UGC Approved Journal no 63975(19)

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

Volume 9 Issue 2
February-2022
eISSN: 2349-5162

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

7.95 impact factor calculated by Google scholar

Unique Identifier

Published Paper ID:
JETIR2202026


Registration ID:
319844

Page Number

a204-a211

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Title

Plant-Leaf Disease Detection using Deep Learning Techniques

Abstract

Abstract: Using imaging technology, we suggest that plant disease detection systems automatically identify the symptoms that occur on the leaves and stems of a plant, allowing for the cultivation of healthy plants on a farm to be improved. It is these systems that monitor the plant's characteristics, such as its leaves or stem, and any variations that are seen from those characteristics will be automatically recognised and sent to the user. The purpose of this paper is to conduct an evaluation of the available disease detection methods in plants. The most recent breakthrough in deep learning-based convolutional neural networks (CNNs) has resulted in a significant improvement in picture categorization accuracy. This Thesis, which is motivated by CNN's success in picture classification, uses a pre-trained deep learning-based technique for identifying plant illnesses to detect plant diseases. The contribution of this work may be divided into two categories: Predictions for a dataset may be made using the most powerful large-scale architectures available today, such as AlexNet GoogleNet, which are utilised for illness detection and the usage of baseline and transfer learning techniques for predictions. CNN's suggested model was trained and tested using data sets gathered from the website, according to the network. The results of training, testing, and experiments demonstrate that the suggested architecture is capable of realising and increasing GoogleNet model getting to 99.10 percent. when compared to other models, the accuracy.

Key Words

AlexNet, GoogleNet, Plant leaf diseases, deep learning,

Cite This Article

"Plant-Leaf Disease Detection using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 2, page no.a204-a211, February-2022, Available :http://www.jetir.org/papers/JETIR2202026.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

"Plant-Leaf Disease Detection using Deep Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 2, page no. ppa204-a211, February-2022, Available at : http://www.jetir.org/papers/JETIR2202026.pdf

Publication Details

Published Paper ID: JETIR2202026
Registration ID: 319844
Published In: Volume 9 | Issue 2 | Year February-2022
DOI (Digital Object Identifier):
Page No: a204-a211
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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