
计算机工程与应用 ›› 2025, Vol. 61 ›› Issue (12): 12-27.DOI: 10.3778/j.issn.1002-8331.2411-0108
张少侠,闫建伟,蒙超,石国照,吴锦涛
出版日期:2025-06-15
发布日期:2025-06-13
ZHANG Shaoxia, YAN Jianwei, MENG Chao, SHI Guozhao, WU Jintao
Online:2025-06-15
Published:2025-06-13
摘要: 随着智能驾驶技术的发展,农业车辆和机器人自主导航技术成为当前研究热点,搭载机器视觉导航系统的农业车辆及机器人也广泛应用在农业生产任务中,但在复杂农业环境中的应用仍存在问题。为此,对农业视觉导航技术进行总结,详细论述了视觉导航图像采集技术;对基于图像分割和作物特征点检测的导航路径提取方法进行讨论,并分析了两种导航路径提取方法共同涉及的导航线拟合方法。最后,讨论了农业车辆及机器人视觉导航当前所面临的挑战和未来发展趋势,可为农业车辆及机器人视觉导航相关研究提供参考。
张少侠, 闫建伟, 蒙超, 石国照, 吴锦涛. 基于机器视觉的农业车辆及机器人导航技术研究进展[J]. 计算机工程与应用, 2025, 61(12): 12-27.
ZHANG Shaoxia, YAN Jianwei, MENG Chao, SHI Guozhao, WU Jintao. Research Progress of Agricultural Vehicle and Robot Navigation Technology Based on Machine Vision[J]. Computer Engineering and Applications, 2025, 61(12): 12-27.
| [1] SUBEESH A, MEHTA C R. Automation and digitization of agriculture using artificial intelligence and Internet of Things[J]. Artificial Intelligence in Agriculture, 2021, 5: 278-291. [2] YAO Z X, ZHAO C J, ZHANG T H. Agricultural machinery automatic navigation technology[J]. iScience, 2024, 27(2): 108714. [3] MALAVAZI F B P, GUYONNEAU R, FASQUEL J B, et al. LiDAR-only based navigation algorithm for an autonomous agricultural robot[J]. Computers and Electronics in Agriculture, 2018, 154: 71-79. [4] ZHANG Q, CHEN Q J, XU Z P, et al. Evaluating the navigation performance of multi-information integration based on low-end inertial sensors for precision agriculture[J]. Precision Agriculture, 2021, 22(3): 627-646. [5] DE PONTE MüLLER F. Survey on ranging sensors and cooperative techniques for relative positioning of vehicles[J]. Sensors, 2017, 17(2): 271. [6] SHI Y G, GUO Y, MI Z Q, et al. Stereo CenterNet-based 3D object detection for autonomous driving[J]. Neurocomputing, 2022, 471: 219-229. [7] XIE B B, JIN Y C, FAHEEM M, et al. Research progress of autonomous navigation technology for multi?agricultural scenes[J]. Computers and Electronics in Agriculture, 2023, 211: 107963. [8] 刘悦,李化义,张世杰,等. 面向视觉惯导的导航系统初始化技术综述[J]. 计算机工程与应用, 2025, 61(2): 1-18. LIU Y, Li H Y, ZHANG S J, et al. Review of Initialization Technology for Visual-Inertial Navigation Systems[J]. Computer Engineering and Applications, 2025, 61(2): 1-18. [9] LI C L, PAN Y L, LI D F, et al. A curved path extraction method using RGB-D multimodal data for single-edge guided navigation in irregularly shaped fields[J]. Expert Systems with Applications, 2024, 255: 124586. [10] OLORUNFEMI B O, NWULU N I, ADEBO O A, et al. Advancements in machine visions for fruit sorting and grading: a bibliometric analysis, systematic review, and future research directions[J]. Journal of Agriculture and Food Research, 2024, 16: 101154. [11] CHEN J Q, QIANG H, WU J H, et al. Navigation path extraction for greenhouse cucumber-picking robots using the prediction?point Hough transform[J]. Computers and Electronics in Agriculture, 2021, 180: 105911. [12] SHAO Y D, LI L, LI J, et al. Out-of-plane full-field vibration displacement measurement with monocular computer vision[J]. Automation in Construction, 2024, 165: 105507. [13] YUN C, KIM H J, JEON C W, et al. Stereovision-based ridge-furrow detection and tracking for auto-guided cultivator[J]. Computers and Electronics in Agriculture, 2021, 191: 106490. [14] WANG H Y, LI D X, WU C D, et al. Depth perception of moving objects viaing structured light sensor with unstructured grid[J]. Results in Physics, 2019, 13: 102163. [15] FAN J F, JING F S, YANG L, et al. A precise seam tracking method for narrow butt seams based on structured light vision sensor[J]. Optics & Laser Technology, 2019, 109: 616-626. [16] YANG Y Y, HAN Y X, LI S, et al. Vision based fruit recognition and positioning technology for harvesting robots[J]. Computers and Electronics in Agriculture, 2023, 213: 108258. [17] BAI Y H, ZHANG B H, XU N M, et al. Vision-based navigation and guidance for agricultural autonomous vehicles and robots: a review[J]. Computers and Electronics in Agriculture, 2023, 205: 107584. [18] CHEN S X, NOGUCHI N. Remote safety system for a robot tractor using a monocular camera and a YOLO-based method[J]. Computers and Electronics in Agriculture, 2023, 215: 108409. [19] WANG Z L, DING Y F, ZHANG T H, et al. Automatic real-time fire distance, size and power measurement driven by stereo camera and deep learning[J]. Fire Safety Journal, 2023, 140: 103891. [20] WANG C, CUI X X, ZHAO S J, et al. The application of deep learning in stereo matching and disparity estimation: a bibliometric review[J]. Expert Systems with Applications, 2024, 238: 122006. [21] CONDOTTA I C F S, BROWN-BRANDL T M, PITLA S K, et al. Evaluation of low-cost depth cameras for agricultural applications[J]. Computers and Electronics in Agriculture, 2020, 173: 105394. [22] LI Y M, HONG Z J, CAI D Q, et al. A SVM and SLIC based detection method for paddy field boundary line[J]. Sensors, 2020, 20(9): 2610. [23] CADENAT V, DURAND-PETITEVILLE A, BILLOT D, et al. Image-based tree detection for autonomous navigation in orchards[C]//Proceedings of the 2023 Latin American Robotics Symposium (LARS), 2023 Brazilian Symposium on Robotics (SBR), and 2023 Workshop on Robotics in Education. Piscataway: IEEE, 2023: 272-277. [24]潘良,曹中华,董继伟,等. 基于机器视觉的马铃薯收获导航线检测方法[J/OL]. 农机化研究 (2024-12-19) [2025-01-22].https://link.cnki.net/urlid/23.1233.S.20241218.1620.019. PAN L, CAO Z H, DONG J W, et al. Detection me-thod of navigation line of potato harvesting based on machine vision[J/OL]. Journal of Agricultural Mechan-ization Research (2024?12?19) [2025?01?22]. https://link.cnki.net/urlid/23. 1233.S.20241218.1620.019. [25] AHMADI A, NARDI L, CHEBROLU N, et al. Visual servoing-based navigation for monitoring row-crop fields[C]//Proceedings of the 2020 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2020: 4920-4926. [26] AHMADI A, HALSTEAD M, MCCOOL C. Towards autonomous visual navigation in arable fields[C]//Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE, 2022: 6585-6592. [27] VROCHIDOU E, OUSTADAKIS D, KEFALAS A, et al. Computer vision in self-steering tractors[J]. Machines, 2022, 10(2): 129. [28] BHUJADE V G, SAMBHE V, BANERJEE B. Digital image noise removal towards soybean and cotton plant disease using image processing filters[J]. Expert Systems with Applications, 2024, 246: 123031. [29] KUMAR D, KUKREJA V. Image segmentation, classification, and recognition methods for wheat diseases: two Decades’ systematic literature review[J]. Computers and Electronics in Agriculture, 2024, 221: 109005. [30] LOTUFO R A, AUDIGIER R, SAúDE A V, et al. Chapter six-morphological image processing[M]//Microscope image processing. 2nd ed. [S.l.]:Academic Press, 2023: 75-117. [31] ZHANG X C, CHEN B Q, LI J B, et al. Novel method of the visual navigation path detection of jujube harvester autopilot based on image processing[J]. International Journal of Agricultural and Biological Engineering, 2023, 16(5): 198-203. [32] LIU L, CHEN H, CHU S, et al. The method of coordinate recognition for maize straws under canopy by monocular vision[C]//Proceedings of the 2016 2nd International Conference on Control, Automation and Robotics. Piscataway: IEEE, 2016: 304-307. [33] LIU X H, ZHANG Z, IGATHINATHANE C, et al. Infield corn kernel detection using image processing, machine learning, and deep learning methodologies under natural lighting[J]. Expert Systems with Applications, 2024, 238: 122278. [34] SHELKE S K, SINHA S K, PATEL G S. Development of complete image processing system including image filtering, image compression & image security[J]. Materials Today: Proceedings, 2023, 80: 2167-2171. [35] ILYIN O. Lattice Boltzmann model for diffusion equation with reduced truncation errors: applications to Gaussian filtering and image processing[J]. Applied Mathematics and Computation, 2023, 456: 128123. [36] SUN Z Z, HU D, XIE L J, et al. Detection of early stage bruise in apples using optical property mapping[J]. Computers and Electronics in Agriculture, 2022, 194: 106725. [37] ZHU M L, WU X K, QI J, et al. Research on image data filtering methods for extreme environments after the nuclear leak accident[J]. Nuclear Engineering and Technology, 2024, 56(10): 4227-4236. [38] TIAN C W, ZHENG M H, ZUO W M, et al. Multi-stage image denoising with the wavelet transform[J]. Pattern Reco-gnition, 2023, 134: 109050. [39] MONTALVO M, PAJARES G, GUERRERO J M, et al. Automatic detection of crop rows in maize fields with high weeds pressure[J]. Expert Systems with Applications, 2012, 39(15): 11889-11897. [40] CHEN J Q, QIANG H, WU J H, et al. Extracting the navigation path of atomato-cucumbergreenhouse robot based on a Median point Hough transform[J]. Computers and Electronics in Agriculture, 2020, 174: 105472. [41] JANA B R, THOTAKURA H, BALIYAN A, et al. Pixel density based trimmed Median filter for removal of noise from surface image[J]. Applied Nanoscience, 2023, 13(2): 1017-1028. [42] BOSSU J, GéE C, JONES G, et al. Wavelet transform to discriminate between crop and weed in perspective agronomic images[J]. Computers and Electronics in Agriculture, 2009, 65(1): 133-143. [43] UESUGI F. Novel image processing method inspired by wavelet transform[J]. Micron, 2023, 168: 103442. [44] YU J Y, ZHANG J Y, SHU A J, et al. Study of convolutional neural network?based semantic segmentation methods on edge intelligence devices for field agricultural robot navigation line extraction[J]. Computers and Electronics in Agriculture, 2023, 209: 107811. [45] ZHOU Y. A serial semantic segmentation model based on encoder-decoder architecture[J]. Knowledge-Based Systems, 2024, 295: 111819. [46] SODJINOU S G, MOHAMMADI V, SANDA MAHAMA A T, et al. A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images[J]. Information Processing in Agriculture, 2022, 9(3): 355-364. [47] HASSANAT A B A, ALKASASSBEH M, AL-AWADI M, et al. Color-based object segmentation method using artificial neural network[J]. Simulation Modelling Practice and Theory, 2016, 64: 3-17. [48] WANG Z, CHEN W, XING J H, et al. Extracting vegetation information from high dynamic range images with shadows: a comparison between deep learning and threshold methods[J]. Computers and Electronics in Agriculture, 2023, 208: 107805. [49] YANG X, WANG R, ZHAO D, et al. Multi-level threshold segmentation framework for breast cancer images using enhanced differential evolution[J]. Biomedical Signal Processing and Control, 2023, 80: 104373. [50] ZHANG X F, SUN Y J, LIU H, et al. Improved clustering algorithms for image segmentation based on non-local information and back projection[J]. Information Sciences, 2021, 550: 129-144. [51] HAMUDA E, GLAVIN M, JONES E. A survey of image processing techniques for plant extraction and segmentation in the field[J]. Computers and Electronics in Agriculture, 2016, 125: 184-199. [52] JIANG G Q, WANG Z H, LIU H M. Automatic detection of crop rows based on multi-ROIs[J]. Expert Systems with Applications, 2015, 42(5): 2429-2441. [53] 张志斌, 潘华稳, 李琛, 等. 一种基于平均垄间距的视觉导航垄线识别算法[J]. 计算机工程与应用, 2011, 47(22): 191-194. ZHANG Z B, PAN H W, LI C, et al. Crop rows identification based-row interval for field vision guidance system[J]. Computer Engineering and Applications, 2011, 47(22): 191-194. [54] HE C X, CHEN Q T, MIAO Z H, et al. Extracting the navigation path of an agricultural plant protection robot based on machine vision[C]//Proceedings of the 2021 40th Chinese Control Conference. Piscataway: IEEE, 2021: 3576-3581. [55] ZHU D L, SHEN J Y, ZHENG Y Y, et al. Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation[J]. Computers in Biology and Medicine, 2024, 176: 108498. [56] ELEN A, D?NMEZ E. Histogram-based global thresholding method for image binarization[J]. Optik, 2024, 306: 171814. [57] TIAN K, LI J H, ZENG J F, et al. Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm[J]. Computers and Electronics in Agriculture, 2019, 165: 104962. [58] MAO J D, NIU W Q, WANG H Y, et al. A agricultural spraying and fertilization robot based on visual navigation[C]//Proceedings of the 2020 15th IEEE Conference on Industrial Electronics and Applications. Piscataway: IEEE, 2020: 586-591. [59] MIAO Y L, LI S, WANG L Y, et al. A single plant segmentation method of maize point cloud based on Euclidean clustering and K-means clustering[J]. Computers and Electronics in Agriculture, 2023, 210: 107951. [60] LUO Z F, YANG W Z, YUAN Y F, et al. Semantic segmentation of agricultural images: a survey[J]. Information Processing in Agriculture, 2024, 11(2): 172-186. [61] XU X F, XU T, LI Z T, et al. SPMUNet: semantic segmentation of citrus surface defects driven by superpixel feature[J]. Computers and Electronics in Agriculture, 2024, 224: 109182. [62] YANG B, YANG S, WANG P, et al. FRPNet: an improved Faster-ResNet with PASPP for real-time semantic segmentation in the unstructured field scene[J]. Computers and Electronics in Agriculture, 2024, 217: 108623. [63] HASAN A S M M, SOHEL F, DIEPEVEEN D, et al. A survey of deep learning techniques for weed detection from images[J]. Computers and Electronics in Agriculture, 2021, 184: 106067. [64] ZOU K L, CHEN X, WANG Y L, et al. A modified U-Net with a specific data argumentation method for semantic segmentation of weed images in the field[J]. Computers and Electronics in Agriculture, 2021, 187: 106242. [65] ZHOU D Y, LI M, LI Y, et al. Detection of ground straw coverage under conservation tillage based on deep learning[J]. Computers and Electronics in Agriculture, 2020, 172: 105369. [66] AHMAD RAHMANI A, SHIRAZI A A B, BEHNAM H. Automatic breast mass segmentation in ultrasound images with U-Net and resolution enhancement blocks[J]. Biomedical Signal Processing and Control, 2024, 94: 106270. [67] SUN J W, ZHOU J, HE Y Q, et al. RL-DeepLabv3+: a lightweight rice lodging semantic segmentation model for unmanned rice harvester[J]. Computers and Electronics in Agriculture, 2023, 209: 107823. [68] ALMEIDA T, LOUREN?O B, SANTOS V. Road detection based on simultaneous deep learning approaches[J]. Robotics and Autonomous Systems, 2020, 133: 103605. [69] FIRUZINIA S, AFZALI S M, GHASEMIAN F, et al. A robust deep learning-based multiclass segmentation method for analyzing human metaphase II oocyte images[J]. Computer Methods and Programs in Biomedicine, 2021, 201: 105946. [70] GENZE N, AJEKWE R, GüRELI Z, et al. Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields[J]. Computers and Electronics in Agriculture, 2022, 202: 107388. [71] MWITTA C, RAINS G C. The integration of GPS and visual navigation for autonomous navigation of an ackerman steering mobile robot in cotton fields[J]. Frontiers in Robotics and AI, 2024, 11: 1359887. [72] JIANG M X, DENG C, SHAN J S, et al. Hierarchical multi-modal fusion FCN with attention model for RGB-D tracking[J]. Information Fusion, 2019, 50: 1-8. [73] ZHANG L, LI M, ZHU X H, et al. Navigation path recognition between rows of fruit trees based on semantic segmentation[J]. Computers and Electronics in Agriculture, 2024, 216: 108511. [74] GUO Z M, GENG Y H, WANG C, et al. InstaCropNet: an efficient Unet-based architecture for precise crop row detection in agricultural applications[J]. Artificial Intelligence in Agriculture, 2024, 12: 85-96. [75] SKOCZE? M, OCHMAN M, SPYRA K, et al. Obstacle detection system for agricultural mobile robot application using RGB-D cameras[J]. Sensors, 2021, 21(16): 5292. [76] LI J Y, JIANG F L, YANG J, et al. Lane-DeepLab: lane semantic segmentation in automatic driving scenarios for high-definition maps[J]. Neurocomputing, 2021, 465: 15-25. [77] LIN Y K, CHEN S F, KUO Y F, et al. Developing a guiding and growth status monitoring system for riding-type tea plucking machine using fully convolutional networks[J]. Computers and Electronics in Agriculture, 2021, 191: 106540. [78] GAO X, BAI H L, XIONG Y J, et al. Robust lane line segmentation based on group feature enhancement[J]. Engineering Applications of Artificial Intelligence, 2023, 117: 105568. [79] JIN Z Y, DOU F R, FENG Z L, et al. BSNet: a bilateral real-time semantic segmentation network based on multi-scale receptive fields[J]. Journal of Visual Communication and Image Representation, 2024, 102: 104188. [80] WANG H, MA Z F, REN Y X, et al. Interactive image segmentation based field boundary perception method and software for autonomous agricultural machinery path planning[J]. Computers and Electronics in Agriculture, 2024, 217: 108568. [81] TASSIS L M, TOZZI DE SOUZA J E, KROHLING R A. A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images[J]. Computers and Electronics in Agriculture, 2021, 186: 106191. [82] PRIYADHARSINI R, SHARMILA T S. Object detection in underwater acoustic images using edge based segmentation method[J]. Procedia Computer Science, 2019, 165: 759-765. [83] YANG D P, PENG B, AL-HUDA Z, et al. An overview of edge and object contour detection[J]. Neurocomputing, 2022, 488: 470-493. [84] IGLESIAS F, AGUILERA A, PADILLA A, et al. Application of computer vision techniques to estimate surface roughness on wood-based sanded workpieces[J]. Measurement, 2024, 224: 113917. [85] SARKAR P, GHOSAL S K, SARKAR M. Stego-chain: a framework to mine encoded stego-block in a decentralized network[J]. Journal of King Saud University-Computer and Information Sciences, 2022, 34(8): 5349-5365. [86] VARDHANA M, ARUNKUMAR N, LASRADO S, et al. Convolutional neural network for bio-medical image segmentation with hardware acceleration[J]. Cognitive Systems Research, 2018, 50: 10-14. [87] EL-MOWAFY M A, GHARGHORY S M, ABO-ELSOUD M A, et al. Efficient mode decision scheme based on edge detection with Gaussian pulse for Intra-prediction in H.264/AVC[J]. Alexandria Engineering Journal, 2022, 61(4): 2709-2722. [88] GAO W S, ZHANG X G, YANG L, et al. An improved Sobel edge detection[C]//Proceedings of the 2010 3rd International Conference on Computer Science and Information Technology. Piscataway: IEEE, 2010: 67-71. [89] YAO G L, SUN A M. Multi-guided-based image matting via boundary detection[J]. Computer Vision and Image Understanding, 2024, 243: 103998. [90] GANESAN P, SAJIV G. A comprehensive study of edge detection for image processing applications[C]//Proceedings of the 2017 International Conference on Innovations in Information, Embedded and Communication Systems. Piscataway: IEEE, 2017: 1-6. [91] RAVIVARMA G, GAVASKAR K, MALATHI D, et al. Implementation of Sobel operator based image edge detection on FPGA[J]. Materials Today: Proceedings, 2021, 45: 2401-2407. [92] ZHANG W C, ZHAO Y L, BRECKON T P, et al. Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels[J]. Pattern Recognition, 2017, 63: 193-205. [93] LIANG X, CHEN B Q, WEI C J, et al. Inter-row navigation line detection for cotton with broken rows[J]. Plant Methods, 2022, 18(1): 1-12. [94] LU W, ZENG M J, WANG L, et al. Navigation algorithm based on the boundary line of tillage soil combined with guided filtering and improved anti-noise morphology[J]. Sensors, 2019, 19(18): 3918. [95] PENG-O T, CHAIKAN P. High performance and energy efficient sobel edge detection[J]. Microprocessors and Microsystems, 2021, 87: 104368. [96] WEI P C, YU X P, DI Z P, et al. Design of robot automatic navigation under computer intelligent algorithm and machine vision[J]. Journal of Industrial Information Integration, 2022, 28: 100366. [97] ZHOU C S, YUAN C, WANG H X, et al. Multi-scale pseudo labeling for unsupervised deep edge detection[J]. Knowledge-Based Systems, 2023, 280: 111057. [98] ZHANG X, LIN C, LI F Z, et al. LVP-Net: a deep network of learning visual pathway for edge detection[J]. Image and Vision Computing, 2024, 147: 105078. [99] LUO Z Q, LIN C, LI F Z, et al. BLEDNet: bio?inspired lightweight neural network for edge detection[J]. Engineering Applications of Artificial Intelligence, 2023, 124: 106530. [100] XIAN R H, XIONG X, PENG H, et al. Feature fusion method based on spiking neural convolutional network for edge detection[J]. Pattern Recognition, 2024, 147: 110112. [101] AVILéS-MEJIA J E, SOTO D, STéPHANT J, et al. Autonomous vision-based navigation and control for intra-row weeding[C]//Proceedings of the 2022 IEEE 18th International Conference on Automation Science and Engineering. Piscataway: IEEE, 2022: 575-582. [102] YANG R B, ZHAI Y M, ZHANG J, et al. Potato visual navigation line detection based on deep learning and feature midpoint adaptation[J]. Agriculture, 2022, 12(9): 1363. [103] 蒋林, 方东君, 雷斌, 等. 单目视觉移动机器人导航算法研究现状及趋势[J]. 计算机工程与应用, 2021, 57(5): 1-9. JIANG L, FANG D J, LEI B, et al. Research status and trend of navigation algorithm for mobile robot with monocular vision[J]. Computer Engineering and Applications, 2021, 57(5): 1-9. [104] LI Y L, ZHENG W F, LIU X J, et al. Research and improvement of feature detection algorithm based on FAST[J]. Rendiconti Lincei Scienze Fisiche e Naturali, 2021, 32(4): 775-789. [105] ZHANG Q, SHAOJIE CHEN M E, LI B. A visual navigation algorithm for paddy field weeding robot based on image understanding[J]. Computers and Electronics in Agriculture, 2017, 143: 66-78. [106] GONG H L, ZHUANG W D, WANG X. Improving the maize crop row navigation line recognition method of YOLOX[J]. Frontiers in Plant Science, 2024, 15: 1338228. [107] QIAO M Y, LIANG X, CHEN M J. Improved SIFT algorithm based on image filtering[J]. Journal of Physics: Conference Series, 2021, 1848(1): 012069. [108] ZHANG Z B, LI P, ZHAO S L, et al. An adaptive vision navigation algorithm in agricultural IoT system for smart agricultural robots[J]. Computers, Materials & Continua, 2020, 66(1): 1043-1056. [109] ZHAI Z Q, ZHU Z X, DU Y F, et al. Multi-crop-row detection algorithm based on binocular vision[J]. Biosystems Engineering, 2016, 150: 89-103. [110] YANG Y, ZHOU Y, YUE X, et al. Real-time detection of crop rows in maize fields based on autonomous extraction of ROI[J]. Expert Systems with Applications, 2023, 213: 118826. [111] YAO L J, HU D, YANG Z D, et al. Depth recovery for unstructured farmland road image using an improved SIFT algorithm[J]. International Journal of Agricultural and Biological Engineering, 2019, 12(4): 141-147. [112] HU Y, HUANG H. Extraction method for centerlines of crop row based on improved lightweight YOLOv4[C]//Proceedings of the 2021 6th International Symposium on Computer and Information Processing Technology. Piscataway: IEEE, 2021: 127-132. [113] LIU T H, ZHENG Y, LAI J S, et al. Extracting visual navigation line between pineapple field rows based on an enhanced YOLOv5[J]. Computers and Electronics in Agriculture, 2024, 217: 108574. [114] DIAO Z H, GUO P L, ZHANG B H, et al. Navigation line extraction algorithm for corn spraying robot based on improved YOLOv8s network[J]. Computers and Electronics in Agriculture, 2023, 212: 108049. [115] WANG S, SU D, JIANG Y Y, et al. Fusing vegetation index and ridge segmentation for robust vision based autonomous navigation of agricultural robots in vegetable farms[J]. Computers and Electronics in Agriculture, 2023, 213: 108235. [116] MA Y, ZHANG W Q, QURESHI W S, et al. Autonomous navigation for a wolfberry picking robot using visual cues and fuzzy control[J]. Information Processing in Agriculture, 2021, 8(1): 15-26. [117] 许贞辉, 李晓娟. 果园树干检测与导航线拟合算法研究[J]. 中国农机化学报, 2024, 45(8): 217-222. XU Z H, LI X J. Research on tree trunk detection and navigation line fitting algorithm in orchard[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(8): 217-222. [118] LIU X N, QI J T, ZHANG W R, et al. Recognition method of maize crop rows at the seedling stage based on MS-ERFNet model[J]. Computers and Electronics in Agriculture, 2023, 211: 107964. [119] HE Y, ZHANG X Y, ZHANG Z Q, et al. Automated detection of boundary line in paddy field using MobileV2-U?Net and RANSAC[J]. Computers and Electronics in Agriculture, 2022, 194: 106697. [120] LI Y M, GANS N R. Predictive RANSAC: effective model fitting and tracking approach under heavy noise and outliers[J]. Computer Vision and Image Understanding, 2017, 161: 99-113. [121] JI R H, QI L J. Crop-row detection algorithm based on random Hough transformation[J]. Mathematical and Computer Modelling, 2011, 54(3/4): 1016-1020. [122] JIANG G Q, WANG X J, WANG Z H, et al. Wheat rows detection at the early growth stage based on Hough transform and vanishing point[J]. Computers and Electronics in Agriculture, 2016, 123: 211-223. [123] DIAO Z H, GUO P L, ZHANG B H, et al. Maize crop row recognition algorithm based on improved UNet network[J]. Computers and Electronics in Agriculture, 2023, 210: 107940. [124] MA Z H, YIN C, DU X Q, et al. Rice row tracking control of crawler tractor based on the satellite and visual integrated navigation[J]. Computers and Electronics in Agriculture, 2022, 197: 106935. [125] WU W T, ZHANG Z Q, ZHANG X Y, et al. Application of visual inertia fusion technology in rice transplanter operation[J]. Computers and Electronics in Agriculture, 2024, 221: 108990. [126] CAO M Y, TANG F F, JI P, et al. Improved real-time semantic segmentation network model for crop vision navigation line detection[J]. Frontiers in Plant Science, 2022, 13: 898131. [127] YANG Z, OUYANG L, ZHANG Z G, et al. Visual navigation path extraction of orchard hard pavement based on scanning method and neural network[J]. Computers and Electronics in Agriculture, 2022, 197: 106964. [128] WU S L, CHEN Z G, BANGURA K, et al. A navigation method for paddy field management based on seedlings coordinate information[J]. Computers and Electronics in Agriculture, 2023, 215: 108436. [129] LI X, SU J H, YUE Z C, et al. Adaptive multi-ROI agricultural robot navigation line extraction based on image semantic segmentation[J]. Sensors, 2022, 22(20): 7707. [130] OPIYO S, OKINDA C, ZHOU J, et al. Medial axis-based machine?vision system for orchard robot navigation[J]. Computers and Electronics in Agriculture, 2021, 185: 106153. [131] LI D F, LI B L, FENG H Q, et al. Low-altitude remote sensing-based global 3D path planning for precision navigation of agriculture vehicles?beyond crop row detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, 210: 25-38. [132] DE SILVA R, CIELNIAK G, GAO J F. Vision based crop row navigation under varying field conditions in arable fields[J]. Computers and Electronics in Agriculture, 2024, 217: 108581. [133] XU S L, RAI R. Vision?based autonomous navigation stack for tractors operating in peach orchards[J]. Computers and Electronics in Agriculture, 2024, 217: 108558. [134] LI D F, LI B L, KANG S, et al. E2CropDet: an efficient end-to-end solution to crop row detection[J]. Expert Systems with Applications, 2023, 227: 120345. [135] LI B L, LI D F, WEI Z B, et al. Rethinking the crop row detection pipeline: an end-to-end method for crop row detection based on row-column attention[J]. Computers and Electronics in Agriculture, 2024, 225: 109264. |
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