Journal
SENSORS
Volume 20, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/s20092610
Keywords
field boundary line detection; vision in agriculture; support vector machine line detection; superpixel segmentation algorithm
Funding
- National Key RD Plan of China [2016YFD0700505]
- Shanghai Agriculture Applied Technology and Development Program
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Visual based route and boundary detection is a key technology in agricultural automatic navigation systems. The variable illumination and lack of training samples has a bad effect on visual route detection in unstructured farmland environments. In order to improve the robustness of the boundary detection under different illumination conditions, an image segmentation algorithm based on support vector machine was proposed. A superpixel segmentation algorithm was adopted to solve the lack of training samples for a support vector machine. A sufficient number of superpixel samples were selected for extraction of color and texture features, thus a 19-dimensional feature vector was formed. Then, the support vector machine model was trained and used to identify the paddy ridge field in the new picture. The recognition F1 score can reach 90.7%. Finally, Hough transform detection was used to extract the boundary of the ridge field. The total running time of the proposed algorithm is within 0.8 s and can meet the real-time requirements of agricultural machinery.
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