An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

Zhang, Zhibin; Li, Ping; Zhao, Shuailing; Lv, Zhimin; Du, Fang; An, Yajian

doi:10.32604/cmc.2020.012517

Cited by 22 publications

(13 citation statements)

References 28 publications

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“…The results indicated a mean deviation between the actual middle of the road and traveled trajectory of 0.031 m, 0.069 m and 0.105 m, for straight, multicurvature and undulating roads, respectively. An adaptive binocular vision-based algorithm was proposed in [86]. Experiments on S-type and O-type paths resulted in an absolute mean of turning angle of 0.7 • and an absolute standard deviation of 1.5 • for navigation speeds less than 0.5 m/s.…”

Section: Binocular Vision Methodsmentioning

confidence: 99%

Computer Vision in Self-Steering Tractors

et al. 2022

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Automatic navigation of agricultural machinery is an important aspect of Smart Farming. Intelligent agricultural machinery applications increasingly rely on machine vision algorithms to guarantee enhanced in-field navigation accuracy by precisely locating the crop lines and mapping the navigation routes of vehicles in real-time. This work presents an overview of vision-based tractor systems. More specifically, this work deals with (1) the system architecture, (2) the safety of usage, (3) the most commonly faced navigation errors, (4) the navigation control system of tractors and presents (5) state-of-the-art image processing algorithms for in-field navigation route mapping. In recent research, stereovision systems emerge as superior to monocular systems for real-time in-field navigation, demonstrating higher stability and control accuracy, especially in extensive crops such as cotton, sunflower, maize, etc. A detailed overview is provided for each topic with illustrative examples that focus on specific agricultural applications. Several computer vision algorithms based on different optical sensors have been developed for autonomous navigation in structured or semi-structured environments, such as orchards, yet are affected by illumination variations. The usage of multispectral imaging can overcome the encountered limitations of noise in images and successfully extract navigation paths in orchards by using a combination of the trees’ foliage with the background of the sky. Concisely, this work reviews the current status of self-steering agricultural vehicles and presents all basic guidelines for adapting computer vision in autonomous in-field navigation.

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Section: Binocular Vision Methodsmentioning

confidence: 99%

Computer Vision in Self-Steering Tractors

et al. 2022

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“…The authorsMachine vision technology was applied for the multi-target recognition of bananas and automatic positioning for the inflorescence axis cutting point (Wu et al, 2021); in addition, the improved YOLOv4 (You Only Look Once, version 4) micromodel and binocular stereo vision technology were applied for fruit detection and location (Wang et al, 2022;Tang et al, 2023). Zhang et al proposed an inter-row information recognition algorithm for an intelligent agricultural robot based on binocular vision, where the effective inter-row navigation information was extracted by fusing the edge contour and height information of crop rows in the image (Zhang et al, 2020). By setting the region of interest, Yang et al used machine vision to accurately identify the crop lines between rows in the early growth stage of maize and extracted the navigation path of the plant protection robot in real time (Yang et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Inter-row information recognition of maize in the middle and late stages via LiDAR supplementary vision

Xie

Liu

et al. 2022

Front. Plant Sci.

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In the middle and late stages of maize, light is limited and non-maize obstacles exist. When a plant protection robot uses the traditional visual navigation method to obtain navigation information, some information will be missing. Therefore, this paper proposed a method using LiDAR (laser imaging, detection and ranging) point cloud data to supplement machine vision data for recognizing inter-row information in the middle and late stages of maize. Firstly, we improved the YOLOv5 (You Only Look Once, version 5) algorithm based on the characteristics of the actual maize inter-row environment in the middle and late stages by introducing MobileNetv2 and ECANet. Compared with that of YOLOv5, the frame rate of the improved YOLOv5 (Im-YOLOv5) increased by 17.91% and the weight size decreased by 55.56% when the average accuracy was reduced by only 0.35%, improving the detection performance and shortening the time of model reasoning. Secondly, we identified obstacles (such as stones and clods) between the rows using the LiDAR point cloud data to obtain auxiliary navigation information. Thirdly, the auxiliary navigation information was used to supplement the visual information, so that not only the recognition accuracy of the inter-row navigation information in the middle and late stages of maize was improved but also the basis of the stable and efficient operation of the inter-row plant protection robot was provided for these stages. The experimental results from a data acquisition robot equipped with a camera and a LiDAR sensor are presented to show the efficacy and remarkable performance of the proposed method.

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“…These, and the many other papers that use alternative but equivalent terminology, span a variety of approaches and applications, from terrestrial [ 4 , 5 , 6 ], to aeronautical [ 7 , 8 , 9 , 10 , 11 ], space [ 12 , 13 , 14 , 15 ]. In some instances, the search terms appear in reference to the technology being used for a specific application, e.g., precision agriculture [ 16 , 17 , 18 , 19 ] and construction [ 20 , 21 ]. In others, reference is made to implementation and algorithms.…”

Section: Introductionmentioning

confidence: 99%

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

D’Adamo

Phillips

McĂree

2022

Sensors

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The requirement to estimate the six degree-of-freedom pose of a moving platform frequently arises in automation applications. It is common to estimate platform pose by the fusion of global navigation satellite systems (GNSS) measurements and translational acceleration and rotational rate measurements from an inertial measurement unit (IMU). This paper considers a specific situation where two GNSS receivers and one IMU are used and gives the full formulation of a Kalman filter-based estimator to do this. A limitation in using this sensor set is the difficulty of obtaining accurate estimates of the degree of freedom corresponding to rotation about the line passing through the two GNSS receiver antenna centres. The GNSS-aided IMU formulation is extended to incorporate LiDAR measurements in both known and unknown environments to stabilise this degree of freedom. The performance of the pose estimator is established by comparing expected LiDAR range measurements with actual range measurements. Distributions of the terrain point-to-model error are shown to improve from 0.27m mean error to 0.06m when the GNSS-aided IMU estimator is augmented with LiDAR measurements. This precision is marginally degraded to 0.14m when the pose estimator is operated in an a prior unknown environment.

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An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

Cited by 22 publications

References 28 publications

Computer Vision in Self-Steering Tractors

Computer Vision in Self-Steering Tractors

Inter-row information recognition of maize in the middle and late stages via LiDAR supplementary vision

LiDAR-Stabilised GNSS-IMU Platform Pose Tracking

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