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Journals / EMO Bilimsel Dergi / 2026 / Cilt: 16 Sayı: 2

Automatic Detection, Digital Identification, and Intelligent Monitoring of Olive Trees

Zeytin Ağaçlarının Otomatik Tespiti, Dijital Kimliklendirilmesi ve Akıllı Takibi

Kübra Şahin
Tayfun Akgül(ISTANBUL TECHNICAL UNIVERSITY)0000-0002-5164-5736
Melike Girgin(ISTANBUL TECHNICAL UNIVERSITY)0009-0006-0659-3515
Pages
7–15
Publication Date
2026-05-31
DOI
—
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AI Summary & Key Takeaways

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

This pilot study may enable to create: i-) a nationwide network capable of assigning digital identities to all fruit trees, ii-) a robust traceability chain from production to consumption.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents the automated detection of olive trees from drone and satellite images, the estimation of their true locations, and the digital identification of individual trees using QR codes.
📊
Key Findings & Data:Using collected dataset, a YOLOv8-based olive tree detection model was trained and applied to the drone images.
💡
Conclusion & Impact:This pilot study may enable to create: i-) a nationwide network capable of assigning digital identities to all fruit trees, ii-) a robust traceability chain from production to consumption.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Ağaç Tespiti · Akıllı Tarım · Coğrafi Konum Belirleme

Abstract

This study presents the automated detection of olive trees from drone and satellite images, the estimation of their true locations, and the digital identification of individual trees using QR codes. The pilot implementation was carried out in two olive fields with more than one thousand trees in the Belevi district of İzmir. Drone-based video and photo recordings were collected in these fields, along with thermal–optical images and GPS data from mobile devices. Using collected dataset, a YOLOv8-based olive tree detection model was trained and applied to the drone images. A geometric method was developed to determine the position of each tree from both drone and satellite images. QR codes which were generated and attached to each olive tree, were linked to a GIS and a web interface. This pilot study may enable to create: i-) a nationwide network capable of assigning digital identities to all fruit trees, ii-) a robust traceability chain from production to consumption.

Özet

Zeytin ağaçlarının dron ve uydulardan otomatik tespiti, her ağacın dijital kimliklendirilmesi ve ürün takibinin kolaylaştırılması çalışması İzmir ili, Belevi ilçesindeki iki zeytin arazisinde pilot uygulama ile hayata geçirilmiştir. Arazilerde, dron ile video ve fotoğraf çekimleri yapılmış; termal–optik görüntüler ve mobil cihazlardan GPS kayıtları alınmıştır. Bu verilerle YOLOv8 tabanlı bir zeytin ağacı tespit modeli eğitilmiş, drondan elde edilen görüntülere uygulanmıştır. Dron görsellerinden her bir ağacın konumunun tespiti için geometrik yöntem geliştirilmiş, elde edilen konum kestirimleriyle uydu verileri karşılaştırılmıştır. Binden fazla zeytin ağacı için QR kodlar hazırlanmış, her bir ağaca çakılmış, CBS ve web arayüzüyle ilişkilendirilmiştir. Bu pilot çalışmanın genişletilmesiyle ülke bazında tüm meyve ağaçlarına dijital kimlik kazandırabilecek bir ağın oluşturulması, böylelikle üretimden tüketime gürbüz takip zincirinin kurulması mümkün kılınabilecektir.

🔖

Cite This Article

Şahin, K., Akgül, T., Girgin, M. (2026). Automatic Detection, Digital Identification, and Intelligent Monitoring of Olive Trees. EMO Bilimsel Dergi, Cilt: 16 Sayı: 2, 7–15. https://ajindex.com/articles/1390247

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References (31)

  1. N. Abdullah, N. A. B. Durani, M. F. B. Shari, K. S. Siong, V. K. W. Hau, W. N. Siong ve I. K. A. Ahmad, “Towards smart agriculture monitoring using fuzzy systems,” IEEE Access, c. 9, ss. 4097–4111, 2021.
  2. K. Jha, A. Doshi, P. Patel ve M. Shah, “A comprehensive review on automation in agriculture using artificial intelligence,” Artificial Intelligence in Agriculture, c. 2, ss. 1–12, 2019.
  3. G. Erdoğan ve S. Şahin, “Kırsal kalkınmada mekânsal model akıllı köy kümeleri: Datça örneği,” International Geography Symposium on the 30th Anniversary of TUCAUM, ss. 1468–1477, 2018.
  4. R. Valentini, L. Belelli Marchesini, D. Gianelle, G. Sala, A. Yarovslavtsev, V. Vasenev ve S. Castaldi, “New tree monitoring systems: from industry 4.0 to nature 4.0,” Annals of Silvicultural Research, c. 43, s. 2, ss. 84–88, 2019.
  5. T.C. Tarım ve Orman Bakanlığı, “IV. Tarım Orman Şûrası Sonuç Bildirgesi açıklandı,” 2025.
  6. M. Brandt, J. Chave, S. Li, R. Fensholt, P. Ciais, J.-P. Wigneron, F. Gieseke, S. Saatchi, C. Tucker ve C. Igel, “High-resolution sensors and deep learning models for tree resource monitoring,” Nature Reviews Electrical Engineering, c. 2, s. 1, ss. 13–26, 2025.
  7. E. C. Rodríguez-Garlito ve A. Paz-Gallardo, “Efficiently mapping large areas of olive trees using drones in Extremadura, Spain,” IEEE Journal on Miniaturization for Air and Space Systems, c. 2, s. 3, ss. 148–156, 2021.
  8. J. M. Jurado, L. Ortega, J. J. Cubillas ve F. R. Feito, “Multispectral mapping on 3D models and multi-temporal monitoring for individual characterization of olive trees,” Remote Sensing, c. 12, s. 7, s. 1106, 2020.
  9. M. I. Ramos, J. J. Cubillas, R. M. Córdoba ve L. M. Ortega, “Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning,” PloS One, c. 20, s. 1, s. e0311530, 2025.
  10. O. Nevalainen, E. Honkavaara, S. Tuominen, N. Viljanen, T. Hakala, X. Yu, J. Hyyppä, H. Saari, I. Pölönen, N. N. Imai ve A. M. G. Tommaselli, “Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging,” Remote Sensing, c. 9, s. 3, 2017.
  11. A. S. Anifantis, S. Camposeo, G. A. Vivaldi, F. Santoro ve S. Pascuzzi, “Comparison of UAV photogrammetry and 3D modeling techniques with other currently used methods for estimation of the tree row volume of a super-high-density olive orchard,” Agriculture, c. 9, s. 11, s. 233, 2019.
  12. D. Stateras ve D. Kalivas, “Assessment of olive tree canopy characteristics and yield forecast model using high resolution UAV imagery,” Agriculture, c. 10, s. 9, s. 385, 2020.
  13. L. Ortenzi, S. Violino, F. Pallottino, S. Figorilli, S. Vasta, F. Tocci, F. Antonucci, G. Imperi ve C. Costa, “Early estimation of olive production from light drone orthophoto, through canopy radius,” Drones, c. 5, s. 4, s. 118, 2021.
  14. R. R. Sola-Guirado, F. J. Castillo-Ruiz, F. Jiménez-Jiménez, G. L. Blanco-Roldan, S. Castro-Garcia ve J. A. Gil-Ribes, “Olive actual ‘on year’ yield forecast tool based on the tree canopy geometry using UAS imagery,” Sensors, c. 17, s. 8, s. 1743, 2017.
  15. A. Di Nisio, F. Adamo, G. Acciani ve F. Attivissimo, “Fast detection of olive trees affected by Xylella fastidiosa from UAVs using multispectral imaging,” Sensors, c. 20, s. 17, s. 4915, 2020.
  16. C. Vasilakos ve V. S. Verykios, “Burned olive trees identification with a deep learning approach in unmanned aerial vehicle images,” Remote Sensing, c. 16, s. 23, s. 4531, 2024.
  17. Google LLC, “Google Earth Pro,” 2026.
  18. A. Moran, “Students tackle environmental issues in Colombia and Türkiye,” IEEE Spectrum, Kasım 2024.
  19. Roboflow, “Roboflow,” erişim: 16 Aralık 2024.
  20. AirData, “AirData UAV - flight data analysis, mapping, and logging,” erişim: 25 Ocak 2025.
  21. Esri, “ArcGIS,” erişim: 20 Şubat 2026.
  22. Earth Maps, “Earth Maps Mobile Application,” erişim: 23 Ekim 2025.
  23. L. Shen, B. Lang ve Z. Song, “DS-YOLOv8-Based object detection method for remote sensing images,” IEEE Access, c. 11, ss. 125122–125137, 2023.
  24. U. Demirci, M. Girgin ve T. Akgül, “Determining tree locations via satellite and drone data,” 2025 33rd Signal Processing and Communications Applications Conference (SIU), ss. 1–4, 2025.
  25. X. Wan, Y. Shao, S. Zhang ve S. Li, “Terrain aided planetary UAV localization based on geo-referencing,” IEEE Transactions on Geoscience and Remote Sensing, c. 60, ss. 1–18, 2022.
  26. X. Zhao, F. Pu, Z. Wang, H. Chen ve Z. Xu, “Detection, tracking, and geolocation of moving vehicle from UAV using monocular camera,” IEEE Access, c. 7, ss. 101160–101170, 2019.
  27. A. Graser, Learning QGIS. Packt Publishing Ltd, 2016.
  28. S. Khan ve K. Mohiuddin, “Evaluating the parameters of ArcGIS and QGIS for GIS applications,” International Journal of Advanced Research in Science and Engineering, c. 7, ss. 582–594, 2018.
  29. Esri, “Import KML/KMZ (Keyhole Markup Language),” ArcGIS CityEngine Documentation, erişim: 15 Ağustos 2025.
  30. K. Şahin, T. Akgül ve M. Girgin, “Smart monitoring of olive fields,” 2025 33rd Signal Processing and Communications Applications Conference (SIU), ss. 1–4, 2025.
  31. Y. Dong, Z. Fu, S. Stankovski, S. Wang ve X. Li, “Nutritional quality and safety traceability system for China’s leafy vegetable supply chain based on fault tree analysis and QR code,” IEEE Access, c. 8, ss. 161261–161275, 2020.