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Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

R. Punithavathi1, A. Delphin Carolina Rani2, K. R. Sughashini3, Chinnarao Kurangi4, M. Nirmala5, Hasmath Farhana Thariq Ahmed6, S. P. Balamurugan7,*

1 Department of Information Technology, M.Kumarasamy College of Engineering, Karur, 639113, India
2 Department of Computer Science and Engineering, K. Ramakrishnan College of Technology, Tiruchirapalli, 621112, India
3 Department of Electronics and Instrumentation, Easwari Engineering College, Tamil Nadu, 600089, India
4 Pondicherry University, Puducherry, 605014, India
5 Department of computer science and engineering, The Oxford college of Engineering, Bangalore, 560068, India
6 Department of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 602105, India
7 Department of Computer and Information Science, Faculty of Science, Annamalai University Chidambaram, 608002, India

* Corresponding Author: S. P. Balamurugan. Email: email

Computer Systems Science and Engineering 2023, 44(3), 2759-2774.


Presently, precision agriculture processes like plant disease, crop yield prediction, species recognition, weed detection, and irrigation can be accomplished by the use of computer vision (CV) approaches. Weed plays a vital role in influencing crop productivity. The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased. Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity, this study presents a novel computer vision and deep learning based weed detection and classification (CVDL-WDC) model for precision agriculture. The proposed CVDL-WDC technique intends to properly discriminate the plants as well as weeds. The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine (ELM) based weed classification. The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization (FFO) algorithm. A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced outcomes over its recent approaches interms of several measures.


Cite This Article

APA Style
Punithavathi, R., Rani, A.D.C., Sughashini, K.R., Kurangi, C., Nirmala, M. et al. (2023). Computer vision and deep learning-enabled weed detection model for precision agriculture. Computer Systems Science and Engineering, 44(3), 2759-2774.
Vancouver Style
Punithavathi R, Rani ADC, Sughashini KR, Kurangi C, Nirmala M, Ahmed HFT, et al. Computer vision and deep learning-enabled weed detection model for precision agriculture. Comput Syst Sci Eng. 2023;44(3):2759-2774
IEEE Style
R. Punithavathi et al., "Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture," Comput. Syst. Sci. Eng., vol. 44, no. 3, pp. 2759-2774. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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