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Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Saud Yonbawi1, Sultan Alahmari2, T. Satyanarayana Murthy3, Padmakar Maddala4, E. Laxmi Lydia5, Seifedine Kadry6,7,8,*, Jungeun Kim9

1 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
2 King Abdul Aziz City for Science and Technology, Riyadh, Kingdom of Saudi Arabia
3 Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India
4 Department of Civil Engineering, Vignan’s Institute of Information and Technology (A), Duvvada, Visakhapatnam, AP, 530049, India
5 Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, 530049, India
6 Department of Applied Data Science, Noroff University College, Kristiansand, Norway
7 Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
8 Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
9 Department of Software, Kongju National University, Cheonan, 31080, Korea

* Corresponding Author: Seifedine Kadry. Email: email

Computer Systems Science and Engineering 2023, 46(2), 1533-1547. https://doi.org/10.32604/csse.2023.036296

Abstract

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield. Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns. Weed control has become one of the significant problems in the agricultural sector. In traditional weed control, the entire field is treated uniformly by spraying the soil, a single herbicide dose, weed, and crops in the same way. For more precise farming, robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type. This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture. This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection (HHOGCN-WD) technique for Precision Agriculture. The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture. For image pre-processing, the HHOGCN-WD model utilizes a bilateral normal filter (BNF) for noise removal. In addition, coupled convolutional neural network (CCNet) model is utilized to derive a set of feature vectors. To detect and classify weed, the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance. The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset. The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches, with increased accuracy of 99.13%.

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APA Style
Yonbawi, S., Alahmari, S., Murthy, T.S., Maddala, P., Lydia, E.L. et al. (2023). Harris hawks optimizer with graph convolutional network based weed detection in precision agriculture. Computer Systems Science and Engineering, 46(2), 1533-1547. https://doi.org/10.32604/csse.2023.036296
Vancouver Style
Yonbawi S, Alahmari S, Murthy TS, Maddala P, Lydia EL, Kadry S, et al. Harris hawks optimizer with graph convolutional network based weed detection in precision agriculture. Comput Syst Sci Eng. 2023;46(2):1533-1547 https://doi.org/10.32604/csse.2023.036296
IEEE Style
S. Yonbawi et al., "Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture," Comput. Syst. Sci. Eng., vol. 46, no. 2, pp. 1533-1547. 2023. https://doi.org/10.32604/csse.2023.036296



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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