Navigation Line Extraction Method for Broad-Leaved Plants in the Multi-Period Environments of the High-Ridge Cultivation Mode Article Swipe
Xiangming Zhou
,
Xiuli Zhang
,
Renzhong Zhao
,
Yong Chen
,
Xiaochan Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture13081496
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture13081496
Navigation line extraction is critical for precision agriculture and automatic navigation. A novel method for extracting navigation lines based on machine vision is proposed herein using a straight line detected based on a high-ridge crop row. Aiming at the low-level automation of machines in field environments of a high-ridge cultivation mode for broad-leaved plants, a navigation line extraction method suitable for multiple periods and with high timeliness is designed. The method comprises four sequentially linked phases: image segmentation, feature point extraction, navigation line calculation, and dynamic segmentation horizontal strip number feedback. The a* component of the CIE-Lab colour space is extracted to preliminarily extract the crop row features. The OTSU algorithm is combined with morphological processing to completely separate the crop rows and backgrounds. The crop row feature points are extracted using an improved isometric segmented vertical projection method. While calculating the navigation lines, an adaptive clustering method is used to cluster the adjacent feature points. A dynamic segmentation point clustering method is used to determine the final clustering feature point sets, and the feature point sets are optimised using lateral distance and point line distance methods. In the optimisation process, a linear regression method based on the Huber loss function is used to fit the optimised feature point set to obtain the crop row centreline, and the navigation line is calculated according to the two crop lines. Finally, before entering the next frame processing process, a feedback mechanism to calculate a number of horizontal strips for the next frame is introduced to improve the ability of the algorithm to adapt to multiple periods. The experimental results show that the proposed method can meet the efficiency requirements for visual navigation. The average time for the image processing of four samples is 38.53 ms. Compared with the least squares method, the proposed method can adapt to a longer growth period of crops.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture13081496
- https://www.mdpi.com/2077-0472/13/8/1496/pdf?version=1690451201
- OA Status
- gold
- Cited By
- 9
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385344556
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385344556Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/agriculture13081496Digital Object Identifier
- Title
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Navigation Line Extraction Method for Broad-Leaved Plants in the Multi-Period Environments of the High-Ridge Cultivation ModeWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-27Full publication date if available
- Authors
-
Xiangming Zhou, Xiuli Zhang, Renzhong Zhao, Yong Chen, Xiaochan LiuList of authors in order
- Landing page
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https://doi.org/10.3390/agriculture13081496Publisher landing page
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https://www.mdpi.com/2077-0472/13/8/1496/pdf?version=1690451201Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2077-0472/13/8/1496/pdf?version=1690451201Direct OA link when available
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Artificial intelligence, Cluster analysis, Computer science, Feature extraction, Feature (linguistics), Segmentation, Computer vision, Image segmentation, Pattern recognition (psychology), Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 4, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2077-0472/13/8/1496/pdf?version=1690451201 |
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| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
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| primary_location.is_published | True |
| primary_location.raw_source_name | Agriculture |
| primary_location.landing_page_url | https://doi.org/10.3390/agriculture13081496 |
| publication_date | 2023-07-27 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3156943756, https://openalex.org/W2915079506, https://openalex.org/W3158002639, https://openalex.org/W4362633067, https://openalex.org/W3049515965, https://openalex.org/W3189774831, https://openalex.org/W3017063165, https://openalex.org/W2041911296, https://openalex.org/W6757593505, https://openalex.org/W6800238566, https://openalex.org/W3199238879, https://openalex.org/W4210491995, https://openalex.org/W3192709932, https://openalex.org/W6800491870, https://openalex.org/W2892071132, https://openalex.org/W2563914425, https://openalex.org/W3135018180, https://openalex.org/W2960284779, https://openalex.org/W2973559712, https://openalex.org/W4317486124, https://openalex.org/W2921199990, https://openalex.org/W2866175125, https://openalex.org/W2793474046, https://openalex.org/W4205857934, https://openalex.org/W4313420583, https://openalex.org/W3196122058, https://openalex.org/W4249348375, https://openalex.org/W4232190498 |
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