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Reseach Article

Crop Identification by using Real Time Object Detection

Published on January 2025 by Sandhya Umrao, Sanjay Kumar, Vivekta Singh, Kanika Singhal
International Conference on Artificial Intelligence and Data Science Applications - 2023
Control System labs
ICAIDSC2023 - Number 3
January 2025
Authors: Sandhya Umrao, Sanjay Kumar, Vivekta Singh, Kanika Singhal
10.5120/icaidsc202421

Sandhya Umrao, Sanjay Kumar, Vivekta Singh, Kanika Singhal . Crop Identification by using Real Time Object Detection. International Conference on Artificial Intelligence and Data Science Applications - 2023. ICAIDSC2023, 3 (January 2025), 18-23. DOI=10.5120/icaidsc202421

@article{ 10.5120/icaidsc202421,
author = { Sandhya Umrao, Sanjay Kumar, Vivekta Singh, Kanika Singhal },
title = { Crop Identification by using Real Time Object Detection },
journal = { International Conference on Artificial Intelligence and Data Science Applications - 2023 },
issue_date = { January 2025 },
volume = { ICAIDSC2023 },
number = { 3 },
month = { January },
year = { 2025 },
issn = 0975-8887,
pages = { 18-23 },
numpages = 6,
url = { /proceedings/icaidsc2023/number3/crop-identification-by-using-real-time-object-detection/ },
doi = { 10.5120/icaidsc202421 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Artificial Intelligence and Data Science Applications - 2023
%A Sandhya Umrao
%A Sanjay Kumar
%A Vivekta Singh
%A Kanika Singhal
%T Crop Identification by using Real Time Object Detection
%J International Conference on Artificial Intelligence and Data Science Applications - 2023
%@ 0975-8887
%V ICAIDSC2023
%N 3
%P 18-23
%D 2025
%I International Journal of Computer Applications
Abstract

Computer vision techniques have been used in every field nowadays and find a wide range of application in agriculture field too due to their fast response and high accuracy. Several applications of computer vision are self-driving cars, the camera of the mobile phones and detection of faces. One of the most popular techniques in computer vision is real time object detection, it is not difficult in humans but for machines difference between main objects and other objects has to be trained. In this paper deep learning is used for detecting and identifying crops using YOLO (You Only Look Once) approach. For recognition and detection of crops, effective training needs to be carried out. The prime reason for using YOLO algorithm is that it looks the image completely by outlining the bounding regions of objects to be detected.

References
  1. Amandeep Kaur and Deepinder Kaur. Yolo Deep Learning Model Based Algorithm for Object Detection, in JSCE International Journal of Computer Science and Engineering, doi:10.26438/ijcse/v8i1.174178,2020
  2. Zu Whan Kim. Real Time Object Tracking based on Dynamic Feature Grouping with Background Subtraction, California PATH, University of California at Berkeley, CA, USA,2018
  3. Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi. You Only Look Once: Unified, Real-Time Object Detection, IEEE,2015
  4. Mrs.M.Usharani,S.Ramya , N.Shwetha , Y.Soundarya , Varsha Rajkumar .Object Detection and Tracking Of Plantation Crops Using SVM Algorithm,International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 6, 2019
  5. Javier Oliver, Alberto Albiol, Samuel Morillas, and Guillermo Peris- Fajarn´es. A Real-Time Person Detection Method for Moving Cameras, IEEE,2016
  6. ChengjiLiu ,Yufan Tao ,Jiawei Liang ,Kai Li ,Yihang Chen .Object Detection based on YOLO network,IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC 2018).
  7. Zhong-Qiu Zhao, Member, IEEE, Peng Zheng, Shou-tao Xu, XindongWu.Object Detection with Deep Learning: A Review, arXiv:1807.05511v2 [cs.CV] 16 Apr 2019, IEEE
  8. Juan Wu, Bo Peng, Zhenxiang Huang, and JietaoXie,Research on Computer Vision-Based Object Detection and Classification, IEEE Transactions on Automation Science and Engineering 8, 292–302 2015
  9. Sharnil Pandey, Ketan Kotecha. A Deep Learning approach for Face detection using YOLO, IEEE Punecon,2018
  10. Li Liu1, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, Matti Pietikäinen. Deep Learning for Generic Object Detection: A Survey,International Journal of Computer Vision, 2020
  11. Omkar Masurekar, Omkar Jadhav, Prateek Kulkarni, Shubham Patil. Real Time Object Detection Using YOLOv3, International Research Journal of Engineering and Technology (IRJET),2020
  12. V. Murugan, V.R. Vijaykumar, and A. Nidhila. Vehicle Logo Recognition using RCNN for Intelligent Transportation Systems, IEEE Student Conference on Research and Development (SCOReD), 2015
  13. Huieun Kim, Youngwan Lee, ByeounghakYim, Eunsoo Park, HakilKim. On-road object detection using Deep Neural Network,IEEE International Conference on Consumer Electronics-Asia ,2016
  14. Shaukat Hayat, She Kun, ZuoTengtao, Yue Yu, Tianyi Tu, YantongDu. A Deep Learning Framework Using Convolutional Neural Network for Multi-class Object Recognition,3rd IEEE International Conference on Image, Vision and Computing,2018
  15. Chengji Liu1, Yufan Tao1, Jiawei Liang1, Kai Li1, Yihang Chen.Object Detection Based on YOLO Network,IEEE 4th Information Technology and Mechatronics Engineering Conference,2018.
  16. Hyeon-Cheol Shin, Kwang-Il Lee, Chang-Eun Lee Data Augmentation Method of Object Detection for Deep Learning in Maritime Image 2020 IEEE International Conference on Big Data and Smart Computing (BigComp)
  17. Liyan Yu, Xianqiao Chen, Sansan Zhou Research of Image Main Objects Detection Algorithm Based on Deep Learning, 2018 3rd IEEE International Conference on Image, Vision and Computing
  18. Dweepna Garg, Parth Goel, Sharnil Pandya, Amit Ganatra, Ketan Kotecha A Deep Learning Approach for Face Detection using YOLO
  19. Chandan G, Ayush Jain, Hash Jain, Mohana Real Time Object Detection and Tracking Using Deep Learning and OpenCV Proceedings of the International Conference on Inventive Research in Computing Applications (ICIRC 2018)
  20. Nigam, A., Jain, D., Talan, M. S., Singh, S. K., & Umrao, S. (2023, May). A Comparative Analysis of ML Algorithms to Improve Crop Productivity Prediction. In 2023 International Conference on Disruptive Technologies (ICDT) (pp. 154-160). IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Computer vision Deep Learning YOLO Bounding regions