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Journals / Journal of Agricultural Biotechnology / 2026 / Cilt: 7 Sayı: 1

UAV-Based Imaging and Artificial Intelligence Applications in Viticulture: Monitoring Plant Growth, Diseases, Pests, and Stress Factors

Bağcılıkta İHA Tabanlı Görüntüleme ve Yapay Zekâ Uygulamaları: Bitki Gelişimi, Hastalık, Zararlı ve Stres Faktörlerinin İzlenmesi

Gürkan Güvenç Avcı(Tekirdağ Bağcılık Arşatırma Enstitüsü)0000-0002-2760-0773
Lerzan Öztürk0000-0003-2199-6807
Pages
32–46
Publication Date
2026-07-03
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AI Summary & Key Takeaways

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

Overall, UAV-based technologies provide significant advantages for vineyard monitoring and contribute to the advancement of precision viticulture practices.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Maintaining yield, fruit quality, and sustainable production in vineyards requires regular monitoring of vine development and the timely identification of factors that limit production.
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Key Findings & Data:Since traditional field surveys are labor-intensive and time-consuming, especially over large areas, the use of unmanned aerial vehicles (UAVs) and remote sensing technologies has increased considerably in recent years.
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Conclusion & Impact:Overall, UAV-based technologies provide significant advantages for vineyard monitoring and contribute to the advancement of precision viticulture practices.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:İHA · Uzaktan Algılama · Görüntü İşleme

Abstract

Maintaining yield, fruit quality, and sustainable production in vineyards requires regular monitoring of vine development and the timely identification of factors that limit production. In particular, the early detection of diseases, pests, nematodes, water stress, nutrient deficiencies, and other environmental factors is essential for sustainable vineyard management. Since traditional field surveys are labor-intensive and time-consuming, especially over large areas, the use of unmanned aerial vehicles (UAVs) and remote sensing technologies has increased considerably in recent years. UAV systems equipped with RGB, multispectral, hyperspectral, thermal, and LiDAR sensors enable the rapid acquisition of high-resolution data from vineyard areas. These data can be used to assess vine growth, canopy characteristics, water status, nutrient conditions, and disease symptoms at a spatial scale. In addition, vegetation indices such as NDVI, NDRE, GNDVI, SAVI, and CWSI, together with plant growth parameters including Leaf Area Index (LAI), Canopy Cover, Canopy Height Model (CHM), and Tree Row Volume (TRV), are widely used for vineyard monitoring. Through image processing and deep learning approaches, UAV imagery can be analyzed to automatically identify diseases, pests, and stress-related changes. This review examines UAVplatforms, sensor technologies, vegetation indices, plant growth parameters, and image analysis techniques used in vineyards, and evaluates their applications for detecting diseases, pests, and stress factors. Overall, UAV-based technologies provide significant advantages for vineyard monitoring and contribute to the advancement of precision viticulture practices.

Özet

Bağcılıkta verim, kalite ve sürdürülebilir üretim hedeflerinin korunabilmesi için bitki gelişiminin düzenli olarak izlenmesi ve üretimi sınırlandıran faktörlerin zamanında belirlenmesi gerekmektedir. Özellikle verim ve kaliteyi etkileyen hastalıklar, zararlılar, nematodlar, su stresi, besin elementi eksiklikleri ve diğer çevresel faktörlerin zamanında belirlenmesi sürdürülebilir üretim açısından büyük önem taşımaktadır. Geleneksel arazi gözlemlerine dayanan yöntemler geniş alanların izlenmesinde zaman ve iş gücü gerektirdiğinden, son yıllarda insansız hava araçları (İHA) ve uzaktan algılama teknolojilerinin kullanımı yaygınlaşmıştır. RGB, multispektral, hiperspektral, termal ve LiDAR sensörleri ile donatılan İHA sistemleri sayesinde bağ alanlarından yüksek çözünürlüklü veriler kısa sürede elde edilebilmektedir. Bu veriler kullanılarak bitki gelişimi, kanopi özellikleri, su stresi, besin elementi durumu ve hastalık belirtileri mekânsal olarak değerlendirilebilmektedir. Ayrıca NDVI, NDRE, GNDVI, SAVI ve CWSI gibi vejetasyon indeksleri ile LAI, Canopy Cover, CHM ve TRV gibi bitki gelişim parametreleri bağların izlenmesinde yaygın olarak kullanılmaktadır. İHA görüntülerinin değerlendirilmesinde görüntü işleme ve derin öğrenme yöntemlerinden yararlanılarak hastalıklar, zararlılar ve stres kaynaklı değişimler otomatik olarak belirlenebilmektedir. Bu derlemede bağ alanlarında kullanılan İHA sistemleri, sensör teknolojileri, vejetasyon indeksleri, bitki gelişim parametreleri ve görüntü analiz yöntemleri ele alınmış; bu teknolojilerin hastalık, zararlı ve stres faktörlerinin belirlenmesindeki kullanım olanakları değerlendirilmiştir. Sonuç olarak İHA tabanlı sistemlerin bağ alanlarının izlenmesinde önemli avantajlar sağladığı ve hassas bağcılık uygulamalarının geliştirilmesine katkıda bulunduğu görülmektedir.

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Cite This Article

Avcı, G. G., Öztürk, L. (2026). UAV-Based Imaging and Artificial Intelligence Applications in Viticulture: Monitoring Plant Growth, Diseases, Pests, and Stress Factors. Journal of Agricultural Biotechnology, Cilt: 7 Sayı: 1, 32–46. https://doi.org/10.58728/joinabt.1970736

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