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)
Every types of plant's advancement is impacted by the infections it harbors, subsequently early conclusion is fundamental. Various AI (ML) models have been utilized for the recognizable proof and characterization of plant infections, however with the advancement of Profound Learning (DL), a subset of ML, this field of concentrate presently hopes to have huge potential for further developed exactness. Various created/changed DL models are utilized related to an assortment of perception ways to deal with distinguish and group the side effects of plant illnesses. What's more, an assortment of execution measurements are utilized to assess these structures and approaches. The definite reasoning of the DL models used to address different plant sicknesses is given in this article.
Keywords:
plant disease; deep learning; convolutional neural networks (CNN), GANs
Cite Article:
"Plant Disease Detection and Classification by Deep Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 6, page no.726 - 733, June-2023, Available :http://www.ijrti.org/papers/IJRTI2306111.pdf
Downloads:
000205214
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