Dergiler / Turkish Journal of Agriculture and Forestry / 2019 / Cilt: 43 - Sayı: 3

The development of a low cost UAV-based image acquisition system and the procedure for capturing data in precision agriculture

Sayfa
288–298
Yayın Tarihi
2022-10-19
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In the next step, future field tests will be conductedto assess the practical usage of running the cycling process.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:: Remote sensing is a method of monitoring the natural heterogeneity of vegetation.
📊
Temel Bulgular & Veri:These were modified to capture electromagnetic energy ranging from 380 nm to 1100 nm.
💡
Sonuç & Katkı:In the next step, future field tests will be conductedto assess the practical usage of running the cycling process.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

: Remote sensing is a method of monitoring the natural heterogeneity of vegetation. Although satellite based remote sensing hasbeen a popular method for monitoring the earth’s surface, it has several drawbacks, such as the orbital period, unattended capture, andinvestment cost. On the other hand, an unmanned air vehicle (UAV) is more flexible in terms of deployment, monitoring a small area,and being easy to obtain at a low cost. From this point of view, the goal of this research was to develop a low cost and easy to implementtechnical solution for mapping spatial heterogeneity and research its relationship with plant conditions. The intention was to developa cycling process starting with a UAV-based image-capturing tool for an easy and reasonable production of a normalized differencevegetation index (NDVI) and the resulting prescription maps, especially of vineyards. The main parts of this image acquisition systemwere the UAV and modified digital cameras purchased from the store. Two different fixed-winged UAVs were built for this study basedon commercial airplane models and used open source autopilot. Two small digital cameras (Nikon and Canon) were tested for capturingthe images. These were modified to capture electromagnetic energy ranging from 380 nm to 1100 nm. Camera calibration tests wereconducted and a UAV-based image acquisition system was successfully developed. In the next step, future field tests will be conductedto assess the practical usage of running the cycling process.

🔖

Bu Makaleye Atıf Yap

TEKİN, A. B., FORNALE, M. (2022). The development of a low cost UAV-based image acquisition system and the procedure for capturing data in precision agriculture. Turkish Journal of Agriculture and Forestry, Cilt: 43 - Sayı: 3, 288–298. https://ajindex.com/articles/599244

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (14)

  1. Aggarwal S (2013). Principles of remote sensing. WAMIS Next Phase Meeting Brief Summary. Florence, Italy. pp. 23-38.
  2. Agisoft Photoscan (2015). Photogrammetric processing of digital image software, Version 1.3.5. Agisoft, LLC. St. Petersburg, Russia. Auernhammer H (2001). Precision farming the environmental challenge. Comput Electron Agric 32: 31-43.
  3. Awasthi A, Reddy SRN (2013). Monitoring for precision agriculture using wireless sensor network – a review. Global Journal of Computer Science and Technology 13: 23-28.
  4. Balafoutis A, Beck B, Fountas S, Vangeyte J, Wal T, Soto I, GómezBarbero M, Barnes A, Eory V (2017). Precision agriculture technologies positively contributing to ghg emissions mitigation, farm productivity and economics. Sustainability 9: 1339-1367.
  5. Baluja J, Diago MP, Balda P, Zorer R, Meggio F Morales F Tardaguila J (2012). Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Sci 30: 511-522.
  6. Berni JAJ, Zarco-Tejada PJ, Suárez L, González-Dugo V, Fereresa E (2008). Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII, 3–11 July 2008;
  7. Beijing, China: International Society of Photogrammetry and Remote Sensing (ISPRS), pp. 1-6.
  8. Berni JAJ, Zarco-Tejada PJ, Suárez L, Fereres E (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE T Geosci Remote 47: 722-738.
  9. Blackmore BS (1994). Precision farming; an introduction. Outlook Agr 23: 275-280.
  10. Blackmore S (2002). Developing the principles of precision farming. In: Proceedings of the International Conference on Agropoles and Agro-Industrial Technological Parks-Agrotech, 1999; pp. 75-77.
  11. Bobbe T, McKean J, Zigadlo JP (1995). An evaluation of natural color and color infrared digital cameras as a remote sensing tool for natural resource management. In: Proc. SPIE 2555. Airborne Reconnaissance XIX, 1 September 1995.
  12. WA, USA: Society of Photo-Optical Instrumentation Engineers, pp. 151-157.
  13. Bramley R, Pearse B, Chamberlain P (2003). Being profitable precisely – A case study of precision viticulture from Margaret River. Annual Technical Issue. Australian and New Zealand Grapegrower and Winemaker 423a: 84-87.
  14. Bramley RGV, Hamilton RP (2004). Understanding variability in wine grape production system