Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review

Bai, Yuhao; Zhang, Baohua; Xu, Naimin; Zhou, Jun; Jiayou, Shi; Diao, Zhihua

doi:10.1016/j.compag.2022.107584

Cited by 112 publications

(33 citation statements)

References 201 publications

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“…Orchards are typically agricultural lands that are planted with relatively tall trees or shrubs, which belong to a semi-structured environment. Many tasks, including monitoring, management, and harvesting, cannot be performed without the aid of orchard mobile vehicles and robotic autonomous navigation platforms [214]. Fruit tree row detection can help the robot to accurately locate the position and shape of fruit tree rows, and improve the accuracy of the robot's autonomous navigation, thus helping fruit farmers to better manage their orchards and improve the yield and quality of fruit trees.…”

Section: Applications Of Row Detection In Orchardsmentioning

confidence: 99%

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

et al. 2023

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Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported.

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Section: Applications Of Row Detection In Orchardsmentioning

confidence: 99%

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

et al. 2023

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“…Each of these existing systems presents a cost-benefit trade-off which results in a lack of reliable navigation options for an affordable solution in crop row navigation. The vision-based infield navigation technologies available for arable field navigation explores the usage of RGB and depth images to identify the crop rows [1], [10]. The existing systems for such infield navigation mainly uses image segmentation, object detection or image matching methods to identify the crop rows for robot navigation [7].…”

Section: Related Workmentioning

confidence: 99%

Towards Infield Navigation: leveraging simulated data for crop row detection

Silva

Cielniak

Gao

2022

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

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Usage of purely vision based solutions for row switching is not well explored in existing vision based crop row navigation frameworks. This method only uses RGB images for local feature matching based visual feedback to exit crop row. Depth images were used at crop row end to estimate the navigation distance within headland. The algorithm was tested on diverse headland areas with soil and vegetation. The proposed method could reach the end of the crop row and then navigate into the headland completely leaving behind the crop row with an error margin of 50 cm.

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“…The most recent advancements in deep learning methods have gained more accurate calculation results. However, the technical bottlenecks and limitations inherent in achieving fully automated vehicles (L5) must be addressed, and the frameworks of ICVs and vehicle to everything (V2X) systems need to be optimized [ 24 ] to provide additional environment information and computing resources from roadside infrastructures [ 25 ]. A vehicle–infrastructure cooperation system (VICS) is defined as the coupling and collaboration of four key elements in the road traffic system: human, vehicle, road, environment.…”

Section: Introductionmentioning

confidence: 99%

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Wang,

Wu,

Liang

et al. 2024

Sensors

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Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle–infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle–infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

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Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review

Cited by 112 publications

References 201 publications

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

Towards Infield Navigation: leveraging simulated data for crop row detection

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

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