UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
Call for Paper
Volume 11 | Issue 11 | November 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:
JETIR2301303


Registration ID:
507021

Page Number

d9-d14

Share This Article


Jetir RMS

Title

A Deep Learning-Based PDConv Model for Plant Disease Detection, Classification and Prediction

Abstract

Plant diseases (PD) have a detrimental effect on agricultural output, quality, economics, and quantity. To detect such diseases, the life cycle of plants should be properly monitored from the beginning. It is necessary to automate the disease-detecting equipment to expedite this process. Developing a disease detection system should take advantage of deep learning (DL) techniques. No environmental issues had hindered agriculture until recently when several plant leaf diseases began to reduce harvests. It is difficult to detect PD early, which is only one of the many problems that producers face. To solve this problem, scientists created the 'PDConv model, an automated PD categorization system built on a deep convolution neural network (Deep CNN). In this four-step technique, we preprocessed the data, added it to it, extracted features, and classified the data. In this study, we classify data and extract features using a Deep CNN, the layered architecture of which is briefly discussed. The complete system was implemented with the help of the suggested method utilizing python programming language and the Jupyter notebook environment. Using data from the FGVC7 dataset, researchers can create deep learning (DL) PD classification system that is both accurate and efficient. When compared to CNN, the PDConv model performed exceptionally well. A CNN model may be trained with only the segmented and annotated parts of images rather than with whole images. After being trained on the preexisting CNN model, the PDConv model's results on independent data sources increased from 61.91% to 95.46%. In addition, 82% of the test data set showed an increase in self-assessment confidence, indicating strong development in this area. Proof that CNNs may be used effectively for identifying PD.

Key Words

Plant Disease Detection, Image Segmentation, Classification, Deep Learning, Deep CNN, FGVC7 Dataset.

Cite This Article

"A Deep Learning-Based PDConv Model for Plant Disease Detection, Classification and Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 1, page no.d9-d14, January-2023, Available :http://www.jetir.org/papers/JETIR2301303.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

"A Deep Learning-Based PDConv Model for Plant Disease Detection, Classification and Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 1, page no. ppd9-d14, January-2023, Available at : http://www.jetir.org/papers/JETIR2301303.pdf

Publication Details

Published Paper ID: JETIR2301303
Registration ID: 507021
Published In: Volume 10 | Issue 1 | Year January-2023
DOI (Digital Object Identifier):
Page No: d9-d14
Country: Gwalior, Madhya Pradesh, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


Preview This Article


Downlaod

Click here for Article Preview

Download PDF

Downloads

000277

Print This Page

Current Call For Paper

Jetir RMS