Open Access

ARTICLE

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. Balamurugan^7,*

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:

Computer Systems Science and Engineering 2023, 44(3), 2759-2774. https://doi.org/10.32604/csse.2023.027647

Received 22 January 2022; Accepted 06 March 2022; Issue published 01 August 2022

Abstract

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.

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Keywords

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