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Jing Li
Sichuan Engineering Technical College
Corresponding Author
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In the development of modern agriculture, the intelligent use of mechanical equipment is one of the main signs for agricultural modernization. Navigation technology is the key technology for agricultural machinery to control autonomously in operating environment, and it is a hotspot in the field of intelligent research on agricultural machinery. Facing the accuracy requirements of autonomous navigation for intelligent agricultural robots, this paper proposes a visual navigation algorithm for agricultural robots based on deep learning image understanding. The method first uses cascaded deep convolutional network and hybrid dilated convolution fusion method to process images collected by vision system. Then it extracts the route of processed images based on improved Hough transform algorithm. At the same time, the posture of agricultural robots is adjusted to realize autonomous navigation. Finally, our proposed method is verified by using non-interference experimental scenes and noisy experimental scenes. Experimental results show that the method can perform autonomous navigation in complex and noisy environments, and has good practicability and applicability.
Keywords
Edge computing, Agricultural robot, Cascaded deep convolutional network, Hybrid dilated convolutional network, Improved Hough transform, Farmland image segmentation, Path extraction, Autonomous navigation
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CURRENT STATUS: Under Review
Version 1
Posted 24 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 03 Apr, 2021
Review #2 received
Received 01 Apr, 2021
Review #3 received
Received 26 Mar, 2021
Review #1 received
Received 26 Mar, 2021
Reviewer #2 agreed
On 01 Mar, 2021
Reviewer #3 agreed
On 01 Mar, 2021
Reviews received
Received 01 Mar, 2021
Reviewers invited
Invitations sent on 01 Mar, 2021
Reviewer #1 agreed
On 01 Mar, 2021
Editor assigned
On 21 Feb, 2021
Submission checks complete
On 21 Feb, 2021
Editor invited
On 21 Feb, 2021
First submitted
On 07 Feb, 2021
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A new preprint design is coming!
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This preprint is under consideration at EURASIP Journal on Advances in Signal Processing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Jing Li
Sichuan Engineering Technical College
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 24 Feb, 2021
No community comments so far
Editorial decision: Major revision
On 03 Apr, 2021
Review #2 received
Received 01 Apr, 2021
Review #3 received
Received 26 Mar, 2021
Review #1 received
Received 26 Mar, 2021
Reviewer #2 agreed
On 01 Mar, 2021
Reviewer #3 agreed
On 01 Mar, 2021
Reviews received
Received 01 Mar, 2021
Reviewers invited
Invitations sent on 01 Mar, 2021
Reviewer #1 agreed
On 01 Mar, 2021
Editor assigned
On 21 Feb, 2021
Submission checks complete
On 21 Feb, 2021
Editor invited
On 21 Feb, 2021
First submitted
On 07 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In the development of modern agriculture, the intelligent use of mechanical equipment is one of the main signs for agricultural modernization. Navigation technology is the key technology for agricultural machinery to control autonomously in operating environment, and it is a hotspot in the field of intelligent research on agricultural machinery. Facing the accuracy requirements of autonomous navigation for intelligent agricultural robots, this paper proposes a visual navigation algorithm for agricultural robots based on deep learning image understanding. The method first uses cascaded deep convolutional network and hybrid dilated convolution fusion method to process images collected by vision system. Then it extracts the route of processed images based on improved Hough transform algorithm. At the same time, the posture of agricultural robots is adjusted to realize autonomous navigation. Finally, our proposed method is verified by using non-interference experimental scenes and noisy experimental scenes. Experimental results show that the method can perform autonomous navigation in complex and noisy environments, and has good practicability and applicability.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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Figure 13
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