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India GDP (Gross Domestic Product) is mainly based on agricultural and high quality crop production plays a very important role. Frequent attacks of pests cause a serious damage to crops, by reducing their yields, and also decrease the nutrients in food products which poses a great threat to food. Safety. This in turn will have a major impact on our economy, farmers will suffer enormous losses due to these issues we have to sacrifice many farmers life. Regular monitoring of the crops is very important to take appropriate measures
on pests on time by using appropriate pesticide and further protect the crops from damage. Pest detection would really help the farmers to avoid the early defilement of crops by using pesticide. Artificial intelligence plays an important role in solving a huge problems in agriculture field thus farmers will benefit from AI-based technologies to boost agricultural production. In this paper we consider MobileNet V2 algorithm to classify the pest to different classes by reshaping the image, extracting the features of pest classifying according to their respective classes. The result proves that MobileNet V2 performs better with a higher accuracy (0.85) when compared to other pre-trained models.
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Cite Article:
"Pest Classification and Detection Using Deep Learning ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 7, page no.668 - 671, July-2022, Available :http://www.ijrti.org/papers/IJRTI2207095.pdf
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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