UGC Approved Journal no 63975(19)

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

Volume 10 Issue 8
August-2023
eISSN: 2349-5162

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

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Published Paper ID:
JETIR2308339


Registration ID:
522748

Page Number

d326-d338

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Title

Crop Guru: Deep Learning for Precise and Rapid Plant Disease Diagnosis

Abstract

Sugarcane is cash crop in India and is also popular crop in Western Maharashtra, with millions of hectares of land dedicated to its cultivation. However, sugarcane crops are vulnerable to several diseases that can cause significant yield loss. In this research, we present an original and innovative approach for sugarcane crop disease detection by utilizing a Convolutional Neural Network (CNN). The proposed approach involves training a CNN model to automatically learn the features of sugarcane leaf images and classify them into healthy or diseased categories. To assess the efficiency of our proposed approach, we employed an openly accessible dataset containing sugarcane leaf images encompassing three distinct disease categories.The dataset was pre-processed and augmented to increase its diversity and size. Crop Guru model trained and evaluated using an augmented dataset, demonstrating an impressive overall accuracy of 95.2% on the test set. Moreover, we conducted a comparative analysis, pitting our proposed CNN model against other state-of-the-art classification techniques. The findings conclusively revealed that the CNN model outperformed all other methods, showcasing its superiority in sugarcane crop disease detection. The proposed CNN-based sugarcane crop disease detection system can be a valuable tool for farmers and researchers to quickly and accurately identify the disease-affected crops in turn, they are able to act effectively to stop the spread of disorders and reduce crop production loss.

Key Words

Deep Learning, Convolution Neural Network, Plant disease detection

Cite This Article

"Crop Guru: Deep Learning for Precise and Rapid Plant Disease Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.d326-d338, August-2023, Available :http://www.jetir.org/papers/JETIR2308339.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

"Crop Guru: Deep Learning for Precise and Rapid Plant Disease Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppd326-d338, August-2023, Available at : http://www.jetir.org/papers/JETIR2308339.pdf

Publication Details

Published Paper ID: JETIR2308339
Registration ID: 522748
Published In: Volume 10 | Issue 8 | Year August-2023
DOI (Digital Object Identifier):
Page No: d326-d338
Country: SATARA, MAHARASHTRA, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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