Deep Learning Approaches for Intelligent Plant Leaf Disease Prediction

Shardul Gawande , Shrawani More , Prerna Madan , Medha Asurlekar

Deep Learning; Data Augmentation; Agriculture

Plant diseases significantly impact crop yields and food security. This paper investigates two data augmentation methods for deep learning-based plant disease classification: custom augmentation layers integrated into a Keras Sequential model and the ImageDataGenerator utility. Our comparative analysis reveals their impact on model performance, offering insights into choosing the most suitable augmentation strategy. Both approaches enhance accuracy, with implications for precision agriculture and crop protection. This research contributes to sustainable agriculture practices.

Published Paper   E-Certificate

"Deep Learning Approaches for Intelligent Plant Leaf Disease Prediction", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 1, page no.78 - 83, January-2024, Available :https://ijsdr.org/papers/IJSDR2401012.pdf

Volume 9 Issue 1, January-2024

Pages : 78 - 83

Paper Reg. ID: IJSDR_209352

Published Paper Id: IJSDR2401012

Downloads: 000347271

Research Area: Engineering

Country: Thane, Maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2401012

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2401012

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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