Algorithm for Path Recognition in-between Tree Rows for Agricultural Wheeled-Mobile Robots
Machine vision has been widely used in recent years in agriculture, as a tool to promote the automation of processes and increase the levels of productivity. The aim of this work is the development of a path recognition algorithm based on image processing to guide a terrestrial robot in-between tree rows. The proposed algorithm was developed using the software MATLAB, and it uses several image processing operations, such as threshold detection, morphological erosion, histogram equalization and the Hough transform, to find edge lines along tree rows on an image and to create a path to be followed by a mobile robot. To develop the algorithm, a set of images of different types of orchards was used, which made possible the construction of a method capable of identifying paths between trees of different heights and aspects. The algorithm was evaluated using several images with different characteristics of quality and the results showed that the proposed method can successfully detect a path in different types of environments.
[1] Y. R. Chen, K. Chao, and M. S. Kim, “Machine vision technology for agricultural applications,” in Computers and Electronics in Agriculture, 2002, vol. 36, no. 2–3, pp. 173–191.
[2] P. Revathi and M. Hemalatha, “Classification of cotton leaf spot diseases using image processing edge detection techniques.” pp. 169–173, 2012.
[3] S. Kalaivanan and R. Kalpana, “Coverage path planning for an autonomous robot specific to agricultural operations,” in 2017 International Conference on Intelligent Computing and Control (I2C2), 2017, pp. 1–5.
[4] Shivaprasad B, Ravishankara M, and B N Shoba, “Design and Implementation of Seeding and Fertilizing Agriculture Robot,” Int. J. Appl. or Innov. Eng. Manag., vol. 3, no. 6, p. 5, 2014.
[5] Q. Lü, J. R. Cai, B. Liu, D. Lie, and Y. J. Zhang, “Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine,” Int. J. Agric. Biol. Eng., vol. 7, no. 2, pp. 115–121, 2014.
[6] L. Biqing, L. Yongfa, Z. Hongyan, and Z. Shiyong, “The design and realization of cherry tomato harvesting robot based on IOT,” Int. J. Online Eng., vol. 12, no. 12, pp. 22–26, 2016.
[7] B. Zhao, Y. Fan, W. Mao, P. Zhou, and X. Zhang, “Path recognition method of agricultural vehicle in diferente illumination,” in Transactions of the Chinese Society of Agricultural Engineering, 2012, pp. 193–196.
[8] G. Gao and M. Li, “Study on navigating path recognition for the greenhouse mobile robot based on K-means algorithm,” in Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014, 2014, pp. 451–456.
[9] C. Tu, B. J. Van Wyk, K. Djouani, Y. Hamam, and S. Du, “An efficient crop row detection method for agriculture robots,” in Proceedings - 2014 7th International Congress on Image and Signal Processing, CISP 2014, 2014, pp. 655–659.
[10] A. Takagaki, R. Masuda, M. Iida, and M. Suguri, “Image Processing for Ridge/Furrow Discrimination for Autonomous Agricultural Vehicles Navigation,” IFAC Proc. Vol., vol. 46, no. 18, pp. 47–51, 2013.
[11] A. Witten, “A MATLAB-based three-dimensional viewer,” Comput. Geosci., vol. 30, no. 7, pp. 693–703, 2004.
[12] G. R. Vidhya and H. Ramesh, “Effectiveness of Contrast Limited Adaptive Histogram Equalization Technique on Multispectral Satellite Imagery,” in Proceedings of the International Conference on Video and Image Processing - ICVIP 2017, 2017, pp. 234–239.
[13] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man. Cybern., vol. 9, no. 1, pp. 62–66, 1979.
[14] M. Sonka, V. Hlavac, and R. Boyle, “Image Processing, Analysis, and Machine Vision,” Thomson Learn., p. 812, 2008.
[15] W. Gao, L. Yang, X. Zhang, and H. Liu, “An improved Sobel edge detection,” in Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010, 2010, vol. 5, pp. 67–71.
[1] Y. R. Chen, K. Chao, and M. S. Kim, “Machine vision technology for agricultural applications,” in Computers and Electronics in Agriculture, 2002, vol. 36, no. 2–3, pp. 173–191.
[2] P. Revathi and M. Hemalatha, “Classification of cotton leaf spot diseases using image processing edge detection techniques.” pp. 169–173, 2012.
[3] S. Kalaivanan and R. Kalpana, “Coverage path planning for an autonomous robot specific to agricultural operations,” in 2017 International Conference on Intelligent Computing and Control (I2C2), 2017, pp. 1–5.
[4] Shivaprasad B, Ravishankara M, and B N Shoba, “Design and Implementation of Seeding and Fertilizing Agriculture Robot,” Int. J. Appl. or Innov. Eng. Manag., vol. 3, no. 6, p. 5, 2014.
[5] Q. Lü, J. R. Cai, B. Liu, D. Lie, and Y. J. Zhang, “Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine,” Int. J. Agric. Biol. Eng., vol. 7, no. 2, pp. 115–121, 2014.
[6] L. Biqing, L. Yongfa, Z. Hongyan, and Z. Shiyong, “The design and realization of cherry tomato harvesting robot based on IOT,” Int. J. Online Eng., vol. 12, no. 12, pp. 22–26, 2016.
[7] B. Zhao, Y. Fan, W. Mao, P. Zhou, and X. Zhang, “Path recognition method of agricultural vehicle in diferente illumination,” in Transactions of the Chinese Society of Agricultural Engineering, 2012, pp. 193–196.
[8] G. Gao and M. Li, “Study on navigating path recognition for the greenhouse mobile robot based on K-means algorithm,” in Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014, 2014, pp. 451–456.
[9] C. Tu, B. J. Van Wyk, K. Djouani, Y. Hamam, and S. Du, “An efficient crop row detection method for agriculture robots,” in Proceedings - 2014 7th International Congress on Image and Signal Processing, CISP 2014, 2014, pp. 655–659.
[10] A. Takagaki, R. Masuda, M. Iida, and M. Suguri, “Image Processing for Ridge/Furrow Discrimination for Autonomous Agricultural Vehicles Navigation,” IFAC Proc. Vol., vol. 46, no. 18, pp. 47–51, 2013.
[11] A. Witten, “A MATLAB-based three-dimensional viewer,” Comput. Geosci., vol. 30, no. 7, pp. 693–703, 2004.
[12] G. R. Vidhya and H. Ramesh, “Effectiveness of Contrast Limited Adaptive Histogram Equalization Technique on Multispectral Satellite Imagery,” in Proceedings of the International Conference on Video and Image Processing - ICVIP 2017, 2017, pp. 234–239.
[13] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man. Cybern., vol. 9, no. 1, pp. 62–66, 1979.
[14] M. Sonka, V. Hlavac, and R. Boyle, “Image Processing, Analysis, and Machine Vision,” Thomson Learn., p. 812, 2008.
[15] W. Gao, L. Yang, X. Zhang, and H. Liu, “An improved Sobel edge detection,” in Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010, 2010, vol. 5, pp. 67–71.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:78347", author = "Anderson Rocha and Pedro Miguel de Figueiredo Dinis Oliveira Gaspar", title = "Algorithm for Path Recognition in-between Tree Rows for Agricultural Wheeled-Mobile Robots", abstract = "Machine vision has been widely used in recent years in agriculture, as a tool to promote the automation of processes and increase the levels of productivity. The aim of this work is the development of a path recognition algorithm based on image processing to guide a terrestrial robot in-between tree rows. The proposed algorithm was developed using the software MATLAB, and it uses several image processing operations, such as threshold detection, morphological erosion, histogram equalization and the Hough transform, to find edge lines along tree rows on an image and to create a path to be followed by a mobile robot. To develop the algorithm, a set of images of different types of orchards was used, which made possible the construction of a method capable of identifying paths between trees of different heights and aspects. The algorithm was evaluated using several images with different characteristics of quality and the results showed that the proposed method can successfully detect a path in different types of environments.
", keywords = "Agricultural mobile robot, image processing, path recognition, Hough transform.", volume = "13", number = "1", pages = "34-4", }
