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Year 2020, Volume: 26 Issue: 2, 190 - 200, 04.06.2020
https://doi.org/10.15832/ankutbd.495903

Abstract

References

  • Aydemir S & Karaoğlu S (2008). Zirai Mücadele Teknik Talimatları Cilt VI. T.C. Gıda Tarım ve Hayvancılık Bakanlığı, Tarımsal Araştırmalar ve Politikalar Genel Müdürlüğü, Bitki Sağlığı Araştırmaları Daire Başkanlığı. Burgos-Artizzu X. P, Ribeiro A, Guijarro M & Pajares G (2011). Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75 (2): 337–346 Hlaing S.H & Khaing, A.S (2014). Weed and Crop Segmentation and Classification Using Area Thresholding. IJRET: International Journal of Research in Engineering and Technology, 3 (3):375-380. IDS (2017). USB 2 uEye ML Endüstriyel Kamera. https://en.ids-imaging.com/store/products/cameras/usb-2-0-cameras/ueye-l.html. (Accessed 26.01.2017) Jeon H.Y, Tian L.F & Zhu H (2011). Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination. Sensors, 11 (1): 6270-6283. Kamal N.A, Karan S, Ganesh C.B & Dongqing L (2012). Weed Recognition Using Image-Processing Technique Based on Leaf Parameters Journal of Agricultural Science and Technology, ISSN 1939-1250. Matlab, 2017. Image Acquisition Toolbox, The Mathworks Inc. https://www.mathworks.com/products/imaq.html. (Accessed 11.03.2017) Otsu N (1979). A Threshold selection method from graylevel histograms. IEEE Trans. Syst. Man Cybern., 9 (1): 62-66. DOI: 10.1109/TSMC.1979.4310076 Rajcan I, Chandler K.J & Swanton C. J (2004). Red-Far-Red Ratio of Reflected Light: A Hypothesis of Why Early-Season Weed Control Is Important in Corn. 52 (5): 774-778 Romeo j, Guerrero J.M, Montalvo M, Emmi L, Guijarro M, Santos P.G & Pajares G (2013). Camera Sensor Arrangement for Crop/Weed Detection Accuracy in Agronomic Images. Sensors, 13 (4): 4348-4366 Sabancı K (2013). Seker pancarı tariminda yabanci ot mucadelesi icin degisken duzeyli herbisit uygulama parametrelerinin yapay sinir aglariyla belirlenmesi. Doktora Tezi, Selcuk Universitesi, Fen Bilimleri Enstitusu, Konya TAGEM (2017). Misir Tarimi. http://arastirma.tarim.gov.tr/ttae/Sayfalar/Detay.aspx?SayfaId=89. (Accessed 27.02.2017) Tekinalp Z, Ozturk S & Kuncan M (2013). OPC Kullanilarak Gercek Zamanli Haberlesen Matlab ve PLC Kontrollu Sistem. Otomatik Kontrol Ulusal Toplantisi, TOK2013, September 26-28, Malatya Tursun N, Sakinmaz M.S & Kantarci Z (2015). Misir Varyetelerinde Yabanci Ot Kontrolu icin Kritik Periyotlarin Belirlenmesi. Tarla Bitkileri Merkez Arastirma Enstitusu Dergisi, 25 (Ozel sayi-1): 58-63 Ustuner T & Guncan A (2002). Nigde ve Yoresi Patates Tarlalarinda Sorun Olan Yabanci Otlarin Yogunlugu ve Onemi ile Topluluk Olusturmalari Uzerine Arastirmalar. Turkiye Herboloji Dergisi. 5 (2): 30-42 Vioix J.B, Sliwa T & Gee C.H (2006). An Automatic Inter and intra-row weed detection in agronomic images, XVI CIGR World Congress Woebbecke D.M, Meyer G.E & Von Bargen Mortensen D.A (1995). Shape features for identifying young weeds using image analysis. Transactions of the ASAE 38 (1): 271-281.

Development of an Automatic System to Detect and Spray Herbicides in Corn Fields

Year 2020, Volume: 26 Issue: 2, 190 - 200, 04.06.2020
https://doi.org/10.15832/ankutbd.495903

Abstract

Weed control is vital in agricultural production. Chemical control methods are generally preferred in weed control as they (1) affect quickly and (2) reduce the labour requirement. However, in conventional applications chemicals are generally applied to whole field surface. Therefore, non-targeted areas are also sprayed. This increases 1) amount of herbicide used and (2) risk of off-target chemical movement. In this study, a patch spraying system was developed to automatically detect and spray herbicides on weeds in the corn field based on weed density. In order to determine the weed regions, a digital camera was fitted in front of the tractor. The images taken using the camera were then simultaneously processed using an algorithm written in MatlabTM software. The results of the field study showed that at 4, 6 and 8 km h-1 forward speeds, application volumes decrease by 30.21%, 28.82% and 32.28%, respectively, when it is compared to the conventional application methods. It was also determined that the application accuracy rates were 80%, 81.66% and 75% respectively for 4, 6 and 8 km h-1 speeds.

References

  • Aydemir S & Karaoğlu S (2008). Zirai Mücadele Teknik Talimatları Cilt VI. T.C. Gıda Tarım ve Hayvancılık Bakanlığı, Tarımsal Araştırmalar ve Politikalar Genel Müdürlüğü, Bitki Sağlığı Araştırmaları Daire Başkanlığı. Burgos-Artizzu X. P, Ribeiro A, Guijarro M & Pajares G (2011). Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 75 (2): 337–346 Hlaing S.H & Khaing, A.S (2014). Weed and Crop Segmentation and Classification Using Area Thresholding. IJRET: International Journal of Research in Engineering and Technology, 3 (3):375-380. IDS (2017). USB 2 uEye ML Endüstriyel Kamera. https://en.ids-imaging.com/store/products/cameras/usb-2-0-cameras/ueye-l.html. (Accessed 26.01.2017) Jeon H.Y, Tian L.F & Zhu H (2011). Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination. Sensors, 11 (1): 6270-6283. Kamal N.A, Karan S, Ganesh C.B & Dongqing L (2012). Weed Recognition Using Image-Processing Technique Based on Leaf Parameters Journal of Agricultural Science and Technology, ISSN 1939-1250. Matlab, 2017. Image Acquisition Toolbox, The Mathworks Inc. https://www.mathworks.com/products/imaq.html. (Accessed 11.03.2017) Otsu N (1979). A Threshold selection method from graylevel histograms. IEEE Trans. Syst. Man Cybern., 9 (1): 62-66. DOI: 10.1109/TSMC.1979.4310076 Rajcan I, Chandler K.J & Swanton C. J (2004). Red-Far-Red Ratio of Reflected Light: A Hypothesis of Why Early-Season Weed Control Is Important in Corn. 52 (5): 774-778 Romeo j, Guerrero J.M, Montalvo M, Emmi L, Guijarro M, Santos P.G & Pajares G (2013). Camera Sensor Arrangement for Crop/Weed Detection Accuracy in Agronomic Images. Sensors, 13 (4): 4348-4366 Sabancı K (2013). Seker pancarı tariminda yabanci ot mucadelesi icin degisken duzeyli herbisit uygulama parametrelerinin yapay sinir aglariyla belirlenmesi. Doktora Tezi, Selcuk Universitesi, Fen Bilimleri Enstitusu, Konya TAGEM (2017). Misir Tarimi. http://arastirma.tarim.gov.tr/ttae/Sayfalar/Detay.aspx?SayfaId=89. (Accessed 27.02.2017) Tekinalp Z, Ozturk S & Kuncan M (2013). OPC Kullanilarak Gercek Zamanli Haberlesen Matlab ve PLC Kontrollu Sistem. Otomatik Kontrol Ulusal Toplantisi, TOK2013, September 26-28, Malatya Tursun N, Sakinmaz M.S & Kantarci Z (2015). Misir Varyetelerinde Yabanci Ot Kontrolu icin Kritik Periyotlarin Belirlenmesi. Tarla Bitkileri Merkez Arastirma Enstitusu Dergisi, 25 (Ozel sayi-1): 58-63 Ustuner T & Guncan A (2002). Nigde ve Yoresi Patates Tarlalarinda Sorun Olan Yabanci Otlarin Yogunlugu ve Onemi ile Topluluk Olusturmalari Uzerine Arastirmalar. Turkiye Herboloji Dergisi. 5 (2): 30-42 Vioix J.B, Sliwa T & Gee C.H (2006). An Automatic Inter and intra-row weed detection in agronomic images, XVI CIGR World Congress Woebbecke D.M, Meyer G.E & Von Bargen Mortensen D.A (1995). Shape features for identifying young weeds using image analysis. Transactions of the ASAE 38 (1): 271-281.
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Details

Primary Language English
Journal Section Makaleler
Authors

Hayrettin Karadöl 0000-0002-5062-0887

Ali Aybek 0000-0003-3036-8204

Mustafa Üçgül This is me 0000-0001-8528-7490

Publication Date June 4, 2020
Submission Date December 12, 2018
Acceptance Date March 19, 2019
Published in Issue Year 2020 Volume: 26 Issue: 2

Cite

APA Karadöl, H., Aybek, A., & Üçgül, M. (2020). Development of an Automatic System to Detect and Spray Herbicides in Corn Fields. Journal of Agricultural Sciences, 26(2), 190-200. https://doi.org/10.15832/ankutbd.495903

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