Method for the navigation line recognition of the ridge without crops via machine vision
Keywords:
navigation line recognition, machine vision, ridge line recognition, intelligent agricultureAbstract
Some agriculture machinery like the transplanter, needs to operate by following the crop-free ridges. In order to improve working efficiency and quality, some autonomous navigation systems were developed and applied to ridge-following machinery. At present, agricultural navigation systems are mainly the satellite navigation system and the machine vision system. The satellite navigation system is difficult to apply to the machinery that needs to work by following the ridge because it cannot distinguish the shape of the navigated ridge and guide the machinery working along the ridge. In this study, 697 cloudy ridge images and 235 sunny ridge images were taken in the field, and these images were used as the dataset. Moreover, a machine vision navigation method based on the color of ridges was proposed. Firstly, the regions of interest (ROI) in the ridge image were extracted according to the reaction time and the forward speed of the machine. Then, a gray reconstruction method was used to enlarge the color difference between the ridge and the furrow. The optimal threshold for the gray image segmenting was calculated real-timely by using the threshold segmentation method. Then, based on the contour detection method, the ridge contour which was not surrounded by holes was extracted. Finally, the approximate quadrilateral method was proposed to recognize the ridge center line as the navigation line. The method proposed in this study was verified by four types of ridges with different colors and textures. The experimental results showed that the recognition success rates of the light ridge, the dark ridge, the film-covered ridge, and the sunny ridge were 100%, 97.5%, 100%, and 98.7%, respectively. The recognition success rate of the proposed method was at least 8% higher than that of the existing ridge-furrow recognition methods. The results indicate that this method can effectively realize navigation line recognition. This method can provide technical support for the autonomous navigation of agricultural machinery, such as transplanters, seeders, etc., operating on the ridge without crops. Key words: navigation line recognition, machine vision, ridge line recognition, intelligent agriculture DOI: 10.25165/j.ijabe.20241702.7480 Citation: Liu W, Hu J P, Liu J X, Yue R C, Zhang T F, Yao M J, et al. Method for the navigation line recognition of the ridge without crops via machine vision. Int J Agric & Biol Eng, 2024; 17(2): 230–239.References
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[3] Galati A, Sabatino L, Prinzivalli C S, D’Anna F, Scalenghe R. Strawberry fields forever: That is, how many grams of plastics are used to grow a strawberry? Journal of Environmental Management, 2020; 276: 111313 .
[4] Han L H, Xiang D Q, Xu Q Q, Du X W, Ma G X, Mao H P. Development of simplified seedling transplanting device for supporting efficient production of vegetable raw materials. Applied Sciences-Basel, 2023; 13(18): 10022.
[5] Tang Z, Wang H, Liu S, Lu D, Tang Y. Development of structure and control system of self-propelled small green vegetables combine harvester. Agricultural Science and Technology, 2023; 25(5): 1045–1058.
[6] Ahmad F, Adeel M, Qiu B J, Ma J, Shoaib M, Shakoor A, et al. Sowing uniformity of bed-type pneumatic maize planter at various seedbed preparation levels and machine travel speeds. Int J Agric & Biol Eng, 2021; 14(1): 165.
[7] Fang H M, Niu M M, Zhu Z B, Zhang Q Y. Experimental and numerical investigations of the impacts of separating board and anti-blocking mechanism on maize seeding. Journal of Agricultural Engineering, 2022; 53(1): 1273.
[8] Huang W Y, Ji X, Wang A Z, Wang Y F, Wei X H. Straight-line path tracking control of agricultural tractor-trailer based on fuzzy sliding mode control. Applied Sciences-Basel, 2023; 13(2): 872.
[9] Ding C, Ding S H, Wei X H, Mei K Q. Output feedback sliding mode control for path-tracking of autonomous agricultural vehicles. Nonlinear Dynamics, 2022; 110(3): 2429-2445.
[10] Opiyo S, Okinda C, Zhou J, Mwangi E, Makange N. Medial axis-based machine-vision system for orchard robot navigation. Computers and Electronics in Agriculture, 2021; 185: 106153.
[11] Bonadies S, Andrew Gadsden S. An overview of autonomous crop row navigation strategies for unmanned ground vehicles. Engineering in Agriculture, Environment and Food, 2019; 12(1): 24–31.
[12] Ospina R, Noguchi N. Simultaneous mapping and crop row detection by fusing data from wide angle and telephoto images. Computers and Electronics in Agriculture, 2019; 162: 602–612.
[13] Chen J Q, Qiang H, Wu J H, Xu G W, Wang Z K. Navigation path extraction for greenhouse cucumber-picking robots using the prediction-point Hough transform. Computers and Electronics in Agriculture, 2021; 180: 105911.
[14] Rabab S, Badenhorst P, Chen Y-P P, Daetwyler H D. A template-free machine vision-based crop row detection algorithm. Precision Agriculture, 2021; 22: 124–153.
[15] Hamuda E, Mc Ginley B, Glavin M, Jones E. Automatic crop detection under field conditions using the HSV colour space and morphological operations. Computers and Electronics in Agriculture, 2016; 133: 97–107.
[16] Morio Y, Teramoto K, Murakami K. Vision-based furrow line detection for navigating intelligent worker assistance robot. Engineering in Agriculture, Environment and Food, 2017; 10(2): 87–103.
[17] Takagaki A, Masuda R, Iida M, Suguri M. Image processing for ridge/furrow discrimination for autonomous agricultural vehicles navigation. IFAC Proceedings Volumes, 2013; 46(18): 47–51.
[18] Gu X B, Cai H J, Zhang Z T, Fang H, Chen P P, Huang P, et al. Ridge-furrow full film mulching: An adaptive management strategy to reduce irrigation of dryland winter rapeseed (Brassica napus L. ) in northwest China. Agricultural and Forest Meteorology, 2019; 266-267: 119–128.
[19] Otsu N. A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics, 1979; 9(1): 62–66.
[20] Sauvola J, Pietikäinen M. Adaptive document image binarization. Pattern Recognition, 2000; 33(2): 225–236.
[21] Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1986; 8(6): 679–698. doi: 10.1109/TPAMI.1986.4767851.
[22] Suzuki S, Abe K. Topological structural analysis of digitized binary images by border following. Computer Vision, Graph, and Image Processing, 1985; 29(3): 396.
[23] Duda R O, Hart P E. Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 1972; 15(1): 11–15.
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Published
2024-05-21
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Liu, W., Hu, J., Liu, J., Yue, R., Zhang, T., Yao, M., & Li, J. (2024). Method for the navigation line recognition of the ridge without crops via machine vision. International Journal of Agricultural and Biological Engineering, 17(2), 230–239. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7480
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Information Technology, Sensors and Control Systems
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