Comparative Evaluation of CNN Models for Precision Agriculture in Deep Learning-Based Weed Detection
Keywords:
Weed Detection, Deep Learning, Resnet, Densenet, VGGnet, Precision Agriculture, Computer VisionAbstract
Precision agriculture depends on automated weed detection to increase crop output and decrease pesticide use. Deep learning-based methods are useful for identifying weeds in the field. This study compares three sets of convolutional neural networks—Densenet, Resnet, and VGGNet—for the purpose of weed detection. A real-time data set that was recorded by a camera in the field is used to perform these models based on accuracy, precision, and recall. All preprocessing methods for performance metrics are completed. According to these results, Densenet outperforms the other two models in terms of accuracy. These observations aid in choosing the best model for agricultural applications in real time
Downloads
Metrics
References
Nafeesa Yousuf Murad,Tariq Mahmood et.al 1Weed Detection Using Deep Learning: A Systematic Literature Review”. Sensors 2023, 23, 3670. https://doi.org/ 10.3390/s23073670
Faisal Dharma Adhinata, Wahyono Raden Sumiharto a, “A Comprehensive Survey on Weed and Crop Classification Using Machine Learning” journal homepage: http://www.keaipublishing.com/en/journals/artificialintelligence-in-agriculture/ https://doi.org/10.1016/j.aiia.2024.06.005
Yemu et.al,"DenseNet Weed Recognition Model Combining Local Variance Preprocessing and Efficient Channel Attention Mechanism"Front. Plant Sci. , 12 January 2023Sec. Sustainable and Intelligent Phytoprotection Volume 13 - 2022 | https://doi.org/10.3389/fpls.2022.1041510
Kun Hu , Guy Coleman , Shan Zeng , Zhiyong Wang , Michael Walsh,"Graph Weeds Net: A Graph-Based Deep Learning Method for Weed Recognition"Computers and Electronics in Agriculture Volume 174, July 2020, 105520, https://doi.org/10.1016/j.compag.2020.105520
Oscar Leonardo García-Navarrete, Adriana Correa-Guimaraes and Luis Manuel Navas-Gracia"Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review"Agriculture 2024, 14, 568. https://doi.org/10.3390/ agriculture14040568
Xiaojun Jin, Muthukumar Bagavathiannan, Patrick E McCullough, Yong Chen et.al"A Deep Learning-Based Method for Classification, Detection, and Localization of Weeds in Turfgrass"https://scijournals.onlinelibrary.wiley.com/doi/epdf/10.1002/ps.7102, 28 July 2022 https://doi.org/10.1002/ps.7102
A Dheeraj, S Marwaha, S Nigam, MA Haque, Madhu,"ADNet: An Attention Embedded DenseNet121 Model for Weed Classification in Maize Crop"Springer Nature Switzerland AG 2024D. Pastor-Escuredo et al. (Eds.): ICDLAIR 2023, LNNS 1001, pp. 626–638, 2024.https://doi.org/10.1007/978-3-031-60935-0_55
Zhuolin Li , Dashuai Wang , Qing Yan, Minghu Zhao , Xiaohu Wu , Xiaoguang Liu "Winter Wheat Weed Detection Based on Deep Learning Models"Computers and Electronics in Agriculture.,Volume 227, Part 1, December 2024, 109448, www.sciencedirect.com https://doi.org/10.1016/j.compag.2024.109448
Marios Vasileiou a, Leonidas Sotirios Kyrgiakos a , Christina Kleisiari et.al Transforming Weed Management in Sustainable Agriculture with Artificial Intelligence: A Review" Contents lists available at ScienceDirect Crop Protection journal homepage: www.elsevier.com/locate/cropro https://doi.org/10.1016/j.cropro.2023.106522
A S M Mahmudul Hasan, Ferdous Sohel, Dean Diepeveen, Hamid Laga, Michael G.K. JonesWeed Recognition using Deep Learning Techniques on Class-imbalanced ImageryCrop & Pasture Science, 74(6), 628–644. doi:10.1071/CP21626
Palanisamy R, Gnana Kousalya C, Ramkumar R, Usha S, Thamizh Thentral TM, D Selvabharathi, Shanmugasundaram V,Analysing UPQC performance with dual NPC converters: Three-dimensional and two-dimensional space vector modulation, Results in Engineering,Volume25,2025,103611,ISSN25901230.
Chandrasekar, L.B., Ramkumar, R., Kiruba, S. et al. The Influence of Bi Doping Concentration on Structural, Dielectric and Optical Properties of Bi-doped ZnO thin Films. Semiconductors 59, 61–69 (2025). https://doi.org/10.1134/S1063782624602127
Geetha, A., Usha, S., Padmanabhan, J.B., Palanisamy, R., Alexander, A., Peter, G.,Ramkumar, R., Ganji, V.: Performance evaluation of coloured filters on PV panels in an outdoor environment. IET Renew. Power Gener. 1– 23 (2024). https://doi.org/10.1049/rpg2.13040
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
