A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16

A.Geethanjali; C.Manikanta; A.Rihana; E.Chandu; E.Hanumantha Reddy; A.Durvasulu

A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16

April 2025

Vol-11, Issue-2

Paper ID: 26392

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Electronics and Communication Engineering

Keywords

Plant leaf diseases Deep Neural Networks Image Classifications Pre processing Feature Selection Transfer Learning Precision

Abstract

Plant leaf diseases present a serious threat to global food security, as they reduce both the quality and quantity of agricultural output. Consequently, identifying such diseases promptly and accurately is crucial to preventing crop damage and addressing the rising food demands. Traditional methods often rely on laboratory tests and expert analysis, which can be expensive and not readily available to all farmers. In contrast, modern Deep Neural Networks have shown great promise in image-based classification tasks. This study introduces an automated system for detecting plant diseases, which includes several stages such as image pre-processing, disease classification, feature extraction, and selection. The classification process is enhanced through the application of transfer learning using pre-trained models like VGG16, which improves efficiency and reduces the need for extensive training.

License

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Author Information

#	Name	Institute / Affiliation
1	A.Geethanjali	Sri Venkatesa Perumal College of Engineering and Technology
2	C.Manikanta	Sri Venkatesa Perumal College of Engineering and Technology
3	A.Rihana	Sri Venkatesa Perumal College of Engineering and Technology
4	E.Chandu	Sri Venkatesa Perumal College of Engineering and Technology
5	E.Hanumantha Reddy	Sri Venkatesa Perumal College of Engineering and Technology
6	A.Durvasulu	Sri Venkatesa Perumal College of Engineering and Technology

How to Cite

Use the following formats to cite this article in your research.

APA Style

A.Geethanjali, C.Manikanta, A.Rihana, E.Chandu, Reddy, E.Hanumantha, & A.Durvasulu (2025). A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16. International Journal of Advance Research and Innovative Ideas In Education, 11(2), 3106-3111.

MLA Style

A.Geethanjali, et al. "A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, 2025, pp. 3106-3111.

IEEE Style

A.Geethanjali, C.Manikanta, A.Rihana, E.Chandu, E.Hanumantha Reddy, and A.Durvasulu, "A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, pp. 3106-3111, 2025.

Vancouver Style

A.Geethanjali, C.Manikanta, A.Rihana, E.Chandu, Reddy E.Hanumantha, A.Durvasulu. A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(2):3106-3111.

Harvard Style

A.Geethanjali, C.Manikanta, A.Rihana, E.Chandu, Reddy, E.Hanumantha, & A.Durvasulu (2025) 'A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16', International Journal of Advance Research and Innovative Ideas In Education, 11(2), pp. 3106-3111.

Chicago Style

A.Geethanjali, et al. "A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 3106-3111.

Turabian Style

A.Geethanjali, et al. "A Deep Learning Approach For Plant Leaf Disease Detection Using VGG16." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 3106-3111.