Plant Leaf Disease Detection using VGG16

Ms Jami Kavitha , Mrs Dunna Taraka Sandeepa

Convolutional Neural Networks (CNN), transfer learning, VGG16, crop management

Agriculture plays a crucial role in our lives and is the backbone of our economy. Effective management can lead to increased profitability in agricultural production. However, farmers often lack the expertise to identify leaf diseases, resulting in reduced yields. Detecting and classifying plant leaf diseases is vital since production outcomes directly affect profits and losses. Convolutional Neural Networks (CNNs) offer a solution for this issue. This research focuses on identifying diseases in the leaves of apple, grape, corn, potato, and tomato plants. By monitoring large crop fields and automatically detecting disease features, timely medical treatment can be administered. The proposed deep CNN model is compared with popular transfer learning methods like VGG16. The detection of plant leaf diseases has numerous applications, including in biological research and agricultural institutions. This research is essential as it facilitates the monitoring of extensive crop fields and the early detection of disease symptoms on plant leaves, thereby enhancing crop management and productivity.

Published Paper   E-Certificate

"Plant Leaf Disease Detection using VGG16", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.d1-d8, October-2024, Available :https://ijnrd.org/papers/IJNRD2410301.pdf

Volume 9 Issue 10, October-2024

Pages : d1-d8

Paper Reg. ID: IJNRD_301568

Published Paper Id: IJNRD2410301

Downloads: 00029

Research Area: Science and Technology

Country: Visakhapatnam, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2410301

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2410301

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition