Development of a servo-controlled nozzle on the mobile robot for target oriented micro-dose spraying in precision weed control
DOI:
https://doi.org/10.5433/1679-0359.2021v42n2p635Keywords:
Dynamic weeding, LabVIEW, Machine vision, Precision weed control, Spraying needleAbstract
Broadcast spraying method is generally used for weed control in agriculture. Therefore, excessive amounts of pesticides are sprayed. In this study, a mobile robot was developed and tested on artificial weed targets for a micro-dose spraying system that works only with weed targets in order to reduce the use of spraying liquid in weed control. A prototype mobile robot consisted of a robotic platform, machine vision and steerable spraying unit was built and controlled by using LabVIEW, and tested to evaluate the feasibility of the spraying system. Greenness method and segmentation algorithm were utilized to extract artificial weed from the background. The artificial weed samples were treated according to their coordinates by the servo-based micro-dose spraying needle nozzle. The experiments were conducted at the speeds of 0.42, 0.54, 0.66, 0.78 and 0.90 km h-1 to evaluate the performance of the spraying system under laboratory conditions. The tracking and targeting performances of the mobile spraying system were observed visually. Consumption, the amount of deposition and coverage rate experiments were conducted by using graduated cups, filter papers and water-sensitive papers to evaluate the spraying efficiency of the system at 200 kPa spraying pressure. As a result, the targeted micro-dose spraying application saved about 95% application volume comparing with the broadcast spraying method. Higher spraying efficiency was determined at the middle locations than the edge locations according to the amount of deposition and coverage rate results. The developed servo-based target oriented weed control system was tested experimentally and found to be very efficient.Downloads
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References
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Tellaeche, A., Burgos-Artizzu, X. P., Pajares, G., & Ribeiro, A. (2008). A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognition, 41(2), 521-530. doi: 10.1016/ j.patcog.2007.07.007
Tian, L. (2002). Development of a sensor-based precision herbicide application system. Computers and Electronics in Agriculture, 36(2-3), 133-149. doi: 10.1016/s0168-1699(02)00097-2
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Wan Ishak, W. I., & Rahman, K. A. (2010). Software development for real-time weed colour analysis. Pertanika Journal of Science & Technology, 18(2), 243-253.
Xiong, Y., Ge, Y., Liang, Y., & Blackmore, S. (2017). Development of a prototype robot and fast path-planning algorithm for static laser weeding. Computers and Electronics in Agriculture, 142(2017), 494-503. doi: 10.1016/j.compag.2017.11.023
Yang, C.-C., Prasher, S. O., Landry, J.-A., & Kok, R. (2002). A vegetation localization algorithm for precision farming. Biosystems Engineering, 81(2), 137-146. doi: 10.1006/bioe.2002.0006
Yang, C.-C., Prasher, S. O., Landry, J.-A., & Ramaswamy, H. S. (2003). Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precision Agriculture, 4(1), 5-18. doi: 10.1023/a:1021847103560
Young, S. L., & Giles, D. K. (2013). Targeted and microdose chemical applications. In S. L. Young, & F. J. Pierce (Eds.), Automation: the future of weed control in cropping systems (pp. 139-147). Netherlands: Springer.
Zhang, Y., Staab, E. S., Slaughter, D. C., Giles, D. K., & Downey, D. (2012). Automated weed control in organic row crops using hyperspectral species identification and thermal micro-dosing. Crop Protection, 41(2012), 96-105. doi: 10.1016/j.cropro.2012.05.007
Zhao, D., Zhao, Y., Wang, X., & Zhang, B. (2016). Theoretical design and first test in laboratory of a composite visual servo-based target spray robotic system. Journal of Robotics, 2016(3), 1-11. doi: 10. 1155/2016/1801434
Blasco, J., Aleixos, N., Roger, J. M., Rabatel, G., & Moltó, E. (2002). Robotic weed control using machine vision. Biosystems Engineering, 83(2), 149-157. doi: 10.1006/bioe.2002.0109
Gonzalez, R. C., Woods, R. E., & Masters, B. R. (2008). Digital image processing (vol. 14). Upper Saddle River, NJ: Prentice Hall.
Gonzalez-de-Soto, M., Emmi, L., Perez-Ruiz, M., Aguera, J., & Gonzalez-de-Santos, P. (2016). Autonomous systems for precise spraying - Evaluation of a robotised patch sprayer. Biosystems Engineering, 146(2016), 165-182. doi: 10.1016/j.biosystemseng.2015.12.018
Jafari, A., Jahromi, H. E., Mohtasebi, S., & Omid, M. (2006a). Color segmentation scheme for classifying weeds from sugar beet using machine vision. International Journal of Information Science and Management (IJISM), 4(1), 1-12.
Jafari, A., Mohtasebi, S., Jahromi, H., & Omid, M. (2006b). Weed detection in sugar beet fields using machine vision. International Journal of Agriculture and Biology, 8(5), 602-605.
Lamm, R. D., Slaughter, D. C., & Giles, D. K. (2002). Precision weed control system for cotton. Transactions of the ASAE, 45(1), 231-238. doi: 10.13031/2013.7861
Lee, W. S., Slaughter, D. C., & Giles, D. K. (1999). Robotic weed control system for tomatoes. Precision Agriculture, 1(1), 95-113. doi: 10.1023/a:1009977903204
Loghavi, M., & Mackvandi, B. B. (2008). Development of a target oriented weed control system. Computers and Electronics in Agriculture, 63(2), 112-118. doi: 10.1016/j.compag.2008.01.020
Loni, R., Loghavi, M., & Jafari, A. (2014). Design, development and evaluation of targeted discrete-flame weeding for inter-row weed control using machine vision. American Journal of Agricultural Science and Technology, 2(1), 17-30. doi: 10.7726/ajast.2014.1003
Meyer, G. E., &, Camargo, J. Neto. (2008). Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 63(2), 282-293. doi: 10.1016/j. compag.2008.03.009
Midtiby, H. S., Mathiassen, S. K., Andersson, K. J., & Jørgensen, R. N. (2011). Performance evaluation of a crop/weed discriminating microsprayer. Computers and Electronics in Agriculture, 77(1), 35-40. doi: 10.1016/j.compag.2011.03.006
Nieuwenhuizen, A. T., Hofstee, J. W., & van Henten, E. J. (2010). Performance evaluation of an automated detection and control system for volunteer potatoes in sugar beet fields. Biosystems Engineering, 107(1), 46-53. doi: 10.1016/j.biosystemseng.2010.06.011
Özlüoymak, Ö. B., & Bolat, A. (2020). Development and assessment of a novel imaging software for optimizing the spray parameters on water-sensitive papers. Computers and Electronics in Agriculture, 168(2020), 105104. doi: 10.1016/j.compag.2019.105104
Sabanci, K., & Aydin, C. (2017). Smart robotic weed control system for sugar beet. Journal of Agricultural Science and Technology, 19(1), 73-83.
Shirzadifar, A., Loghavi, M., & Raoufat, M. (2013). Development and evaluation of a real time site-specific inter-row weed management system. Iran Agricultural Research, 32(2), 39-54. doi: 10.22099/IAR. 2015.2004
Song, Y., Sun, H., Li, M., & Zhang, Q. (2015). Technology application of smart spray in agriculture: a review. Intelligent Automation & Soft Computing, 21(3), 319-333. doi: 10.1080/10798587.2015.10157 81
Tangwongkit, R., Salokhe, V., & Jayasuriya, H. W. (2006). Development of a real-time, variable rate herbicide applicator using machine vision for between-row weeding of sugarcane fields. Agricultural Engineering International: CIGR Journal, Manuscript PM 06 009, 8, 1-12.
Tellaeche, A., Burgos-Artizzu, X. P., Pajares, G., & Ribeiro, A. (2008). A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognition, 41(2), 521-530. doi: 10.1016/ j.patcog.2007.07.007
Tian, L. (2002). Development of a sensor-based precision herbicide application system. Computers and Electronics in Agriculture, 36(2-3), 133-149. doi: 10.1016/s0168-1699(02)00097-2
Timmermann, C., Gerhards, R., & Kühbauch, W. (2003). The economic impact of site-specific weed control. Precision Agriculture, 4(3), 249-260. doi: 10.1023/a:1024988022674
Underwood, J. P., Calleija, M., Taylor, Z., Hung, C., Nieto, J., Fitch, R., & Sukkarieh, S. (2015). Real-time target detection and steerable spray for vegetable crops. Sydney: Sydney Univ. Retrieved from http://confluence.acfr.usyd.edu.au/download/attachments/14452007/2015-Underwood-ICRAAgWs-Spray.pdf?version=1&modificationDate=1465979705000&api=v2
Wan Ishak, W. I., & Rahman, K. A. (2010). Software development for real-time weed colour analysis. Pertanika Journal of Science & Technology, 18(2), 243-253.
Xiong, Y., Ge, Y., Liang, Y., & Blackmore, S. (2017). Development of a prototype robot and fast path-planning algorithm for static laser weeding. Computers and Electronics in Agriculture, 142(2017), 494-503. doi: 10.1016/j.compag.2017.11.023
Yang, C.-C., Prasher, S. O., Landry, J.-A., & Kok, R. (2002). A vegetation localization algorithm for precision farming. Biosystems Engineering, 81(2), 137-146. doi: 10.1006/bioe.2002.0006
Yang, C.-C., Prasher, S. O., Landry, J.-A., & Ramaswamy, H. S. (2003). Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precision Agriculture, 4(1), 5-18. doi: 10.1023/a:1021847103560
Young, S. L., & Giles, D. K. (2013). Targeted and microdose chemical applications. In S. L. Young, & F. J. Pierce (Eds.), Automation: the future of weed control in cropping systems (pp. 139-147). Netherlands: Springer.
Zhang, Y., Staab, E. S., Slaughter, D. C., Giles, D. K., & Downey, D. (2012). Automated weed control in organic row crops using hyperspectral species identification and thermal micro-dosing. Crop Protection, 41(2012), 96-105. doi: 10.1016/j.cropro.2012.05.007
Zhao, D., Zhao, Y., Wang, X., & Zhang, B. (2016). Theoretical design and first test in laboratory of a composite visual servo-based target spray robotic system. Journal of Robotics, 2016(3), 1-11. doi: 10. 1155/2016/1801434
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Published
2021-02-24
How to Cite
Özlüoymak, Ömer B. (2021). Development of a servo-controlled nozzle on the mobile robot for target oriented micro-dose spraying in precision weed control. Semina: Ciências Agrárias, 42(2), 635–656. https://doi.org/10.5433/1679-0359.2021v42n2p635
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