Autonomous detection of crop rows based on adaptive multi-ROI in maize fields

Yang Zhou, Yang Yang, Boli Zhang, Xing Wen, Xuan Yue, Liqing Chen

Abstract


Crop rows detection in maize fields remains a challenging problem due to variation in illumination and weeds interference under field conditions. This study proposed an algorithm for detecting crop rows based on adaptive multi-region of interest (multi-ROI). First, the image was segmented into crop and soil and divided into several horizontally labeled strips. Feature points were located in the first image strip and initial ROI was determined. Then, the ROI window was shifted upward. For the next image strip, the operations for the previous strip were repeated until multiple ROIs were obtained. Finally, the least square method was carried out to extract navigation lines and detection lines in multi-ROI. The detection accuracy of the method was 95.3%. The average computation time was 240.8 ms. The results suggest that the proposed method has generally favorable performance and can meet the real-time and accuracy requirements for field navigation.
Keywords: machine vision, crop rows detection, navigation, multi-ROI
DOI: 10.25165/j.ijabe.20211404.6315

Citation: Zhou Y, Yang Y, Zhang B L, Wen X, Yue X, Chen L Q. Autonomous detection of crop rows based on adaptive multi-ROI in maize fields. Int J Agric & Biol Eng, 2021; 14(4): 217–225.

Keywords


machine vision, crop rows detection, navigation, multi-ROI

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References


Cervantes-Godoy D, Dewbre J. Economic importance of agriculture for poverty reduction. OECD Food, Agriculture and Fisheries Working Papers No. 23, IECD Publishing, 2010. doi: 10.1787/5kmmv9s20944-en.

Bonadies S, Gadsden S A. An overview of autonomous crop row navigation strategies for unmanned ground vehicles. Engineering in Agriculture, Environment and Food, 2019; 12(1): 24–31.

Yin X, Wang Y X, Chen Y L, Jin C Q, Du J. Development of autonomous navigation controller for agricultural vehicles. Int J Agric & Biol Eng, 2020; 13(4): 70–76.

Li Z, Chen L, Zheng Q, Dou X, Yang L. Control of a path following caterpillar robot based on a sliding mode variable structure algorithm. Biosyst Eng, 2019; 186: 293–306.

Liu Y, Noguchi N. Development of an unmanned surface vehicle for autonomous navigation in a paddy field. Engineering in Agriculture, Environment and Food, 2016; 9(1): 21–26.

Bakker T, Asselt K, Bontsema J, Müller J, Straten G. Systematic design of an autonomous platform for robotic weeding. J Terramechanics, 2010; 47(2): 63–73.

Bengochea-Guevara J M, Conesa-Munoz J, Andujar D, Ribeiro A. Merge fuzzy visual servoing and GPS-based planning to obtain a proper navigation behavior for a small crop-inspection robot. Sensors (Basel), 2016; 16(3): 276. doi: 10.3390/s16030276.

Feng J, Li Z, Yang W, Han X, Zhang X. Detection navigation baseline in row-following operation of maize weeder based on axis extraction. Int J Agric & Biol Eng, 2020; 13(5): 181–186.

Rabab S, Badenhorst P, Chen Y-P, Daetwyler H D. A template-free machine vision-based crop row detection algorithm. Precis Agric, 2020; 22(1): 124–153.

Keicher R, Seufert H. Automatic guidance for agricultural vehicles in Europe. Comput & Electron Agr, 2000; 25(1-2):169–194.

Hough P V C. A method and means for recognizing complex patterns. 1962; US Patent 3069654.

Rovira-Más F, Zhang Q, Reid J F, Will J D. Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle. Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, 2005; 219(8): 999–1010.

Bakker T, Wouters H, van Asselt K, Bontsema J, Tang L, Müller J, et al. A vision based row detection system for sugar beet. Comput & Electron Agr, 2008; 60(1): 87–95.

Ji R, Qi L. Crop-row detection algorithm based on Random Hough Transformation. Math & Comput Model, 2011; 54(3-4): 1016–1020.

Xu L, Oja E. Randomized Hough Transform (RHT): Basic mechanisms, algorithms, and computational complexities. CVGIP: Image Understanding, 1993; 57(2): 131–154.

Okamoto H, Hata S, Kataoka T, Terawaki M. Automatic weeding cultivator using crop-row detector. IFAC Proceedings Volumes, 2001; 34(19): 117–122.

Sogaard H T, Olsen H J. Determination of crop rows by image analysis without segmentation. Comput & Electron Agr, 2003; 38(2): 141–158.

Si Y, Jiang G, Liu G, Gao R, Liu Z. Early stage crop rows detection based on least square method. Transactions of the Chinese Society of Agricultural Machinery, 2010; 41(7): 163–167, 185. (in Chinese)

Ospina R, Noguchi N. Simultaneous mapping and crop row detection by fusing data from wide angle and telephoto images. Comput & Electron Agr, 2019; 162: 602–612.

Pla F, Sanchiz J M, Marchant J A, Brivot R. Building perspective models to guide a row crop navigation vehicle. Image & Vision Comput, 1997; 15(6): 465–470.

Jiang G, Wang X, Wang Z, Liu H. Wheat rows detection at the early growth stage based on Hough transform and vanishing point. Comput & Electron Agr, 2016; 123(45): 211–223.

Zhang X, Li X, Zhang B, Zhou J, Tian G, Xiong Y, et al. Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method. Comput & Electron Agr, 2018; 154: 165–175.

Jiang G, Wang Z, Liu H. Automatic detection of crop rows based on multi-ROIs. Expert Syst Appl, 2015; 42(5): 2429–2441.

García-Santillán I D, Montalvo M, Guerrero J M, Pajares G. Automatic detection of curved and straight crop rows from images in maize fields. Biosyst Eng, 2017; 156: 61–79.

Li J, Zhu R, Chen B. Image detection and verification of visual navigation route during cotton field management period. Int J Agric & Biol Eng, 2018; 11(6): 159–165.

Montalvo M, Pajares G, Guerrero J M, Romeo J, Guijarro M, Ribeiro A, et al. Automatic detection of crop rows in maize fields with high weeds pressure. Expert Syst Appl, 2012; 39(15): 11889–11897.

Evert F K V, Van Der Heijden G W A M, Lotz L A P, Polder G, Lamaker A, De Jong A, et al. A mobile field robot with vision-based detection of volunteer potato plants in a corn crop. Weed Technol, 2017; 20(4): 853–861.

Burgos-Artizzu X P, Ribeiro A, Guijarro M, Pajares G. Real-time image processing for crop/weed discrimination in maize fields. Comput & Electron Agr, 2011; 75(2): 337–346.

Sainz-Costa N, Ribeiro A, Burgos-Artizzu X P, Guijarro M, Pajares G. Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed. Sensors (Basel), 2011; 11(7): 7095–109.

Woebbecke D M, Meyer G E, Von Bargen K, Mortensen D A. Shape features for identifying young weeds using image analysis. Transactions of the ASABE, 1995; 38(1): 271–281.

Otsu N. A threshold selection method from gray-level histograms. IEEE T Syst Man & Cy, 2007; 9(1): 62–66.

Wilcoxon F. Individual comparisons by ranking methods. Biometrics, 1945; 1(6): 80–83.




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