Journals / Tarım Bilimleri Dergisi / 2020 / Cilt: 26 - Sayı: 2

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

Pages
190–200
Publication Date
2022-10-17
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It was alsodetermined that the application accuracy rates were 80%, 81.66%and 75% respectively for 4, 6 and 8 km h-1 speeds.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Weed control is vital in agricultural production.
📊
Key Findings & Data:Chemical controlmethods are generally preferred in weed control as they (1) affectquickly and (2) reduce the labour requirement.
💡
Conclusion & Impact:It was alsodetermined that the application accuracy rates were 80%, 81.66%and 75% respectively for 4, 6 and 8 km h-1 speeds.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Weed control is vital in agricultural production. Chemical controlmethods are generally preferred in weed control as they (1) affectquickly and (2) reduce the labour requirement. However, inconventional applications chemicals are generally applied towhole field surface. Therefore, non-targeted areas are alsosprayed. This increases 1) amount of herbicide used and (2)risk of off-target chemical movement. In this study, a patchspraying system was developed to automatically detect and sprayherbicides on weeds in the corn field based on weed density. Inorder to determine the weed regions, a digital camera was fittedin front of the tractor. The images taken using the camera werethen simultaneously processed using an algorithm written inMatlabTM software. The results of the field study showed that at4, 6 and 8 km h-1 forward speeds, application volumes decreaseby 30.21%, 28.82% and 32.28%, respectively, when it iscompared to the conventional application methods. It was alsodetermined that the application accuracy rates were 80%, 81.66%and 75% respectively for 4, 6 and 8 km h-1 speeds.

🔖

Cite This Article

ÜÇGÜL, (., AYBEK, A., KARADÖL, H. (2022). Development of an Automatic System to Detect and Spray Herbicides in Corn Fields. Tarım Bilimleri Dergisi, Cilt: 26 - Sayı: 2, 190–200. https://ajindex.com/articles/594742

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (8)

  1. Agrawal K N, Singh K, Bora G C & Lin D (2012). Weed recognition using image-processing technique based on leaf parameters. Journal of Agricultural Science and Technology 2(8B): 899-908 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. Retrieved in December, 07, 2018 from https://www.tarimorman.gov.tr/TAGEM/Belgeler/Teknik%20tal%C4%B1matlar%202008/C%C4%B0LT %204. pdf 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
  2. 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: 165-182 Hlaing S H & Khaing A S (2014). Weed and crop segmentation and classification using area thresholding. International Journal of Research in Engineering and Technology 3(3): 375-380
  3. IDS (2017). USB 2 uEye ML industrial camera. Retrieved in January, 26, 2017 from https://en.ids-imaging.com/store/ products/cameras/usb-2-0-cameras/ueye-l.html Jeon H Y, Tian L F & Zhu H (2011). Robust crop and weed segmentation under uncontrolled outdoor illumination. Sensors 11(1): 6270-6283
  4. Liu H, Lee S H & Saunders C (2014). Development of a machine vision system for weed detection during both of off-season and in-season in broadacre no-tillage croppıng lands. American Journal of Agricultural and Biological Sciences 9(2): 174- 193 Matlab (2017).
  5. Image acquisition toolbox, The Mathworks Inc. Retrieved in March, 11, 2017 from https://www.mathworks.com/products/imaq.html
  6. Ortiz M P, Pena J M, Gutierrez P A, Sanchez J T & Martinez C H (2015). A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Applied Soft Computing 37(2015): 533- 544 Otsu N (1979). A threshold selection method from graylevel histograms. IEEE Transactions Systems Man Cybernetics 9(1): 62-66
  7. Pajares G (2015). Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVS), Photogrammetric Engineering & Remote Sensing 81(4): 281-330
  8. 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. Weed Science 52(5)