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)
– Agriculture is considered as backbone for the survival of all the living creatures. Rice crop cultivation is one of the significant practices in many parts of the world. In recent times Due to a variety of factors, farmers are experiencing crop production losses. Crop diseases are one of the main reasons of the aforementioned issue. This is a result of the disease's lack of awareness and the wide range of pesticides available to control rice infections. Yet, it is tough and takes more time to locate the most recent sickness and the proper pesticide to control the infected disease. We are employing a deep learning model with a transfer learning strategy and an image processing technique to diagnose the disease in order to address the aforementioned problem. As a result, our system alerts the farmer to the crop illness so that they can take appropriate action. The accuracy of the suggested model is 95%.
Keywords:
Deep Learning, Transfer Learning, CNN, crop production, rice diseases.
Cite Article:
"Rice Crop Disease Detection Using Deep Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.515 - 519, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304087.pdf
Downloads:
000205417
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