Journals / Alınteri Zirai Bilimler Dergisi / 2020 / Cilt: 35 - Sayı: 2

Operational Image Processing Approach for Spray Coverage Measurements in Pesticide Application

Pages
121–129
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)

Overall, the spray coverage was high and measurement error was clearly observable for some of the WSP images using the automatic thresholding approach.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Water sensitive papers (WSPs) are mostly used to determine the performance of spray systems used in pesticide application.
📊
Key Findings & Data:The percent spray coverage of 17 out of 432 analysed WSP images was found to be 24 to 414 times higher when using the automatic as opposed to the dependent thresholding approach.
💡
Conclusion & Impact:Overall, the spray coverage was high and measurement error was clearly observable for some of the WSP images using the automatic thresholding approach.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

Water sensitive papers (WSPs) are mostly used to determine the performance of spray systems used in pesticide application. The most critical stage of an image processing operation is determining the threshold level of WSP images. In this study, an analysis of WSP images was conducted using two thresholding methods. The first was a variable - defined approach (dependent), which determined the thresholding level according to the greyscale level of each WSP sample. The second approach was automated, where the thresholding level was automatically set by image processing software. Using both thresholding methods, the percent spray coverage of the WSP samples was determined using a macro module prepared using the software interface, and the results of both methods were compared. Overall, the spray coverage was high and measurement error was clearly observable for some of the WSP images using the automatic thresholding approach. The percent spray coverage of 17 out of 432 analysed WSP images was found to be 24 to 414 times higher when using the automatic as opposed to the dependent thresholding approach. In addition, extreme values for the percent spray coverage was observed for the automatic method. Our results suggest that percent spray coverage without extreme data can be quickly and practically determined for WSP images using a dependent, variable - defined thresholding method using a macro module.

🔖

Cite This Article

SAYINCI, B., ÇÖMLEK, R., ÇOMAKLI, M., DEMİR, B. (2020). Operational Image Processing Approach for Spray Coverage Measurements in Pesticide Application. Alınteri Zirai Bilimler Dergisi, Cilt: 35 - Sayı: 2, 121–129. https://ajindex.com/articles/356638

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (9)

  1. Cerruto, E., Aglieco, C., Failla, S. & Manetto, G. (2013). Parameters influencing deposit estimation when using water sensitive papers. Journal of Agricultural Engineering, 44(2): e9. https://doi.org/10.4081/jae.2013.e9
  2. Cerruto, E., Failla, S., Lomgo, D. & Manetto, G. (2016). Simulation of water sensitive papers for spray analysis. Agricultural Engineering International: CIGR Journal, 18(4): 22-29.
  3. Cunha, J. P. A. R., Farnese, A. C. & Olivet, J. J. (2013). Computer programs for analysis of droplets sprayed on water sensitive papers. Planta Daninha, 31(3): 715-720. https://doi.org/10.1590/S0100-83582013000300023
  4. Cunha, M., Carvalho, C. & Marcal, A. R. S. (2012). Assessing the ability of image processing software to analyse spray quality on water-sensitive papers used as artificial targets. Biosystems Engineering, 111(1): 11- 23. https://doi.org/10.1016/j.biosystemseng.2011.10.002
  5. Cunha, J. P. A. R., da Reis, E. F., dos Assunçao, H. H. T. & de Landim, T. N. (2019). Evaluation of droplet spectra of the spray tip AD 11002 using different techniques. Engenharia Agrícola, 39(4): 476-481. https://doi.org/10.1590/1809-4430- Eng.Agric.v39n4p476-481/2019 Degré, A., Mostade, O., Huyghebaert, B., Tissot, S. & Debouche, C. (2001). Comparison by image processing of target supports of spray droplets. Transactions of the ASAE, 44(2): 217-222. https://doi.org/10.13031/2013.4677
  6. Fox, R. D., Derksen. R. C., Cooper. J. A., Krause, C. R. & Ozkan, H. E. (2003). Visual and image system measurement of spray deposits using water-sensitive paper. Applied Engineering in Agriculture, 19(5): 549- 552. https://doi.org/10.13031/2013.15315 Lipiński, J. A. & Lipiński, S. (2020). Binarizing water sensitive papers - how to assess the coverage area properly? Crop Protection, 127: 104949. https://doi.org/10.1016/j.cropro.2019.104949
  7. Khot, L. R., Salyani, M., Farooq, M., Walker, T. W., Sweeb, R. D., Larbi, P. A., Smith, V., Pomolis, R. & Stoops, C. A. (2011). Assessment of aerosol deposition and movement in open field conditions. Agricultural Engineering International: CIGR Journal, 13(3): 1-12.
  8. Matthews, G. A. (2000). Pesticide Application Methods. 3rd ed. Oxford, UK: Blackwell Science 7 Ltd. 432p. Nascimento, AB, de Oliveira, G. M., Fonseca, I. C. D., Saab, O. J. G. A. & Canteri, M. G. (2013). Determination of the samples required of water-sensitive paper in experiments related spray technology. Semina: Ciências Agrárias, 34(6): 2687-2696.
  9. Nuyttens, D., Windey, S. & Sonck, B. (2004). Optimiz