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

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.

Keywords

• UAV
• Remote Sensing
• Image Processing
• Precision Viticulture.

Supporting Institution

no

Project Number

yok

Ethical Statement

no

Thanks

no

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

Abstract

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.

Keywords

• İHA
• Uzaktan Algılama
• Görüntü İşleme
• Hassas Bağcılık.

Supporting Institution

yok

Project Number

yok

Ethical Statement

yok

Thanks

yok

References

1. [1] Cándido-Mireles, M., Hernández-Gama, R., Salas, J. (2023). Detecting vineyard plants stress in situ using deep learning. Computers and Electronics in Agriculture, 210, 107837. https://doi.org/10.1016/j.compag.2023.107837 [ 2] Creasy, G.L., Creasy, L.L. (2009). Grapevine pests, diseases and disorders. İçinde: Grapes (s. 185–223). CABI, Wallingford, UK.
2. [3] Matese, A., Di Gennaro, S.F. (2018). Practical applications of a multisensor UAV platform based on multispectral, thermal and RGB high resolution images in precision viticulture. Agriculture, 8(7), 116. https://doi.org/10.3390/agriculture8070116
3. [4] Demirel, O. (2022). Bitkisel indeksler. Smart Soil Tarım Teknolojileri. https://smartsoil.com.tr/blog/hassastarim-uygulamalari-1/bitkisel-indeksler-2.
4. [5] Bai, Y., Hou, F., Fan, X., Lin, W., Lu, J., Zhou, J., Fan, D., Li, L. (2023). A lightweight pest detection model for drones based on transformer and super-resolution sampling techniques. Agriculture, 13(9), 1812. https://doi.org/10.3390/agriculture13091812
5. [6] Xue, X., Thakur, V., Dhumras, H., Jhaveri, R.H., Gadekallu, T.R. (2025). Real-time pest detection using ResNet-50 and vision transformer: An IoT-enabled mobile application for smart agriculture. IEEE Transactions on Consumer Electronics.
6. [7] Manoj, H., Shanthi, D.L., Lakshmi, B.N., vd. (2026). AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas. Scientific Reports, 16, 2479. https://doi.org/10.1038/s41598-025-32384-1
7. [8] Akkamış, M., Çalışkan, S. (2020). İnsansız hava araçları ve tarımsal uygulamalarda kullanımı. Türkiye İnsansız Hava Araçları Dergisi, 2(1), 8–16. https://izlik.org/JA86RE98UX.
8. [9] Zhu, H., Lin, C., Liu, G., Wang, D., Qin, S., Li, A., Xu, J.L., He, Y. (2024). Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection. Frontiers in Plant Science, 15, 1435016. https://doi.org/10.3389/fpls.2024.1435016

9. [10] Gholami, A. (2026). AI-driven UAV systems in precision farming: Technologies, applications, and future perspectives. International Journal of Research Publication and Reviews, 7(2), 872–896. DOI: 10.55248/gengpi.07.0226.0254
10. [11] Isa, A.I., Muhammad, A.B. (2024). The different types of unmanned aerial vehicle (UAV): Characteristics, capabilities, and challenges. Global Journal of Research in Engineering & Computer Sciences, 4(5), 89–94. https://doi.org/10.5281/zenodo.13799954
11. [12] Agrawal, J., Arafat, M.Y. (2024). Transforming farming: A review of AI-powered UAV technologies in precision agriculture. Drones, 8(11), 664. https://doi.org/10.3390/drones8110664
12. [13] Yang, X., Chen, J., Lu, X., Liu, H., Liu, Y., Bai, X., Qian, L., Zhang, Z. (2025). Advances in factors, and challenges. Plants, 14(16), 2544. https://doi.org/10.3390/plants14162544
13. [14] Abbas, A., Zhang, Z., Zheng, H., Alami, M.M., Alrefaei, A.F., Abbas, Q., Naqvi, S.A.H., Rao, M.J., Mosa, W.F.A., Abbas, Q., Hussain, A., Hassan, M.Z., Zhou, L. (2023). Drones in plant disease assessment, efficient monitoring, and detection: A way forward to smart agriculture. Agronomy, 13(6), 1524. https://doi.org/10.3390/agronomy13061524
14. [15] HOVERAir. (2026). Different types of drones and uses. HOVERAir Blog. Erişim adresi: https://hoverair.com/de-de/blogs/guide/drone-types-and-uses-2026 (Erişim Tarihi: 14.06.2026).
15. [16] Panday, U.S., Pratihast, A.K., Aryal, J., Kayastha, R.B. (2020). A review on drone-based data solutions for cereal crops. Drones, 4(3), 41. https://doi.org/10.3390/drones4030041
16. [17] Gracia-Romero, A., Vergara-Díaz, O., Thierfelder, C., Cairns, J. E., Kefauver, S. C., & Araus, J. L. (2018). Phenotyping conservation agriculture management effects on ground and aerial remote sensing assessments of maize hybrids performance in Zimbabwe. Remote Sensing, 10(2), 349. https://doi.org/10.3390/rs10020349
17. [18] DJI. (2015). Phantom 3. DJI Technology Co., Ltd. https://www.dji.com/global/products/phantom-3 (ErişimTarihi: 14.06.2026).
18. [19] MikroKopter. (2026). MK-OktoXL 6S12 AGRAR precision farming UAV platform. MikroKopter GmbH. https://mikrokopter.de/en/products/copter-oktoxls12/addon-6sagrar (Erişim Tarihi: 14.06.2026).
19. [20] Aiviewgroup. (2026). Delairtech DT-18 long range BVLOS-certified small UAV. Aiviewgroup. https://aiviewgroup.com/products.html (Erişim Tarihi: 14.06.2026).
20. [21] Sassu, A., Psiroukis, V., Bettucci, F., vd. (2024). Unmanned aerial system plant protection products spraying performance evaluation on a vineyard. Precision Agriculture, 25, 2082–2112. https://doi.org/10.1007/s11119-024-10155-8
21. [22] Dorofeeva, A.A., Ponomarenko, E.A., Lukyanova, Y.Y., Fomina, E.A., Buchatskiy, P.Y. (2021). High precision unmanned agro copters in eco-friendly viticulture systems. CEUR Workshop Proceedings, 2914, 299–306.
22. [23] TT Aviation. (2018). Agriculture UAV. https://www.ttaviation.org/procat/agriculture-uav/ (Erişim Tarihi: 13.06.2026)
23. [24] DJI. (2026). DJI AGRAS T16. DJI Technology Co., Ltd. https://www.dji.com/global/downloads/products/t16#app (Erişim Tarihi: 14.06.2026).
24. [25] DJI. (2016). DJI Agras MG-1 agricultural spraying system. DJI Technology Co., Ltd. Shenzhen, China.
25. [26] Beijing TT Aviation Technology Co., Ltd. (2026). Spraying rack drone for agricultural spraying. Made-inChina. https://ttadrone.en.made-in-china.com/product/kQEpRIrdueUt/China-Crop-Agricultural-PesticideSpraying-Drone-Uav-Drone-Agricultural-Spraying-Rack-Drone-for-Agricultural-Spraying.html (Erişim Tarihi: 14.06.2026).
26. [27] Mousavi, S.A., Farahani, G. (2022). A novel enhanced VGG16 model to tackle grapevine leaves diseases with automatic method. IEEE Access, 10, 111564–111578.
27. [28] Haleeva, A., Hruban, V., Horbunov, M., Ruzhniak, M. (2024). Improving the process of plant protection mechanisation in grape growing. Ukrainian Black Sea Region Agrarian Science, 28(4), 85–95. https://doi.org/10.56407/bs.agrarian/4.2024.85
28. [29] Kim, J., Chung, Y.S. (2021). A short review of RGB sensor applications for accessible high-throughput phenotyping. Journal of Crop Science and Biotechnology, 24(5), 495–499. https://doi.org/10.1007/s12892- 021-00104-6
29. [30] Mukhtar, S., Arbabi, A., Viegas, J. (2025). Advances in spectral imaging: A review of techniques and technologies. IEEE Access, 13, 35848–35902. https://doi.org/10.1109/ACCESS.2025.3544476
30. [31] Di Gennaro, S.F., Toscano, P., Gatti, M., Poni, S., Berton, A., Matese, A. (2022). Spectral comparison of UAV-based hyper and multispectral cameras for precision viticulture. Remote Sensing, 14(3), 449. https://doi.org/10.3390/rs14030449
31. [32] Albetis, J., Duthoit, S., Guttler, F., Jacquin, A., Goulard, M., Poilvé, H., Dedieu, G. (2017). Detection of Flavescence dorée grapevine disease using unmanned aerial vehicle (UAV) multispectral imagery. Remote Sensing, 9(4), 308. https://doi.org/10.3390/rs9040308
32. [33] Zhang, H., Zhang, B., Wei, Z., Wang, C., Huang, Q. (2020). Lightweight integrated solution for a UAV-borne hyperspectral imaging system. Remote Sensing, 12(4), 657. https://doi.org/10.3390/rs12040657
33. [34] Zarco-Tejada, P.J., Berjón, A., López-Lozano, R., Miller, J.R., Martín, P., Cachorro, V., De Frutos, A. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a rowstructured discontinuous canopy. Remote Sensing of Environment, 99(3), 271–287. 10.1016/j.rse.2005.09.002
34. [35] Sivakumar, P., Muthumanickam, D., Pazhanivelan, S., Jagadeeswaran, R., Kanniappan, G. (2025). Applications of thermal infrared remote sensing in agriculture. Plant Science Today, 12(3), 1–13. https://doi.org/10.14719/pst.9186
35. [36] Baluja, J., Diago, M.P., 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 Science, 30, 511–522. https://doi.org/10.1007/s00271-012-0382-9
36. [37] Santesteban, L.G., Di Gennaro, S.F., Herrero-Langreo, A., Miranda, C., Royo, J.B., Matese, A. (2017). Highresolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management, 183, 49–59. https://doi.org/10.1016/j.agwat.2016.08.026
37. [38] Sanz, R., Llorens, J., Escolà, A., Arnó, J., Planas, S., Román, C., Rosell-Polo, J.R. (2018). LiDAR and nonLiDAR-based canopy parameters to estimate the leaf area in fruit trees and vineyard. Agricultural and Forest Meteorology, 260, 229–239. https://doi.org/10.1016/j.agrformet.2018.06.017
38. [39] Sanz, R., Rosell, J.R., Llorens, J., Gil, E., Planas, S. (2013). Relationship between tree row LiDAR-volume and leaf area density for fruit orchards and vineyards obtained with a LiDAR 3D dynamic measurement system. Agricultural and Forest Meteorology, 171, 153–162. https://doi.org/10.1016/j.agrformet.2012.11.013
39. [40] Cheraïet, A., Taylor, J., Codis, S., Petitot, P., Bidaut, F., vd. (2023). Testing the suitability of a terrestrial 2D LiDAR scanner for 3D canopy characterisation of narrow vineyards to optimise the spraying process. Acta Horticulturae, 1360, 29–36. https://doi.org/10.17660/ActaHortic.2023.1360.4
40. [41] InfinitDrones. (2026). DJI Zenmuse L1. InfinitDrones. https://infinitdrones.com/products/dji-zenmuse-l1 (Erişim Tarihi: 14.06.2026).
41. [42] Gil, E., Escolà, A., Rosell, J.R., Planas, S., Val, L. (2007). Variable rate application of plant protection products in vineyard using ultrasonic sensors. Crop Protection, 26(8), 1287–1297. https://doi.org/10.1016/j.cropro.2006.11.003
42. [43] Sassu, A., Gambella, F., Ghiani, L., Mercenaro, L., Caria, M., Pazzona, A.L. (2021). Advances in unmanned aerial system remote sensing for precision viticulture. Sensors, 21(3), 956. https://doi.org/10.3390/s21030956
43. [44] Vera-Esmeraldas, A., Pizarro-Oteíza, S., Labbé, M., Rojo, F., Salazar, F. (2025). UAV-based spectral and thermal indices in precision viticulture: A review of NDVI, NDRE, SAVI, GNDVI, and CWSI. Agronomy, 15(11), 2569. https://doi.org/10.3390/agronomy15112569
44. [45] Vélez, S., Ariza-Sentís, M., Valente, J. (2024). Benchmarking the reliability of Sentinel-2 satellite data for estimating vineyard NDVI and leaf area index parameters through UAV LiDAR and multispectral imagery. Environmental Sciences Proceedings, 29(1), 1. https://doi.org/10.3390/ECRS2023-15859
45. [46] Judith, J., Tamilselvi, R., Beham, M.P., Lakshmi, S., Panthakkan, A., Mansoori, S.A., Ahmad, H.A. (2025). Remote sensing based crop health classification using NDVI and fully connected neural networks. arXiv, arXiv:2504.10522.
46. [47] Albetis, J., Jacquin, A., Goulard, M., Poilvé, H., Rousseau, J., Clenet, H., Dedieu, G., Duthoit, S. (2019). On the potentiality of UAV multispectral imagery to detect Flavescence dorée and grapevine trunk diseases. Remote Sensing, 11(1), 23. https://doi.org/10.3390/rs11010023
47. [48] Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298.
48. [49] Geilebagan, Tanaka, T., Gomi, T., Kotani, A., Nakaoki, G., Wang, X., Inokoshi, S. (2025). Evaluating forest canopy structures and leaf area index using a five-band depth image sensor. Forests, 16(8), 1294. https://doi.org/10.3390/f16081294
49. [50] Kamilaris, A., Prenafeta-Boldú, F.X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. 10.1016/j.compag.2018.02.016
50. [51] Ogungbuyi, M.G., Mohammed, C., Fischer, A.M., Turner, D., Whitehead, J., Harrison, M.T. (2024). Integration of drone and satellite imagery improves agricultural management agility. Remote Sensing, 16(24), 4688. https://doi.org/10.3390/rs16244688
51. [52] Li, Y., Yen, B., Hioka, Y. (2021). Performance evaluation on multi-channel Wiener filter based speech enhancement for unmanned aerial vehicles recordings. INTER-NOISE and NOISE-CON Congress and Conference Proceedings, 263(3), 3584–3594. https://doi.org/10.3397/IN-2021-2457
52. [53] Rani, A.A., Prasanna, K.L., Ashraf, M.S., Dey, A.K., Walid, M.A.A., Saikanth, D.R.K. (2023). Classification for crop pest on U-SegNet. İçinde: 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 926–932. https://doi.org/10.1109/ICCMC56507.2023.10083888
53. [54] Ouhami, M., Hafiane, A., Es-Saady, Y., El Hajji, M., Canals, R. (2021). Computer vision, IoT and data fusion for crop disease detection using machine learning: A survey and ongoing research. Remote Sensing, 13(13), 2486. https://doi.org/10.3390/rs13132486
54. [55] Rudolph, R., Herzog, K., Töpfer, R., Steinhage, V. (2019). Efficient identification, localization and quantification of grapevine inflorescences and flowers in unprepared field images using fully convolutional networks. Vitis, 58(3), 95–104. https://doi.org/10.5073/vitis.2019.58.95-104
55. [56] Sozzi, M., Cantalamessa, S., Cogato, A., Kayad, A., Marinello, F. (2022). Automatic bunch detection in white grape varieties using YOLOv3, YOLOv4, and YOLOv5 deep learning algorithms. Agronomy, 12(2), 319. https://doi.org/10.3390/agronomy12020319
56. [57] Ouhami, M., Es-Saady, Y., El Hajji, M., Canals, R., Hafiane, A. (2022). Meteorological data and UAV images for the detection and identification of grapevine disease using deep learning. İçinde: 2022 E-Health and Bioengineering Conference (EHB), Iași, Romania, 1–4. https://doi.org/10.1109/EHB55594.2022.9991443
57. [58] Laroche-Pinel, E., Vasquez, K.R., Brillante, L. (2024). Assessing grapevine water status in a variably irrigated vineyard with NIR/SWIR hyperspectral imaging from UAV. Precision Agriculture, 25, 2356–2374. https://doi.org/10.1007/s11119-024-10170-9
58. [59] Liu, Y., Yu, Q., Geng, S. (2024). Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO. Frontiers in Plant Science, 15, 1269423. https://doi.org/10.3389/fpls.2024.1269423
59. [60] Portela, F., Paredes, C.A., Morais, R., Sousa, J.J., Pádua, L. (2025). UAV RGB and multispectral data for grapevine viral disease symptoms classification. İçinde: IGARSS 2025 IEEE International Geoscience and Remote Sensing Symposium, 3689–3693. 10.1109/IGARSS55030.2025.11243164
60. [61] Kerkech, M., Hafiane, A., Canals, R. (2020). Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach. Computers and Electronics in Agriculture, 174, 105446. 10.1016/j.compag.2020.105446
61. [62] Longhi, V., Martino, A., Lingua, A.M., Maschio, P.F., Belcore, E. (2024). Monitoring the spread of a pathogenic insect on vineyards using UAS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 443–450. https://doi.org/10.5194/isprs-archives-XLVIII-1- 2024-443-2024
62. [63] Montalban-Faet, G., Pérez-Mateo, E., Fayos-Jordan, R., Benlloch-Caballero, P., Lada, A., Segura-Garcia, J., Garcia-Pineda, M. (2026). Direct UAV-based detection of Botrytis cinerea in vineyards using chlorophyllabsorption indices and YOLO deep learning. Sensors, 26(2), 374. https://doi.org/10.3390/s26020374
63. [64] Bálo, B., Szobonya, N., Vanek, B., Váradi, G., Bodor, P., Firtha, F., Koch, C. (2019). Using remotely sensed (UAV) and in situ field measurements to describe grapevine canopy characteristics. IVES Conference Series, GiESCO 2019.
64. [65] Ariza-Sentís, M., Vélez, S., Valente, J. (2023). Dataset on UAV RGB videos acquired over a vineyard including bunch labels for object detection and tracking. Data in Brief, 46, 108848. https://doi.org/10.1016/j.dib.2022.108848
65. [66] Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148. https://doi.org/10.1016/j.comnet.2020.107148
66. [67] Mogili, U.R., Deepak, B.B.V.L. (2018). Review on application of drone systems in precision agriculture. Procedia Computer Science, 133, 502–509. https://doi.org/10.1016/j.procs.2018.07.063
67. [68] Mahlein, A.K. (2016). Plant disease detection by imaging sensors: Parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease, 100(2), 241–251. https://doi.org/10.1094/PDIS03-15-0340-FE
68. [69] Colomina, I., Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
69. [70] Nhamo, L., Magidi, J., Nyamugama, A., Clulow, A.D., Sibanda, M., Chimonyo, V.G.P., Mabhaudhi, T. (2020). Prospects of improving agricultural and water productivity through unmanned aerial vehicles. Agriculture, 10(7), 256. https://doi.org/10.3390/agriculture10070256
70. [71] Ahmad, F., Qiu, B., Dong, X., Ma, J., Huang, X., Ahmed, S., Chandio, F.A. (2020). Effect of operational parameters of UAV sprayer on spray deposition pattern in target and off-target zones during outer field weed control application. Computers and Electronics in Agriculture, 172, 105350. https://doi.org/10.1016/j.compag.2020.105350