Scalable Plant Disease Detection utilizing VGG based Deep Models

DOI

10.2991/978-94-6239-693-7_62How to use a DOI?

Keywords

Plant Disease; VGG16; VGG19; Sustainable Agriculture; PlantVillage Dataset

Abstract

Plant disease detection is essential for enhancing sustainability in agriculture domain, as it enables early detection of any possible crop loss. It allows to have reduced production loss and chemical dependency. This paper proposes deep architectures utilizing VGG16 and VGG19 for automated plant disease detection. For experimentation, the PlantVillage dataset is used. The emphasis is on enhanced crop production along with low-resource environments. Growing population and lowering fertility of land demands sustainable crop production. The work demonstrates that VGG19 outperforms VGG16 in classification accuracy while maintaining reasonable computational requirements. The proposed approach offers a scalable and environmentally conscious solution named “Deep Green” for real-time plant disease diagnosis, supporting the broader goals of precision farming and food security. Deep is inspired by the deep neural network architecture and Green has emerged from green revolution. This study contributes a deep architecture for reducing crop loss by predicting the disease in advance and allowing the possible solutions providing insight into the anticipated problems.

Copyright

© 2026 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  - Somya R. Goyal
PY  - 2026
DA  - 2026/06/16
TI  - Scalable Plant Disease Detection utilizing VGG based Deep Models
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 627
EP  - 634
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_62
DO  - 10.2991/978-94-6239-693-7_62
ID  - Goyal2026
ER  -