Weed Detection Model Using the Generative Adversarial Network and Deep Convolutional Neural Network

Authors

  • S. Anthoniraj MVJ College of Engineering, Bangalore, Karnataka, India
  • P. Karthikeyan Jain (Deemed to be University), Bangalore, Karnataka, India https://orcid.org/0000-0001-8977-5520
  • V. Vivek Faculty of Engineering & Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India https://orcid.org/0000-0003-2748-2890

DOI:

https://doi.org/10.13052/jmm1550-4646.1826

Keywords:

Deep learning, Weed detection, Generative Adversarial Network, Deep Convolutional Neural Network

Abstract

Agriculture crop demand is increasing day by day because of population. Crop production can be increased by removing weeds in the agriculture field. However, weed detection is a complicated problem in the agriculture field. The main objective of this paper is to improve the accuracy of weed detection by combining generative adversarial networks and convolutional neural networks. We have implemented deep learning models, namely Generative Adversarial Network and Deep Convolutional Neural Network (GAN-DCNN), AlexNet, VGG16, ResNet50, and Google Net perform the detection of the weed. A generative Adversarial Network generates the weed image, and Deep Convolutional Neural Network detects the weed in the image. GAN-DCNN method outperforms than existing weed detection method. Simulation results confirm that the proposed GAN-DCNN has improved performance with a maximum weed detection rate of 87.12 and 96.34 accuracies.

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Author Biographies

S. Anthoniraj, MVJ College of Engineering, Bangalore, Karnataka, India

S. Anthoniraj received his BE in Computer Science Engineering from Anna University Chennai, ME in Computer Science Engineering from Vinayaka Mission University Salem and PhD in Server Virtualization from Manonmaniam Sundarnar University Tirunelveli, Tamilnadu. He is working as an Professor in Computer Science Engineering at M V J College of Engineering, Whitefield, Bangalore. His area of interest includes Virtualization, Data Science, Machine Learning Java Programming, Internet Programming, and Computer Networks, Open-source tools and components.

P. Karthikeyan, Jain (Deemed to be University), Bangalore, Karnataka, India

P. Karthikeyan obtained his the Bachelor of Engineering (B.E.,) in Computer Science and Engineering from Anna University, Chennai, and Tamil nadu, India in 2005 and received his Master of Engineering (M.E,) in Computer Science and Engineering from Anna University, Coimbatore India in 2009. He has completed Ph.D. degree in Anna University, Chennai in 2018. Skilled in developing projects and carrying out research in the area of Cloud computing and Data science with the programming skill in Java, Python, R and C. He published more than 20 International journals with good impact factor and presented more than 10 International conferences. He was the reviewer of Elsevier, Springer, Inderscience and reputed Scopus indexed journals. He is acting as editorial board members in EAI Endorsed Transactions on Energy Web, The International Arab Journal of Information Technology and Blue Eyes Intelligence Engineering and Sciences Publication journal.

V. Vivek, Faculty of Engineering & Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India

V. Vivek is a dedicated educationist with 13+ years of experience in teaching and research domains. His area of expertise includes Distributed Systems, Cloud Computing, Computer Networks, Agent-based Computing. As a continuous learner and researcher, published research articles in leading journals (SCI, and Scopus) and was a resource person for various guest lectures. Dr. V. Vivek currently working as a Program Coordinator in the Dept. of CSE (AI&ML) and (Cybersecurity) at FET-JAIN (Deemed-to-be University)-City campus.

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Published

2021-11-16

Issue

Section

Computer Vision and its Application in Agriculture

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