|
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter
Copyright Infringement Claims
Indexing Partner
|
Published Paper Details
|
|
| Paper Title: | SUGARCANE DISEASE IDENTIFICATION AND QUALITY CHECKING USING CNN |
| Authors Name: | K SHALINI , S MOUNIKA |
| Unique Id: | IJSDR2307159 |
| Published In: | Volume 8 Issue 7, July-2023 |
| Abstract: | Pest detection in plants is one in all the most important problems in agriculture. However, recent advances in advanced pc imaging equipment have opened the manner for automatic sickness detection. Results from public datasets using Convolutional Neural Network (CNN) models demonstrate its suitability. A lot of plant infection data is gathered and recorded beneath various situations coming from the digicam. They extensively utilized two exclusive detection algorithms, YOLO and Ocius-Rcnn, to accurately identify corrupt places. When two fibers have been evaluated in a given set, the test set had a mean. In widespread, the approach of the usage of genes on heavily analyzed datasets prepares a mechanized contamination control system. Agriculture is the most essential area that drives the united states of america's financial boom and is carefully related to all sectors of society. Sugarcane is the most flourishing crop of India. The sugar enterprise makes use of sugar compounds to supply sugar, bioelectricity, bioethanol and different chemical merchandise. The sugar crop need to be accelerated to cope with the sector's growing population. Sugarcane manufacturing is seriously suffering from pests and diverse diseases. As a result, the farmers in addition to the nation suffer heavy financial losses. Therefore, early prognosis of diverse cane sicknesses and pest manipulate techniques are important to growth manufacturing. Detection of cane diseases with the bare eye leads to incorrect pesticide measures. Therefore, automated identity and early prognosis of sugarcane illnesses is crucial to boom manufacturing and nice. Drawing strategies can successfully extract functions from cane leaves and also pick out types of illnesses at an early stage. |
| Keywords: | IMAGE PROCESSING PYTHON CNN |
| Cite Article: | "SUGARCANE DISEASE IDENTIFICATION AND QUALITY CHECKING USING CNN", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 7, page no.1083 - 1087, July-2023, Available :http://www.ijsdr.org/papers/IJSDR2307159.pdf |
| Downloads: | 000338536 |
| Publication Details: | Published Paper ID: IJSDR2307159 Registration ID:207956 Published In: Volume 8 Issue 7, July-2023 DOI (Digital Object Identifier): Page No: 1083 - 1087 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijsdr.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
Track Paper
Important Links
Conference Proposal
ISSN
 |
 |
DOI (A digital object identifier)
  Providing A digital object identifier by DOI How to GET DOI and Hard Copy Related |
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
 |
 |
 |
Social Media
Indexing Partner |
|||
| Copyright © 2024 - All Rights Reserved - IJSDR | |||
Facebook Twitter Instagram LinkedIn