Identification of apple leaf diseases based on improved VGG19 network

Yunhe Wang; Shenming Gu

doi:10.1117/12.3045253

9 October 2024 Identification of apple leaf diseases based on improved VGG19 network

Yunhe Wang, Shenming Gu

Proceedings Volume 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024); 132880G (2024) https://doi.org/10.1117/12.3045253
Event: Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 2024, Chengdu, China

Abstract

Apples are well adapted to most climates and are grown all over the world. Some diseases are encountered during the growing of apples.⁰ These disease problems can be manifested through leaf changes. This paper aims to improve the recognition accuracy of various diseases by improving the vgg19 network and transfer learning methods. In this study, we introduce a transfer learning approach and a more stable approach to the optimal solution through a decay strategy of learning rates, utilizing the capabilities of the VGG19 architecture to efficiently classify various apple leaf diseases. The performance of the model was calculated on the verification data set after training, and the accuracy rate was more than 99%. A series of detailed evaluations were made on the test set to confirm the excellent accuracy of the developed model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yunhe Wang and Shenming Gu "Identification of apple leaf diseases based on improved VGG19 network", Proc. SPIE 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 132880G (9 October 2024); https://doi.org/10.1117/12.3045253

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KEYWORDS

Diseases and disorders

Data modeling

Education and training

Machine learning

Matrices

Deep learning

Performance modeling

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