UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 7
July-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2107451


Registration ID:
312602

Page Number

d465-d468

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Title

MANGO LEAF DEFICIENCY DETECTION USING DIGITAL IMAGE PROCESSING AND MACHINE LEARNING

Abstract

Among world's mango delivering nations,India positions first and record half of the world's mango creation. The mango organic product is well known due to its wide scope of versatility, high dietary benefit, diverse assortment, scrumptious taste and incredible flavor. The natural product contains nutrient A and nutrient C in a rich degree. The yield is inclined to sicknesses like fine buildup, anthracnose, kick the bucket back, scourge, red rust, dirty form, and so on Problems may likewise affect the plant without powerful case and control measures. These incorporate difference in structure, biennial bearing, fall of natural product, dark top, bunching, and so on The rancher should counsel and take proficient help for the anticipation/control of illnesses and harvest problem. New procedures of distinguishing mango infection are needed to elevate better control to keep away from this emergency. By thinking about this, paper portrays picture acknowledgment which gives practical and adaptable infection recognition innovation. Paper further portrays AI models which offer a chance for simple sending of this innovation. By considering a dataset of mango sickness, pictures are taken from Konkan region in India. Machine learning strategy is utilized to prepare a significant Convolutionary.

Key Words

Crop, Mango, Neural Network, Machine learning, Image Recognition, Convolutionary Neural Network (CNN).

Cite This Article

"MANGO LEAF DEFICIENCY DETECTION USING DIGITAL IMAGE PROCESSING AND MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.d465-d468, July-2021, Available :http://www.jetir.org/papers/JETIR2107451.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"MANGO LEAF DEFICIENCY DETECTION USING DIGITAL IMAGE PROCESSING AND MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppd465-d468, July-2021, Available at : http://www.jetir.org/papers/JETIR2107451.pdf

Publication Details

Published Paper ID: JETIR2107451
Registration ID: 312602
Published In: Volume 8 | Issue 7 | Year July-2021
DOI (Digital Object Identifier):
Page No: d465-d468
Country: Mandya, Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162


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