Navigation line detection algorithm for corn spraying robot based on improved LT-YOLOv10s
文献类型： 外文期刊
作者： Diao, Zhihua 1 ; Ma, Shushuai 1 ; Li, Jiangbo 2 ; Zhang, Jingcheng 3 ; Li, Xingyi 1 ; Zhao, Suna 1 ; He, Yan 1 ; Zhang, Baohua 4 ; Jiang, Liying 5 ;
作者机构： 1.Zhengzhou Univ Light Ind, Coll Elect & Informat Engn, Zhengzhou 450002, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100000, Peoples R China
3.Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310000, Peoples R China
4.Nanjing Agr Univ, Coll Artificial Intelligence, Nanjing 211800, Peoples R China
5.Zhengzhou Univ Light Ind, Sch Elect & Informat, Zhengzhou 450002, Peoples R China
6.Zhengzhou Univ Light Ind, Acad Quantum Sci & Technol, Zhengzhou 450002, Peoples R China
关键词： Deep learning; Corn spraying robot; Navigation line detection; Lightweight network
期刊名称：PRECISION AGRICULTURE （ 影响因子：6.6； 五年影响因子：7.4 ）
ISSN： 1385-2256
年卷期： 2025 年 26 卷 3 期
页码：
收录情况： SCI
摘要： The deep integration of artificial intelligence technology and agriculture has significantly propelled the rapid development of smart agriculture. However, the field still faces numerous challenges, including high algorithm complexity and limited detection speed in farmland environments. To address the challenges encountered by corn spraying robots in navigating and identifying lines, we have proposed a corn crop row navigation line recognition algorithm based on the LT-YOLOv10s model. By introducing lightweight network models (GhosNet), efficient feature pyramid models (SPPFA), and efficient feature attention modules (PSCA) into the YOLOv10s network, we have reduced the complexity of the model and significantly enhanced the detection efficiency of corn plants. Then, the algorithm precisely locates corn plants using the center points of detection boxes and accurately fits crop rows using the least squares method. Finally, the navigation lines centered on the corn crop rows are determined through the adjacent centerline method. Experimental data significantly demonstrates that the comprehensive performance of the LT-YOLOv10s model surpasses industry benchmark models such as YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and the traditional YOLOv10s. The proposed algorithm for extracting the center navigation line of corn crop rows boasts an average fitting time of just 26ms with an accuracy rate of up to 93.8%, ensuring precision and reliability in navigation line extraction. This provides robust technical support for precise navigation of corn-spraying robots.
- 相关文献
作者其他论文 更多>>
-
Determination of the SSC in oranges using Vis-NIR full transmittance hyperspectral imaging and spectral visual coding: A practical solution to the scattering problem of inhomogeneous mixtures
作者：Cai, Letian;Li, Jiangbo;Zhang, Yizhi;Hao, Haoyuan;Cai, Letian;Zhang, Junyi;Zhang, Hailiang;Zhang, Yizhi
关键词：Citrus; SSC detection; Hyperspectral transmittance imaging; Spectral visual coding; Feature selection
-
Hyperspectral transmittance imaging detection of early decayed oranges caused by Penicillium digitatum using NFINDR-JMSAM algorithm with spectral feature separating
作者：Cai, Letian;Chen, Liping;Li, Xuetong;Zhang, Yizhi;Shi, Ruiyao;Li, Jiangbo;Cai, Letian
关键词：Citrus; Decay detection; Hyperspectral transmittance imaging; NFINDR-JMSAM; Spectral separation
-
Construction of a stable YOLOv8 classification model for apple bruising detection based on physicochemical property analysis and structured-illumination reflectance imaging
作者：Zhang, Junyi;Chen, Liping;Cai, Zhonglei;Shi, Ruiyao;Cai, Letian;Li, Jiangbo;Zhang, Junyi;Luo, Liwei;Yang, Xuhai;Li, Jiangbo
关键词：Apple; Bruising detection; Physicochemical property analysis; Structured-illumination reflectance imaging; Deep learning model
-
Smartphone-assisted fluorescent film based on the Flu grafted on Eu-MOF for real-time monitoring of fresh-cut fruit freshness
作者：Zhang, Zhepeng;Gao, Mingjie;Zou, Xiaobo;Guo, Zhiming;Zhang, Liang;Li, Jiangbo;El-Seedi, Hesham R.;Guo, Zhiming;El-Seedi, Hesham R.
关键词：Metal-organic framework; Grafted materials; Multifunctional filler; Fluorescence film; Fresh-cut fruits; Smartphone application
-
Detection of bruising in pear with varying bruising degrees and formation times by using SIRI technique combining with texture feature-based LS-SVM and ResNet-18-based CNN model
作者：Li, Jiangbo;Zhang, Junyi;Mei, Mengwen;Li, Xuetong;Shi, Ruiyao;Cai, Zhonglei;Diao, Zhihua
关键词：Pears; Bruising detection; Convolutional neural network; Machine learning; Enhanced imaging
-
Synchronous detection of internal and external defects of citrus by structured-illumination reflectance imaging coupling with improved YOLO v7
作者：Cai, Zhonglei;Zhang, Yizhi;Li, Jiangbo;Zhang, Junyi;Li, Xuetong;Cai, Zhonglei
关键词：Citrus; Early decay; Structured-illumination; Internal defect; YOLO v7
-
Navigation line extraction algorithm for corn spraying robot based on YOLOv8s-CornNet
作者：Guo, Peiliang;Diao, Zhihua;Ma, Shushuai;He, Zhendong;Zhao, Suna;Zhao, Chunjiang;Li, Jiangbo;Zhang, Ruirui;Yang, Ranbing;Zhang, Baohua
关键词：agricultural robotics; computer vision; deep learning; navigation line extraction; network lightweight
