| Research on navigation line generation of kiwi orchard between rows based on root point substitution |
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| DOI：10.7606/j.issn.1000-7601.2021.05.29 |
| Key Words: kiwi orchard navigation line generation trunk detection root point substitution |
| Author Name | Affiliation | | MA Chi | College of Mechanical and Electronic Engineering,Northwest A&F University, Yangling, Shaanxi 712100, China | | DONG Ziyang | College of Mechanical and Electronic Engineering,Northwest A&F University, Yangling, Shaanxi 712100, China | | CHEN Zhijun | College of Mechanical and Electronic Engineering,Northwest A&F University, Yangling, Shaanxi 712100, China | | ZHU Yue | College of Mechanical and Electronic Engineering,Northwest A&F University, Yangling, Shaanxi 712100, China | | SHI Fuxi | College of Mechanical and Electronic Engineering,Northwest A&F University, Yangling, Shaanxi 712100, China |
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| Abstract: |
| In orchard visual navigation, the selection and extraction of navigation signs are the key issues in the generation of navigation lines, which determine the robust and accuracy of the generation of navigation lines in the orchard. Based on the physiological characteristics of the separation of the trunk as well as the separation of the trunk and the crown of the trellis kiwi fruit orchard,and using the deep learning method of the bottom mid\|point of the target detection frame, this study proposed a navigation mark generation method to replace the root point of the fruit tree. LabelImg was used to annotate the trunk in the preprocessed image and generate a kiwi trunk data set. Object detection model based on Faster R-CNN was built to realize the trunk recognition of the effective range of kiwi fruit orchard between rows. The root point substitution method was employed to determine positioning base point of the trunk root point. The base point coordinates were extracted from the tree line based on the cubic spline interpolation method, and the navigation line was generated according to the least squares method. The test results showed that in the inter\|row environment of mulch plastic film, weeds and soil, the correct recognition rates of trunks were 90.6%, 90.1%, and 89.4%. The trunk pixel height in the image was greater than 300 pixels,trunk pixel height was between 200~300 pixels, and trunk pixel heights between 100~200 pixels, the correct recognition rates of small targets trunk were 94.6%, 89.9%,and 85.6%. Selecting trunks as the orchard visual navigation target showed effective use of the stability of the depth of learning and improved accuracy of navigation target recognition. Root point substitution simplified the navigation positioning base point generation algorithm. The average horizontal pixel error between the fruit tree positioning base point and the actual trunk root point accounted for 1.3%, average vertical pixel error accounted for 2.2%, and the actual average distance error was 0.102 m. The positioning base point of navigation had high accuracy. The average horizontal pixel deviation of the generated inter\|row navigation line was 5.1 pixels, and the actual average horizontal deviation was 0.052 m. Navigation path generation of kiwi orchard between rows met the requirements. |
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