UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
Call for Paper
Volume 11 | Issue 4 | April 2024

JETIREXPLORE- Search Thousands of research papers



WhatsApp Contact
Click Here

Published in:

Volume 10 Issue 1
January-2023
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:
JETIR2301346


Registration ID:
506949

Page Number

d385-d390

Share This Article


Jetir RMS

Title

Apple Leaf Diseases Identification Using Deep Learning

Abstract

Abstract-The healthful growth of the apple business depends on the prompt and accurate identification of apple leaf diseases. Typically, each of these illnesses is examined by knowledgeable professionals individually. This is a laborious task with erratic precision. We thus suggested a low-cost, stable, high accuracy approach for identifying apple leaf diseases in this work. The Mobile-Net concept is used to achieve this. First of all, it is simple to install on mobile devices, it is a low-cost Model when compared to typical deep learning models. Second, with the aid of an algorithm, anybody can successfully complete the apple leaf diseases examination rather than only seasoned professionals. Thirdly, Mobile-Net's accuracy is almost on par with that of current sophisticated deep learning models. Finally, various studies have been conducted to show the efficacy of our suggested strategy for identifying apple leaf diseases. We evaluated the effectiveness and accuracy to well-known CNN models, MobileV3, ResNet152 and InceptionV3. The datasets for apple disease (containing the classifications complicated, frog-eye leaf spot, healthy, powdery mildew, rust, and scab) .

Key Words

Apple leaf diseases, Mobile device, Mobile-Net, Deep learning.

Cite This Article

"Apple Leaf Diseases Identification Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.d385-d390, January-2023, Available :http://www.jetir.org/papers/JETIR2301346.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

"Apple Leaf Diseases Identification Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppd385-d390, January-2023, Available at : http://www.jetir.org/papers/JETIR2301346.pdf

Publication Details

Published Paper ID: JETIR2301346
Registration ID: 506949
Published In: Volume 10 | Issue 1 | Year January-2023
DOI (Digital Object Identifier):
Page No: d385-d390
Country: srinagar, Jammu & Kashmir, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


Preview This Article


Downlaod

Click here for Article Preview

Download PDF

Downloads

000128

Print This Page

Current Call For Paper

Jetir RMS