Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/515501
Title: IOT Based Deep Learning Approach to Analyse and Detect Pest Fall Army Worm Using Odor Substances in Maize Fields
Researcher: Sheema D
Guide(s): Ramesh K
Keywords: Computer Science
Computer Science Cybernetics
Engineering and Technology
University: Hindustan Institute of Technology and Science
Completed Date: 2023
Abstract: he agricultural industry has undergone dramatic changes over the past 60 newlineyears. New advances in machinery have increased the efficiency of farming newlineequipment, leading to higher yields. The use of data and connectivity is driving newlinea revolution in agriculture today. Further yield increases may be possible with newlineemerging technology such as artificial intelligence, analytics, and connected newlinesensors. In India, food production is renowned. It is the primary source of newlinesustenance for a large proportion of the population. Currently, farmers rely newlineheavily on maize for their livelihood. Indian maize exports have increased newlinesubstantially during the past decade. The crop can be grown in a variety of newlineclimates and requires less water than other crops in India. A pest infestation newlinecauses farmers to lose income, which is unfortunate. newline
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URI: http://hdl.handle.net/10603/515501
Appears in Departments:Department of Computer Application

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02_prelim page.pdf277.21 kBAdobe PDFView/Open
03_content.pdf84.84 kBAdobe PDFView/Open
04_abstract.pdf74.68 kBAdobe PDFView/Open
05_chapter1.pdf563.65 kBAdobe PDFView/Open
06_chapter2.pdf281.26 kBAdobe PDFView/Open
07_chapter3.pdf821.44 kBAdobe PDFView/Open
08_chapter4.pdf1.51 MBAdobe PDFView/Open
09_chapter5.pdf1.53 MBAdobe PDFView/Open
10_chapter6.pdf79.28 kBAdobe PDFView/Open
11_chapter7.pdf113.99 kBAdobe PDFView/Open
80_recommendation.pdf74.68 kBAdobe PDFView/Open
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