Automated Sugarcane Disease Detection Using Faster RCNN with an Android Application-Reference-Cited by-同舟云学术

Automated Sugarcane Disease Detection Using Faster RCNN with an Android Application

Published:2022-04-28 Issue: Volume: Page:
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Container-title:2022 6th International Conference on Trends in Electronics and Informatics (ICOEI)
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Author:

Murugeswari R.¹,Anwar Z. Sharik¹,Dhananjeyan V. Raja¹,Karthik C. Naveen¹

Affiliation:

1. Kalasalingam Academy Of Research and Education,Department of Computer Science and Engineering,Krishnankovil,Tamilnadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9776594/9776641/09776685.pdf?arnumber=9776685

Reference23 articles.

1. Plant Disease Detection Using Ma-chine Learning;shima;2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C),2018

2. Infected Leaf Analysis and Comparison by Otsu Threshold and k -Means Clustering;badnakhe;International Journal of Advanced Research in Computer Science and Software Engineering,2012

3. Fast and Accurate Detection and Classification of Plant Diseases

4. Design of Monitoring and Control Plant Disease System Based on DSPFPGA;zhang;Second In-ternational Conference on Networks Security Wireless Communications and Trusted Computing,2010

5. Rice disease identification using pattern recognition techniques

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1. Sugar Learning: Deep Learning for Rapid Detection and Classification of Sugarcane Diseases;2023 World Conference on Communication & Computing (WCONF);2023-07-14

2. A comparative study of vision transformers and convolutional neural networks: sugarcane leaf diseases identification;European Food Research and Technology;2023-04-21

3. Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models;Remote Sensing;2022-12-03