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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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Issue: December 2025
Volume 10 | Issue 12
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19445
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
Total Countries : 144
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| Paper Title: | Enhanced Deep Learning-Driven Framework for Efficient Plant Disease Diagnosis |
| Authors Name: | Nikita Goswami , Anurag Shrivastava |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_206897
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| Published Paper Id: | IJRTI2510119 |
| Published In: | Volume 10 Issue 10, October-2025 |
| DOI: | |
| Abstract: | Agriculture forms the foundation of human livelihood, yet crop productivity is often hampered by various plant diseases that remain undetected during their initial stages. Conventional disease identification methods rely heavily on manual observation by agricultural experts, which is time-consuming, subjective, and limited by human expertise. To overcome these challenges, this research presents an enhanced deep learning-based framework for the automated detection and classification of plant leaf diseases. The proposed framework utilizes the plant disease detection dataset, comprising images of both healthy and diseased plant leaves across multiple crop species. The model employs a fine-tuned Convolutional Neural Network (CNN) backbone such as Efficient Net/ResNet-50, integrated with attention mechanisms to focus on disease-relevant regions of the leaf image. The developed framework thus provides a scalable, interpretable, and efficient solution for plant disease detection, promoting precision agriculture and supporting sustainable crop management through early and reliable diagnosis. |
| Keywords: | Deep Learning, Agriculture, Plant, Disease, Farmers, Crop |
| Cite Article: | "Enhanced Deep Learning-Driven Framework for Efficient Plant Disease Diagnosis", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 10, page no.b170-b173, October-2025, Available :http://www.ijrti.org/papers/IJRTI2510119.pdf |
| Downloads: | 00089 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2510119
Registration ID:206897
Published In: Volume 10 Issue 10, October-2025
DOI (Digital Object Identifier):
Page No: b170-b173 Country: Bhopal, MP, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2510119 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2510119 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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