognition. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_47
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DOI: https://doi.org/10.1007/978-981-99-7502-0_47
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