A visual navigation algorithm for paddy field weeding robot based on image understanding

Zhang, Qin; Chen, M.E. Shaojie; Li, Bin

doi:10.1016/j.compag.2017.09.008

Cited by 70 publications

(28 citation statements)

References 15 publications

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“…Next, it is necessary to obtain depth information d for the 2D coordinate point (f x , f y ) of the target in the depth image aligned with the colored image and obtain the ratio of the depth pixel to the real unit depth scale. In the end, the conversion from the pixel coordinate system to the camera coordinate system can be directly completed by equation (17). So that, the target 2D coordinate point in the pixel coordinate system can be transformed into the 3D coordinate point in the camera coordinate system.…”

Section: Edge Detection Algorithm Based On Cannymentioning

confidence: 99%

“…is method is only used to remove weeds in a certain area. Zhang et al proposed a navigation method for weeding robots based on the smallest univalue segment assimilating nucleus (SUSAN) corner and improved sequential clustering algorithm [17] and does not involve the removal of weeds in paddy fields. Malavaz et al developed a general and robust approach for autonomous robot navigation inside a crop by using light detection and ranging (LiDAR) data [18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Quadratic Traversal Algorithm of Shortest Weeding Path Planning for Agricultural Mobile Robots in Cornfield

Zhang

et al. 2021

Journal of Robotics

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In order to improve the weeding efficiency and protect farm crops, accurate and fast weeds removal guidance to agricultural mobile robots is an utmost important topic. Based on this motivation, we propose a time-efficient quadratic traversal algorithm for the removal guidance of weeds around the recognized corn in the field. To recognize the weeds and corns, a Faster R-CNN neural network is implemented in real-time recognition. Then, an ultra-green characterization (EXG) hyperparameter is used for grayscale image processing. An improved OTSU (IOTSU) algorithm is proposed to accurately generate and optimize the binary image. Compared to the traditional OTSU algorithm, the improved OTSU algorithm effectively shortens the search speed of the algorithm and reduces the calculation processing time by compressing the range of the search grayscale range. Finally, based on the contour of the target plants and the Canny edge detection operator, the shortest weeding path guidance can be calculated by the proposed quadratic traversal algorithm. The experimental results proved that our search success rate can reach 90.0% on the testing date. This result ensured the accurate selection of the target 2D coordinates in the pixel coordinate system. Transforming the target 2D coordinate point in the pixel coordinate system into the 3D coordinate point in the camera coordinate system as well as using a depth camera can achieve multitarget depth ranging and path planning for an optimized weeding path.

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Section: Edge Detection Algorithm Based On Cannymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Quadratic Traversal Algorithm of Shortest Weeding Path Planning for Agricultural Mobile Robots in Cornfield

Zhang

et al. 2021

Journal of Robotics

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“…Thus, how to use the images come from the low resolution imaging sensor for the data analysis purpose is a challenge. To conquer these problems above, on one hand, the special materials and process will be used to improve the performance of the imaging sensor; on the other hand, some typical workflows and data fusion algorithm [52] should be designed according to the environment characters in Mars.…”

Section: The Autonomous Navigation For Mars Exploration 31 the Autonmentioning

confidence: 99%

Autonomous Navigation for Mars Exploration

Liu¹

2020

Mars Exploration - A Step Forward

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The autonomous navigation technology uses the multiple sensors to percept and estimate the spatial locations of the aerospace prober or the Mars rover and to guide their motions in the orbit or the Mars surface. In this chapter, the autonomous navigation methods for the Mars exploration are reviewed. First, the current development status of the autonomous navigation technology is summarized. The popular autonomous navigation methods, such as the inertial navigation, the celestial navigation, the visual navigation, and the integrated navigation, are introduced. Second, the application of the autonomous navigation technology for the Mars exploration is presented. The corresponding issues in the Entry Descent and Landing (EDL) phase and the Mars surface roving phase are mainly discussed. Third, some challenges and development trends of the autonomous navigation technology are also addressed.

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“…Navigation systems are a crucial part of such autonomous robots where a guidance line has to be computed to guide the robot for weed control. Vision sensor-based autonomous guidance systems have been widely researched for extracting the crop lines to guide the robot (Choi et al, 2015; Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Learning Semantic Graphics Using Convolutional Encoder–Decoder Network for Autonomous Weeding in Paddy

2019

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Weeds in agricultural farms are aggressive growers which compete for nutrition and other resources with the crop and reduce production. The increasing use of chemicals to control them has inadvertent consequences to the human health and the environment. In this work, a novel neural network training method combining semantic graphics for data annotation and an advanced encoder–decoder network for (a) automatic crop line detection and (b) weed (wild millet) detection in paddy fields is proposed. The detected crop lines act as a guiding line for an autonomous weeding robot for inter-row weeding, whereas the detection of weeds enables autonomous intra-row weeding. The proposed data annotation method, semantic graphics, is intuitive, and the desired targets can be annotated easily with minimal labor. Also, the proposed “extended skip network” is an improved deep convolutional encoder–decoder neural network for efficient learning of semantic graphics. Quantitative evaluations of the proposed method demonstrated an increment of 6.29% and 6.14% in mean intersection over union (mIoU), over the baseline network on the task of paddy line detection and wild millet detection, respectively. The proposed method also leads to a 3.56% increment in mIoU and a significantly higher recall compared to a popular bounding box-based object detection approach on the task of wild–millet detection.

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A visual navigation algorithm for paddy field weeding robot based on image understanding

Cited by 70 publications

References 15 publications

A Quadratic Traversal Algorithm of Shortest Weeding Path Planning for Agricultural Mobile Robots in Cornfield

A Quadratic Traversal Algorithm of Shortest Weeding Path Planning for Agricultural Mobile Robots in Cornfield

Autonomous Navigation for Mars Exploration

Learning Semantic Graphics Using Convolutional Encoder–Decoder Network for Autonomous Weeding in Paddy

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