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A Software Development for Real Time Spray Control System in Herbicide Application

Year 2017, Volume: 1 Issue: 1, 13 - 19, 30.08.2017

Abstract

Advances in different technologies,
such as high-resolution vision systems, innovative sensors and embedded
computing systems, are finding direct application in agriculture. In precision
farming, image analysis techniques can aid farmers in herbicide applications,
and thus lower the risk of soil and water pollution by reducing the amount of
chemicals applied. Optical sensors and computer vision, which can be used in
automated weed detection and control spray systems, are being used in recent
years extensively. A real-time auto tracking and determination system for weed
detection and spray on/off were designed, built and set up in the laboratory at
the Department of Agricultural Machinery and Technologies Engineering of
Çukurova University. In this study; to get the target images, a web camera,
mounted at a height of 50 cm above the target object was used. During the start
of the weed tracking operation, the web camera captured images of the
artificial weeds. Developed software, which could be reprogrammed and adjusted
according to the user preference, was created by using LabVIEW. Weed coverage
was determined from each image by using a “greenness method” in which the red,
green, and blue intensities of each pixel were compared. The sprayer nozzle was
turned ‘on’ or ‘off’ by using a data acquisition card and a relay card,
depending on the green color pixels of weeds. The sprayer valve opened the
nozzle when the camera detected the presence of weeds. Image processing
performance of this system, in where nozzle and camera were mounted at a
stationary position while weeds were on a movable belt, was tested at the
different speeds of conveyor belt consisted of an inverter drive system and 3
phase 4 pole electric motor. The laboratory performance evolution revealed that
the system could detect the weeds successfully and could be used to decrease
the herbicide quantity.

References

  • Blasco J., Aleixos N., Roger J. M., Rabatel G. & Molto E. 2002. Robotic Weed Control Using Machine Vision, Biosystems Engineering, 83 (2), 149–157.
  • Gonzalez-de-Soto M., Emmi L., Perez-Ruiz M., Aguera J. & Gonzalez-de-Santos P. 2016. A utonomous Systems for Precise Spraying - Evaluation of A Robotised Patch Sprayer, Biosystems Engineering, 146, 165-182.
  • Jafari A., Mohtasebi S. S., Jahromi H. E. & Omid M. 2006a. Weed Detection in Sugar Beet Fields Using Machine Vision, International Journal of Agriculture & Biology, 8 (5), 602-605.
  • Jafari A., Mohtasebi S. S., Jahromi H. E. & Omid M. 2006b. Color Segmentation Scheme for Classifying Weeds from Sugar Beet Using Machine Vision, Iranian Journal of Information Science & Technology, 4 (1), 1-12.
  • 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.
  • Sabancı K. & Aydın C. 2014. Image Processing Based Precision Spraying Robot, Journal of Agricultural Sciences, 20, 406-414.
  • Sabanci K. & Aydin C. 2017. Smart Robotic Weed Control System for Sugar Beet, Journal of Agricultural Science and Technology, 19, 73-83.
  • Shirzadifar A. M., Loghavi M. & Raoufat M. H. 2013. Development and Evaluation of a Real Time Site-Specific Inter-Row Weed Management System, Iran Agricultural Research, 32 (2), 39-54.
  • Slaughter D. C., Giles D. K. & Downey D. 2008. Autonomous Robotic Weed Control Systems: A Review, Computers and Electronics in Agriculture, 61, 63–78.
  • Tangwongkit R., Salokhe V. M. & Jayasuriya H. P. W. 2006. Development of a Real-Time, Variable Rate Herbicide Applicator Using Machine Vision for Between-Row Weeding of Sugarcane Fields, Agricultural Engineering International: The CIGR Ejournal, Manuscript PM 06 009, vol. 8.
  • Tellaechea A., Burgos-Artizzub X. P., Pajaresa G. & Ribeirob A. 2008. A Vision-Based Method for Weeds Identification through the Bayesian Decision Theory, Pattern Recognition Society, 41, 521-530.
  • Tian L. 2002. Development of a Sensor-Based Precision Herbicide Application System, Computers and Electronics in Agriculture, 36, 133-149.
  • Timmermann C., Gerhads R. & Kühbauch W. 2003. The Economic Impact of Site-Specific Weed Control, Precision Agriculture, 4, 249-260.
  • Wan Ishak W. I. & Abdul Rahman K. 2010. Software Development for Real-Time Weed Colour Analysis, Pertanika Journal of Science & Technology, 18 (2), 243-253.
  • Yang C., Prasher S. O., Landry J. & Ramaswamy H. S. 2003. Development of an Image Processing System and a Fuzzy Algorithm for Site-Specific Herbicide Applications, Precision Agriculture, 4, 5–18.
Year 2017, Volume: 1 Issue: 1, 13 - 19, 30.08.2017

Abstract

References

  • Blasco J., Aleixos N., Roger J. M., Rabatel G. & Molto E. 2002. Robotic Weed Control Using Machine Vision, Biosystems Engineering, 83 (2), 149–157.
  • Gonzalez-de-Soto M., Emmi L., Perez-Ruiz M., Aguera J. & Gonzalez-de-Santos P. 2016. A utonomous Systems for Precise Spraying - Evaluation of A Robotised Patch Sprayer, Biosystems Engineering, 146, 165-182.
  • Jafari A., Mohtasebi S. S., Jahromi H. E. & Omid M. 2006a. Weed Detection in Sugar Beet Fields Using Machine Vision, International Journal of Agriculture & Biology, 8 (5), 602-605.
  • Jafari A., Mohtasebi S. S., Jahromi H. E. & Omid M. 2006b. Color Segmentation Scheme for Classifying Weeds from Sugar Beet Using Machine Vision, Iranian Journal of Information Science & Technology, 4 (1), 1-12.
  • 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.
  • Sabancı K. & Aydın C. 2014. Image Processing Based Precision Spraying Robot, Journal of Agricultural Sciences, 20, 406-414.
  • Sabanci K. & Aydin C. 2017. Smart Robotic Weed Control System for Sugar Beet, Journal of Agricultural Science and Technology, 19, 73-83.
  • Shirzadifar A. M., Loghavi M. & Raoufat M. H. 2013. Development and Evaluation of a Real Time Site-Specific Inter-Row Weed Management System, Iran Agricultural Research, 32 (2), 39-54.
  • Slaughter D. C., Giles D. K. & Downey D. 2008. Autonomous Robotic Weed Control Systems: A Review, Computers and Electronics in Agriculture, 61, 63–78.
  • Tangwongkit R., Salokhe V. M. & Jayasuriya H. P. W. 2006. Development of a Real-Time, Variable Rate Herbicide Applicator Using Machine Vision for Between-Row Weeding of Sugarcane Fields, Agricultural Engineering International: The CIGR Ejournal, Manuscript PM 06 009, vol. 8.
  • Tellaechea A., Burgos-Artizzub X. P., Pajaresa G. & Ribeirob A. 2008. A Vision-Based Method for Weeds Identification through the Bayesian Decision Theory, Pattern Recognition Society, 41, 521-530.
  • Tian L. 2002. Development of a Sensor-Based Precision Herbicide Application System, Computers and Electronics in Agriculture, 36, 133-149.
  • Timmermann C., Gerhads R. & Kühbauch W. 2003. The Economic Impact of Site-Specific Weed Control, Precision Agriculture, 4, 249-260.
  • Wan Ishak W. I. & Abdul Rahman K. 2010. Software Development for Real-Time Weed Colour Analysis, Pertanika Journal of Science & Technology, 18 (2), 243-253.
  • Yang C., Prasher S. O., Landry J. & Ramaswamy H. S. 2003. Development of an Image Processing System and a Fuzzy Algorithm for Site-Specific Herbicide Applications, Precision Agriculture, 4, 5–18.
There are 15 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ömer Barış Özlüoymak

Ali Bolat This is me

Ali Bayat This is me

Emin Güzel This is me

Publication Date August 30, 2017
Published in Issue Year 2017 Volume: 1 Issue: 1

Cite

APA Özlüoymak, Ö. B., Bolat, A., Bayat, A., Güzel, E. (2017). A Software Development for Real Time Spray Control System in Herbicide Application. Eurasian Journal of Agricultural Research, 1(1), 13-19.
AMA Özlüoymak ÖB, Bolat A, Bayat A, Güzel E. A Software Development for Real Time Spray Control System in Herbicide Application. EJAR. August 2017;1(1):13-19.
Chicago Özlüoymak, Ömer Barış, Ali Bolat, Ali Bayat, and Emin Güzel. “A Software Development for Real Time Spray Control System in Herbicide Application”. Eurasian Journal of Agricultural Research 1, no. 1 (August 2017): 13-19.
EndNote Özlüoymak ÖB, Bolat A, Bayat A, Güzel E (August 1, 2017) A Software Development for Real Time Spray Control System in Herbicide Application. Eurasian Journal of Agricultural Research 1 1 13–19.
IEEE Ö. B. Özlüoymak, A. Bolat, A. Bayat, and E. Güzel, “A Software Development for Real Time Spray Control System in Herbicide Application”, EJAR, vol. 1, no. 1, pp. 13–19, 2017.
ISNAD Özlüoymak, Ömer Barış et al. “A Software Development for Real Time Spray Control System in Herbicide Application”. Eurasian Journal of Agricultural Research 1/1 (August 2017), 13-19.
JAMA Özlüoymak ÖB, Bolat A, Bayat A, Güzel E. A Software Development for Real Time Spray Control System in Herbicide Application. EJAR. 2017;1:13–19.
MLA Özlüoymak, Ömer Barış et al. “A Software Development for Real Time Spray Control System in Herbicide Application”. Eurasian Journal of Agricultural Research, vol. 1, no. 1, 2017, pp. 13-19.
Vancouver Özlüoymak ÖB, Bolat A, Bayat A, Güzel E. A Software Development for Real Time Spray Control System in Herbicide Application. EJAR. 2017;1(1):13-9.
Eurasian Journal of Agricultural Research (EJAR)   ISSN: 2636-8226   Web: https://dergipark.org.tr/en/pub/ejar   e-mail: agriculturalresearchjournal@gmail.com