Quality checking of sugar cane and disease identification using CNN in python

S.ABHILASH , S.DINESH , S.RAVINDRA , C.ARCHIT SAI VIVEK , MRS. J. RANGANAYAKI

Pest detection in crops is certainly one of the most important issues in agriculture. However, recent advances in superior pc imaging equipment have opened the way for automatic sickness detection. Results from public datasets using Convolutional Neural Network (CNN) models demonstrate its suitability. A lot of plant contamination records is amassed and recorded beneath diverse situations coming from the digicam. We used specific object detection algorithms.

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"Quality checking of sugar cane and disease identification using CNN in python", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 4, page no.1356 - 1362, April-2023, Available :https://ijsdr.org/papers/IJSDR2304219.pdf

Volume 8 Issue 4, April-2023

Pages : 1356 - 1362

Paper Reg. ID: IJSDR_205421

Published Paper Id: IJSDR2304219

Downloads: 000347207

Research Area: Engineering

Country: KANCHIPURAM, TAMILNADU, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2304219

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2304219

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition