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)
Crop diseases in agriculture are among the greatest threats to the food chain around the world both in terms of stability
and sustain203475ability. Detection of plant diseases relies heavily on human inspection, with methods that take time, but
are not only riddled with inaccuracies but demand a highly specialist approach. This is overcoming by an ensemble
learning model-proposed framework, which combines a custom CNN with transfer models, namely VGG-16 and
ResNet-50. Thus, the system is auto-detecting and classifying plant diseases based on leaf images; the earlier
diagnosis becomes more precise and faster. It makes use of the strengths of various neural networks to minimize loss
in crops, optimize pesticides, and improve sustainable agricultural practices so that food security is ensured in
addition to quality yield improvement. In this regard, the proposed system promises a reduction in the manual time
taken for disease detection, leads to crop management excellence, and results in real-time scalable solutions for the
farmers.
"Deep learning based plant disease detection using image recognition ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a578-a584, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505067.pdf
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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