Abstract
Three sets of drip emitter samples that were used in agricultural farms for 3–5 years were examined using a Computed Tomography (CT) scanner. The 2D slices and 3D images obtained were processed using Dragonfly 2020.1 software. Clogging material that was deposited gradually over the years on the emitter geometry was segmented using three different methods: (1) intensity thresholding, (2) machine learning (ML), and (3) deep learning (DL). The DL method not only delivered a more precise estimation of the quantity of clogging material, but also eased the segmentation process. Various measurements of emitter geometry, covering flow path and outlet areas, were taken and compared for three sample emitters. Clogging material got deposited predominantly on the outlet areas for all three samples, irrespective of their different usage times and emitter geometries. Efforts to optimize the design of emitters against clogging need to take this finding into consideration. Sample Emitter 2 with distinctly narrower flow path and smoother curved flow boundaries was found to have the least deposition of clogging material on its surface. Further study with a larger data set is required to establish a definite relationship between the geometric features and clogging intensity of drip emitters.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
The authors acknowledge Nondestructive Imaging Laboratory, Central Research Facility, IIT Kharagpur, India for CT scanning of images and ORS, Canada for providing with a noncommercial license of Dragonfly 2020.1.
References
Badran, A., D. Marshall, Z. Legault, R. Makovetsky, B. Provencher, N. Piché, and M. Marsh. 2020. “Automated segmentation of computed tomography images of fiber-reinforced composites by deep learning.” J. Mater. Sci. 55 (34): 16273–16289. https://doi.org/10.1007/s10853-020-05148-7.
Bezerra, E. T. V., K. S. Augusto, and S. Paciornik. 2020. “Discrimination of pores and cracks in iron ore pellets using deep learning neural networks.” REM—Int. Eng. J. 73 (2): 197–203. https://doi.org/10.1590/0370-44672019730119.
Dazhuang, Y., Y. Peiling, R. Shumei, Y. Li, and X. Tingwu. 2007. “Numerical study on flow property in dentate path of drip emitters.” N. Z. J. Agric. Res. 50 (5): 705–712. https://doi.org/10.1080/00288230709510341.
Feng, J., Y. Li, W. Wang, and S. Xue. 2018. “Effect of optimization forms of flow path on emitter hydraulic and anti-clogging performance in drip irrigation system.” Irrig. Sci. 36 (1): 37–47. https://doi.org/10.1007/s00271-017-0561-9.
Li, G., J. Wang, M. Alam, and Y. Zhao. 2006. “Influence of geometrical parameters of labyrinth flow path of drip emitters on hydraulic and anti-clogging performance.” Trans. ASABE 49 (3): 637–643. https://doi.org/10.13031/2013.20483.
Li, Y., J. Feng, S. Xue, T. Muhammad, X. Chen, N. Wu, W. Li, and B. Zhou. 2019. “Formation mechanism for emitter composite-clogging in drip irrigation system.” Irrig. Sci. 37 (2): 169–181. https://doi.org/10.1007/s00271-018-0612-x.
Liu, Y., D. Li, P. Wu, L. Zhang, D. Zhu, and J. Chen. 2018. “Clogging characteristics of different emitters in drip irrigation with hard water.” [In Chinese.] Trans. Chinese Soc. Agric. Eng. 34 (3): 96–102. https://doi.org/10.11975/j.issn.1002-6819.2018.03.013.
Liu, Y., P. Wu, D. Zhu, L. Zhang, and J. Chen. 2015. “Effect of water hardness on emitter clogging of drip irrigation.” Trans. Chinese Soc. Agric. Eng. 31 (20): 95–99. https://doi.org/10.11975/j.issn.1002-6819.2015.20.014.
Lorenz, R., I. Curosu, S. Paciornik, V. Mechtcherine, M. Oppermann, and F. Silvad. 2020. “Semantic segmentation of the micro-structure of strain-hardening cement-based composites (SHCC) by applying deep learning on micro-computed tomography scans.” Cem. Concr. Compos. 108 (Apr): 103551. https://doi.org/10.1016/j.cemconcomp.2020.103551.
Ma, X., N. Kittikunakorn, B. Sorman, H. Xi, A. Chen, M. Marsh, A. Mongeau, N. Piché, R. O. Williams III, and D. Skomski. 2020. “Application of deep learning convolutional neural networks for internal tablet defect detection: High accuracy, throughput, and adaptability.” J. Pharm. Sci. 109 (4): 1547–1557. https://doi.org/10.1016/j.xphs.2020.01.014.
Ramachandrula, V. R., and R. R. Kasa. 2020. “Non-destructive characterization of physical and chemical clogging in cylindrical drip emitters.” Heliyon 6 (10): e05327. https://doi.org/10.1016/j.heliyon.2020.e05327.
Reethi, K., Mallikarjuna, and B. Vijaya Raghu. 2015. “Analysis of Flow through a Drip Irrigation Emitter.” Int. J. Mod. Eng. Res. 5 (11): 57–64.
Sinchuk, Y., P. Kibleur, J. Aelterman, M. N. Boone, and V. P. Wim. 2020. “Variational and Deep Learning Segmentation of Very-Low-Contrast X-ray Computed Tomography Images of Carbon/Epoxy Woven Composites.” Materials 13 (4): 936. https://doi.org/10.3390/ma13040936.
Xiao, Y., B. Sawicka, Y. Liu, B. Zhou, P. Hou, and Y. Li. 2020. “Visualizing the macro-scale spatial distributions of biofilms in complex flow channels using industrial computed tomography.” Biofouling 36 (2): 115–125. https://doi.org/10.1080/08927014.2020.1728260.
Yang, B., J. Wang, Y. Zhang, H. Wang, X. Ma, and Y. Mo. 2020. “Anti-clogging performance optimization for dentiform labyrinth emitters.” Irrig. Sci. 38 (3): 275–285. https://doi.org/10.1007/s00271-020-00671-6.
Zhangzhong, L., P. Yang, W. Zhen, X. Zhang, and C. Wang. 2019. “A kinetic model for the chemical clogging of drip irrigation system using saline water.” Agric. Water Manage. 223 (20): 105696. https://doi.org/10.1016/j.agwat.2019.105696.
Zhou, B., D. Wang, T. Wang, and Y. Li. 2018. “Chemical clogging behaviour in drip irrigation systems using reclaimed water.” Trans. ASABE 61 (5): 1667–1675. https://doi.org/10.13031/trans.12902.
Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 148 • Issue 4 • April 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Dec 8, 2020
Accepted: Nov 17, 2021
Published online: Feb 15, 2022
Published in print: Apr 1, 2022
Discussion open until: Jul 15, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Venkata Ramamohan Ramachandrula, Ramamohan Reddy Kasa, Arun Torris, Micro-Computed Tomography (µCT) Study of Clogging in Long-Used Strip and Cylindrical Drip Emitters, Journal of The Institution of Engineers (India): Series A, 10.1007/s40030-022-00697-3, 104, 1, (167-174), (2022).