Recognition and localization method of maize weeding robot based on improved YOLOv5

Lijun Zhao, Yunfan Jia, Wenke Yin, Zihuan Li, Chuandong Liu, Hang Luo, Xin Hu, Hua Huang, Qiang Li, Cheng Lyu, Bin Li

Abstract


In response to the challenge posed by low recognition accuracy in rugged terrains with diverse topography as well as feature recognition agricultural settings, this paper presents an optimized version of the YOLOv5 algorithm alongside the development of a specialized laser weeding experimental platform designed for precise identification of corn seedlings and weeds. The enhanced YOLOv5 algorithm integrates the effective channel attention (CBAM) mechanism while incorporating the DeepSort tracking algorithm to reduce parameter count for seamless mobile deployment. Ablation tests validated this model’s achievement of 96.2% accuracy along with superior mAP values compared to standard YOLOv5 by margins of 3.1% and 0.7%, respectively. Additionally, three distinct datasets captured different scenarios, and their amalgamation resulted in an impressive recognition rate reaching up to 96.13%. Through comparative assessments against YOLOv8, the model demonstrated lightweight performance improvements, including a notable enhancement of 2.1% in recognition rate coupled with a marginal increase of 0.2% in mAP value, thus ensuring heightened precision and robustness during dynamic object detection within intricate backgrounds.
Key words: precision agriculture; YOLOv5; weeding robot; maize; laser weeding
DOI: 10.25165/j.ijabe.20251802.9463

Citation: Zhao L J, Jia Y F, Yin W K, Li Z H, Liu C D, Luo H, et al. Recognition and localization method of maize weeding robot based on improved YOLOv5. Int J Agric & Biol Eng, 2025; 18(2): 248–258.

Keywords


precision agriculture; YOLOv5; weeding robot; maize; laser weeding

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References


Jiang H, Murengami B G, Jiang L, Chen C, Johnson C, Cheein F A, et al. Automated segmentation of individual leafy potato stems after canopy consolidation using YOLOv8x with spatial and spectral features for UAV-based dense crop identification. Computers and Electronics in Agriculture, 2024; 219: 108795.

Liu S Q, Jin Y S, Ruan Z W, Ma Z, Gao R, Su Z B. Real-time detection of seedling maize weeds in sustainable agriculture. Sustainability, 2022; 14(22): 15088.

Chen J Q, Wang H B, Zhang H D, Luo T, Wei D P, Long T, et al. Weed detection in sesame fields using a YOLO model with an enhanced attention mechanism and feature fusion. Computers and Electronics in Agriculture, 2022; 202: 107412.

Fatima H S, ul Hassan I, Hasan S, Khurram M, Stricker D, Afzal M Z. Formation of a lightweight, deep learning-based weed detection system for a commercial autonomous laser weeding robot. Applied Sciences, 2023; 13(6): 3997.

Zhu H B, Zhang Y Y, Mu D L, Bai L Z, Zhuang H, Li H. YOLOX-based blue laser weeding robot in corn field. Frontiers in Plant Science, 2022; 13: 1017803.

Wang M J, Li Y, Meng H W, Chen Z W, Gui Z Y, Li Y P, et al. Small target tea bud detection based on improved YOLOv5 in complex background. Frontiers in Plant Science, 2024; 15: 1393138.

Jin X, Jiao H W, Zhang C, Li M Y, Zhao B, Liu G W, et al. Hydroponic lettuce defective leaves identification based on improved YOLOv5s. Frontiers in Plant Science, 2023; 14: 1242337.

Ju J Y, Chen G Q, Lv Z Y, Zhao M Y, Sun L, Wang Z T, et al. Design and experiment of an adaptive cruise weeding robot for paddy fields based on improved YOLOv5. Computers and Electronics in Agriculture, 2024; 219: 108824.

Zhang T, Zhao D F, Chen Y S, Zhang H L, Liu S L. DeepSORT with siamese convolution autoencoder embedded for honey peach young fruit multiple object tracking. Computers and Electronics in Agriculture, 2024; 217: 108583.

Du P C, Chen S, Li X, Hu W W, Lan N, Lei X M, et al. Green pepper fruits counting based on improved DeepSort and optimized Yolov5s. Frontiers in Plant Science, 2024; 15: 1417682.

Kumar S, Singh S K, Varshney S, Singh S, Kumar P, Kim B G, et al. Fusion of deep sort and Yolov5 for effective vehicle detection and tracking scheme in real-time traffic management sustainable system. Sustainability, 2023; 15(24): 16869.

Cao Y Y, Chen J, Zhang Z C. A sheep dynamic counting scheme based on the fusion between an improved-sparrow-search YOLOv5x-ECA model and few-shot deepsort algorithm. Computers and Electronics in Agriculture, 2023; 206: 107696.

Zhang D Y, Zhang W H, Cheng T, Zhou X G, Yan Z H, Wu Y H, et al. Detection of wheat scab fungus spores utilizing the Yolov5-ECA-ASFF network structure. Computers and Electronics in Agriculture, 2023; 210: 107953.

Zhang P, Li D L. CBAM+ASFF-YOLOXs: An improved YOLOXs for guiding agronomic operation based on the identification of key growth stages of lettuce. Computers and Electronics in Agriculture, 2022; 203: 107491.

Xu X L, Li W S, Duan Q L. Transfer learning and SE-ResNet152 networks-based for small-scale unbalanced fish species identification. Computers and Electronics in Agriculture, 2021; 180: 105878.

Asadi B, Shamsoddini A. Crop mapping through a hybrid machine learning and deep learning method. Remote Sensing Applications: Society and Environment, 2024; 33: 101090.

Ahmed Z, Nalley L, Brye K, Green V S, Popp M, Shew A M, et al. Winter-time cover crop identification: A remote sensing-based methodological framework for new and rapid data generation. International Journal of Applied Earth Observation and Geoinformation, 2023; 125: 103564.

Chen R Q, Sun L, Chen Z X, Wuyun D J, Sun Z. Early identification of corn and soybean using crop growth curve matching method. Agronomy, 2024; 14(1): 146.

Tian Y H, Zhang K, Hu X B, Lu Y. Crop type recognition of VGI road-side images via hierarchy structure based on semantic segmentation model Deeplabv3+. Displays, 2024; 81: 102574.

Lv M, Su W H. YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection. Frontiers in Plant Science, 2024; 14: 1323301.

Xu H Y, Song J, Zhu Y Q. Evaluation and Comparison of Semantic Segmentation Networks for Rice Identification Based on Sentinel-2 Imagery. Remote Sensing, 2023; 15(6): 1499.

Wang Z, Liu D, Wang Z, Liao X, Zhang Q. A new remote sensing change detection data augmentation method based on mosaic simulation and haze image simulation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023; 16: 4579–4590.

Li R, Wu Y P. Improved YOLO v5 wheat ear detection algorithm based on attention mechanism. Electronics, 2022; 11(11): 1673.

Lin Y H, Hu W X, Zheng Z H, Xiong J T. Citrus identification and counting algorithm based on improved YOLOv5s and DeepSort. Agronomy, 2023; 13: 1674.

García-Navarrete O L, Santamaria O, Martín-Ramos P, Valenzuela-Mahecha M Á, Navas-Gracia L M. Development of a detection system for types of weeds in maize (Zea mays L.) under greenhouse conditions using the YOLOv5 v7. 0 model. Agriculture, 2024; 14(2): 286.

Li L P, Zhao H, Liu N. MCD-Yolov5: accurate, real-time crop disease and Pest identification approach using UAVs. Electronics, 2023; 12(20): 4365.




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