INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (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)
The paper provides the description behind the idea of crop pest identification system that classifies between a benifitical and a harmful pest that may effect the crop, this paper provides a detailed description of the methods and techniques available for the crop pest identification system along with their strengths and weakness of the identified pest. Based on the research the model proposed in this paper is developed using convolutional neural network(CNN). This trained model consist of a data set of 9,000 images of Nine different pests each of 1000 images , the system has been tested across a large amount of data and verified across other traditional models , The accuracy provided by the proposed model is measured by 90% which is the highest compared to other cnn methods
Keywords:
pest classification, CNN, AlexNet, InceptionNet ,DenseNet, Crops, Pest names
Cite Article:
"Crop pest identification using alexnet", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.a667-a671, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304085.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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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