Wheat Disease Detection Using Transfer Learning Techniques

DOI

10.2991/978-94-6463-471-6_3How to use a DOI?

Keywords

Transfer Learning; wheat leaf disease detection; Convolution Neural Networks (CNN); Deep Neural Networks (DNN); TL architectures (ResNet50; VGG19)

Abstract

Wheat stands as a crucial staple crop for a substantial portion of the global population, contributing significantly to food security. However, the productivity and expansion of wheat cultivation face substantial challenges due to the prevalence of diseases, resulting in considerable annual crop losses. Nowadays, deep learning methods have become major in the identification of leaf diseases. The study proposes the techniques that mainly concentrating on transfer learning (TL) architectures, to advance agricultural research. Various TL architectures, such as VGG16, ResNet50, Squeeze Net, and VGG19, are explored for disease detection in wheat plants. The methodology involves preprocessing of leaf images, utilizing TL architectures to extract the features of the leaf. Subsequently, TL architectures are fine-tuned using these segmented images, and the fully connected layers of the combined architecture of VGG19 and RESNET50 are employed for disease classification. The model focuses on all diseases caused by fungi and bacteria in wheat plants. The analysis confirms that the developed model outperforms existing counterparts, highlighting its efficacy in advancing wheat leaf disease detection. This project contributes to empowering farmers with innovative tools for accurate and early disease detection, ultimately safeguarding wheat crop yield and quality.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - J. Avanija
AU  - Boddiga Sai Keerthi
AU  - Balla Vijay
AU  - Chevireddy Hemasree Reddy
AU  - Bala Naveen Kumar Yadav
AU  - Mohammad Gouse Galety
PY  - 2024
DA  - 2024/07/30
TI  - Wheat Disease Detection Using Transfer Learning Techniques
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 21
EP  - 29
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_3
DO  - 10.2991/978-94-6463-471-6_3
ID  - Avanija2024
ER  -