Deep W2net: Deep learning based YOLO network for accurate weed detection in wheat fields
Abstract
Aim of study: To detect weeds in the wheat-growing regions of northwestern India using a novel Deep W2net to enhance wheat protection.
Area of study: Northwestern India.
Material and methods: The captured video sequence was transformed into frames, and the frames were denoised to enhance image clarity by sharpening edges and reducing blurriness. The denoised images were augmented to increase the size of the dataset for training. The proposed Deep W2net consists of Squeeze and Excitation (SE) and Swin Transformer (ST) modules integrated YOLO network (SET-YOLO) for efficient detection of weeds. The SE adjusts the dependencies between convolutional kernel channels, and ST enables the model to analyse spatial relationships at multiple scales.
Main results: The proposed Deep W2net attains an accuracy of 99.16% for detecting the weeds in wheat fields. The proposed model improves the range of overall accuracy by 4.19%, 1.47%, and 6.51% better than the existing techniques such as CNN, CSCW YOLOV7, and YOLOV8 respectively.
Research highlights: The proposed Deep W2net method demonstrates a significant advancement in weed detection to maximize the wheat productivity and potentially lead to more efficient and precise crop management.
Downloads
References
De Camargo T, Schirrmann M, Landwehr N, Dammer KH, Pflanz M (2021). Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops. Remote Sensing, 13(9), 1704. https://doi.org/10.3390/rs13091704
El-Kenawy ESM, Khodadadi N, Mirjalili S, Makarovskikh T, Abotaleb M, Karim FK, Khafaga DS (2022). Metaheuristic optimization for improving weed detection in wheat images captured by drones. Mathematics, 10(23), 4421. https://doi.org/10.3390/math10234421
Espejo-Garcia B, Mylonas N, Athanasakos L, Fountas S, Vasilakoglou I (2020). Towards weeds identification assistance through transfer learning. Computers and Electronics in Agriculture, 171, 105306. https://doi.org/10.1016/j.compag.2020.105306
Haq MA (2022). CNN based automated weed detection system using UAV imagery. Computer Systems Science and Engineering, 42(2), 837–849. https://doi.org/10.32604/csse.2022.023016
Haq SIU, Tahir MN, Lan Y (2023). Weed detection in wheat crops using image analysis and artificial intelligence (AI). Applied Sciences, 13(15), 8840. https://doi.org/10.3390/app13158840
He C, Wan F, Ma G, Mou X, Zhang K, Wu X, Huang X (2024). Analysis of the Impact of Different Improvement Methods Based on YOLOv8 for Weed Detection. Agriculture, 14(5), 674. https://doi.org/10.3390/agriculture14050674
Horvath DP, Clay SA, Swanton CJ, Anderson JV, Chao WS (2023). Weed-induced crop yield loss: a new paradigm and new challenges. Trends in Plant Science, 28(5), 567–582. https://doi.org/10.1016/j.tplants.2022.12.014
Jabir B, Falih N (2022). Deep learning-based decision support system for weeds detection in wheat fields. International Journal of Electrical and Computer Engineering, 12(1), 816–825. https://doi.org/10.11591/ijece.v12i1.pp816-825
Li Z, Wang D, Yan Q, Zhao M, Wu X, Liu X (2024). Winter wheat weed detection based on deep learning models. Computers and Electronics in Agriculture, 227, 109448. https://doi.org/10.1016/j.compag.2024.109448
Liu Q, Wu T, Deng Y, Liu Z (2023). Se-YOLOv7 landslide detection algorithm based on attention mechanism and improved loss function. Land, 12(8), 1522. https://doi.org/10.3390/land12081522
Liu T, Jin X, Zhang L, Wang J, Chen Y, Hu C, Yu J (2023). Semi-supervised learning and attention mechanism for weed detection in wheat. Crop Protection, 174, 106389. https://doi.org/10.1016/j.cropro.2023.106389
Liu Y, Zeng F, Diao H, Zhu J, Ji D, Liao X, Zhao Z (2024). YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion. Sensors, 24(13), 4379. https://doi.org/10.3390/s24134379
Ma J, Li Y, Du K, Zheng F, Zhang L, Gong Z, Jiao W (2020). Segmenting ears of winter wheat at flowering stage using digital images and deep learning. Computers and Electronics in Agriculture, 168, 105159. https://doi.org/10.1016/j.compag.2019.105159
Mateen A, Zhu Q (2019). Weed detection in wheat crop using UAV for precision agriculture. Pakistan Journal of Agricultural Sciences, 56, 809–817. https://doi.org/10.21162/PAKJAS/19.8036
Mota-Delfin C, López-Canteñs GDJ, López-Cruz IL, Romantchik-Kriuchkova E, Olguín-Rojas JC (2022). Detection and counting of corn plants in the presence of weeds with convolutional neural networks. Remote Sensing, 14(19), 4892. https://doi.org/10.3390/rs14194892
Selvi CT, Subramanian RS, Ramachandran R (2021). Weed detection in agricultural fields using deep learning process. En: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1, pp. 1470–1473. IEEE. https://doi.org/10.1109/ICACCS51430.2021.9441683
Shapira U, Herrmann I, Karnieli A, Bonfil DJ (2013). Field spectroscopy for weed detection in wheat and chickpea fields. International Journal of Remote Sensing, 34(17), 6094–6108. https://doi.org/10.1080/01431161.2013.793860
Wang K, Hu X, Zheng H, Lan M, Liu C, Liu Y, Tan S (2024). Weed detection and recognition in complex wheat fields based on an improved YOLOv7. Frontiers in Plant Science, 15, 1372237. https://doi.org/10.3389/fpls.2024.1372237
Wang X, Huang J, Feng Q, Yin D (2020). Winter wheat yield prediction at county level and uncertainty analysis in main wheat-producing regions of China with deep learning approaches. Remote Sensing, 12(11), 1744. https://doi.org/10.3390/rs12111744
Wang Y, Zou H, Yin M, Zhang X (2023). Smff-yolo: A scale-adaptive YOLO algorithm with multi-level feature fusion for object detection in UAV scenes. Remote Sensing, 15(18), 4580. https://doi.org/10.3390/rs15184580
Wu H, Wang Y, Zhao P, Qian M (2023). Small-target weed-detection model based on YOLO-V4 with improved backbone and neck structures. Precision Agriculture, 24(6), 2149–2170. https://doi.org/10.1007/s11119-023-10035-7
Wu Z, Chen Y, Zhao B, Kang X, Ding Y (2021). Review of weed detection methods based on computer vision. Sensors, 21(11), 3647. https://doi.org/10.3390/s21113647
Xu K, Li H, Cao W, Zhu Y, Chen R, Ni J (2020). Recognition of weeds in wheat fields based on the fusion of RGB images and depth images. IEEE Access, 8, 110362–110370. https://doi.org/10.1109/ACCESS.2020.3001999
Xu K, Shu L, Xie Q, Song M, Zhu Y, Cao W, Ni J (2023). Precision weed detection in wheat fields for agriculture 4.0: A survey of enabling technologies, methods, and research challenges. Computers and Electronics in Agriculture, 212, 108106. https://doi.org/10.1016/j.compag.2023.108106
Xu K, Zhu Y, Cao W, Jiang X, Jiang Z, Li S, Ni J (2021). Multi-modal deep learning for weeds detection in wheat field based on RGB-D images. Frontiers in Plant Science, 12, 732968. https://doi.org/10.3389/fpls.2021.732968
Zhang S, Huang W, Wang Z (2021). Combing modified Grabcut, K-means clustering and sparse representation classification for weed recognition in wheat field. Neurocomputing, 452, 665–674. https://doi.org/10.1016/j.neucom.2020.06.140
Zou K, Liao Q, Zhang F, Che X, Zhang C (2022). A segmentation network for smart weed management in wheat fields. Computers and Electronics in Agriculture, 202, 107303. https://doi.org/10.1016/j.compag.2022.107303
Zou K, Chen X, Wang Y, Zhang C, Zhang F (2021). A modified U-Net with a specific data argumentation method for semantic segmentation of weed images in the field. Computers and Electronics in Agriculture, 187, 106242. https://doi.org/10.1016/j.compag.2021.106242
Copyright (c) 2025 Consejo Superior de Investigaciones Científicas (CSIC)
This work is licensed under a Creative Commons Attribution 4.0 International License.
© CSIC. Manuscripts published in both the print and online versions of this journal are the property of the Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
All contents of this electronic edition, except where otherwise noted, are distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. You may read the basic information and the legal text of the licence. The indication of the CC BY 4.0 licence must be expressly stated in this way when necessary.
Self-archiving in repositories, personal webpages or similar, of any version other than the final version of the work produced by the publisher, is not allowed.
