Crop Row Detection Based on Wavelet Transformation and Otsu Segmentation Algorithm

HAN Yonghua; WANG Yaming; SUN Qi; ZHAO Yun

doi:10.11999/JEIT150421

Volume 38 Issue 1

Jan. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(1): 63-70

HAN Yonghua, WANG Yaming, SUN Qi, ZHAO Yun. Crop Row Detection Based on Wavelet Transformation and Otsu Segmentation Algorithm[J]. Journal of Electronics & Information Technology, 2016, 38(1): 63-70. doi: 10.11999/JEIT150421

Citation:

HAN Yonghua, WANG Yaming, SUN Qi, ZHAO Yun. Crop Row Detection Based on Wavelet Transformation and Otsu Segmentation Algorithm[J]. Journal of Electronics & Information Technology, 2016, 38(1): 63-70. doi: 10.11999/JEIT150421

Citation:

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Crop Row Detection Based on Wavelet Transformation and Otsu Segmentation Algorithm

doi: 10.11999/JEIT150421

1.
(Department of Electronics and Informatics, Zhejiang Sci-Tech University, Hangzhou 310018, China)
2.
(Department of Electronics and Informatics, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Funds:

The National Natural Science Foundation of China (61272311), Zhejiang Provincial Natural Science Foundation (LZ15F020004), The Young Researchers Foundation of Zhejiang Provincial Top Key Academic Discipline of Mechanical Engineering and Zhejiang Sci-Tech University Key Laboratory (ZSTUME 01B17), Graduate Student Innovation Research Project of Computer Application Innovation Key Subject (XDY14003), Science Foundation of Zhejiang Sci-Tech University (ZSTU) (13032156-Y), 521 Project of Zhejiang Sci-Tech University

Received Date: 2015-04-10
Rev Recd Date: 2015-09-13
Publish Date: 2016-01-19

Abstract

Abstract

Vision-based agricultural vehicle navigation has become a popular research area of automated guidance, however, crop row detection in high weeds field is still a challenging topic. An image segmentation method mainly based on frequency and color information is proposed to remove weeds. The algorithm is based on total frequency parameters, more total crop frequency, alternation regular of crop rows, Otsu method and color model transformation. The total frequency parameters are obtained from wavelet multi-resolution decomposition. The least square method is used in fitting straight line to detect the crop rows. Experiments show that the algorithm can effectively overcome the high weeds. The average processing time of a single pixels image is 132 ms.
- Agriculture navigation,
- Crop rows,
- Wavelet transformation,
- Otsu

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References(27)

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