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Evaluation of the Most Appropriate UAV Model for Agricultural Activities with Entropy-Based Grey Relational Analysis Model

Yıl 2025, Cilt: 37 Sayı: 3, 340 - 355, 24.09.2025

Öz

Agricultural activities are important for the continuation of humanity and play a major role in ensuring sustainability in production. Thanks to technological developments, improvements and efficiency in agricultural activities and land studies have increased. Thanks to precision agriculture practices, more effective agricultural management strategies have been developed by using remote control and sensing systems and unmanned aerial vehicle (UAV) technologies. In this way, thanks to current technologies, both productivity has increased and traditional methods are overcome with the integration of satellite space technology in an economical and reliable way. In this study, due to the success of UAVs in many different fields, evaluating which UAV model is the most suitable for increasing the efficiency of agricultural activities is an important decision problem. Due to the complex nature of the problem, multiple criteria and alternatives, and uncertainty in the data, successful results are obtained by using decision making and grey relational analysis (GRA) approach. First, a political, economic, social and technological (PEST) analysis was conducted to assess the impacts of drone technology on the agricultural sector from a broader perspective. Then, since it is a multidimensional and strategically important decision problem, the ideal UAV model is investigated with the entropy-based grey relational analysis approach, which is a two-stage approach since it is expected to provide more reliable results. Accordingly, the A1 model emerged as the most suitable UAV type, followed by the A6, A4, A2, A3 and A5 models. The results obtained from this study are expected to help researchers and those who take an active role in agricultural activities with the analytical approach it offers.

Etik Beyan

No conflict of interest

Kaynakça

  • Özgüven, D. M., & Altaş, Z. (2022). Tarımda drone kullanımı ve geleceği. Ordu Üniversitesi Bilim ve Teknoloji Dergisi, 12(1), 64–83. https://doi.org/10.54370/ORDUBTD.1097519
  • Kutlu, G., Avaroğlu, E., & Yazar, S. (2024). İHA’ların batarya seviyelerinin makine öğrenmesi ile tahmini. Türkiye İnsansız Hava Araçları Dergisi, 6(2), 56–62. https://doi.org/10.51534/TIHA.1437254
  • Gupta, A., Afrin, T., Scully, E., & Yodo, N. (2021). Advances of UAVs toward future transportation: The state-of-the-art, challenges, and opportunities. Future Transportation, 1(2), 326–350. https://doi.org/10.3390/futuretransp1020019
  • AUAV. (2016). İHA türleri: Çok rotorlu, sabit kanatlı, tek rotorlu, hibrit VTOL. Retrieved June 11, 2025, from https://www.auav.com.au/articles/drone-types/
  • Liu, Z., & Li, J. (2023). Application of unmanned aerial vehicles in precision agriculture. Agriculture, 13(7), 1375. https://doi.org/10.3390/agriculture13071375
  • Ritchie, H., & Roser, M. (2018). Water use and stress. Our World in Data. https://doi.org/10.4060/CB6241EN
  • Abbas, A., et al. (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
  • Akkamış, M., & Çalışkan, S. (2020). İnsansız hava araçları ve tarımsal uygulamalarda kullanımı. Türkiye İnsansız Hava Araçları Dergisi, 2(1), 8–16. https://dergipark.org.tr/tr/pub/tiha/issue/54200/707831
  • Wang, L., et al. (2022). Progress in agricultural unmanned aerial vehicles (UAVs) applied in China and prospects for Poland. Agriculture, 12(3), 397. https://doi.org/10.3390/agriculture12030397
  • Narzari, R., Burhan, U., Choudhury, G., Singhal, K., & Choudhary, K. K. (2025). A critical review of how UAVs can transform precision agriculture in the realm of agroecology. Discover Soil, 2(1), 1–26. https://doi.org/10.1007/s44378-025-00055-2
  • Inoue, Y. (2020). Satellite- and drone-based remote sensing of crops and soils for smart farming – A review. Soil Science and Plant Nutrition, 66(6), 798–810. https://doi.org/10.1080/00380768.2020.1738899
  • Gago, J., et al. (2015). UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153, 9–19. https://doi.org/10.1016/j.agwat.2015.01.020
  • Kalischuk, M., et al. (2019). An improved crop scouting technique incorporating unmanned aerial vehicle-assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon. Plant Disease, 103(7), 1642–1650. https://doi.org/10.1094/PDIS-08-18-1373-RE
  • Maimaitijiang, M., et al. (2017). Unmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 43–58. https://doi.org/10.1016/j.isprsjprs.2017.10.011
  • López-Granados, F., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A. I., Mesas-Carrascosa, F. J., & Peña, J. M. (2016). Early season weed mapping in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds. Precision Agriculture, 17(2), 183–199. https://doi.org/10.1007/s11119-015-9415-8
  • Melville, B., Lucieer, A., & Aryal, J. (2019). Classification of lowland native grassland communities using hyperspectral unmanned aircraft system (UAS) imagery in the Tasmanian Midlands. Drones, 3(1), 5. https://doi.org/10.3390/drones3010005
  • Moharana, S., & Dutta, S. (2016). Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 17–29. https://doi.org/10.1016/j.isprsjprs.2016.09.002
  • Deng, L., Mao, Z., Li, X., Hu, Z., Duan, F., & Yan, Y. (2018). UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 124–136. https://doi.org/10.1016/j.isprsjprs.2018.09.008
  • Guan, S., et al. (2019). Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing, 11(2), 112. https://doi.org/10.3390/rs11020112
  • Fawcett, D., et al. (2020). Multi-scale evaluation of drone-based multispectral surface reflectance and vegetation indices in operational conditions. Remote Sensing, 12(3), 514. https://doi.org/10.3390/rs12030514
  • 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
  • Su, J., Coombes, M., Liu, C., Guo, L., & Chen, W. H. (2018). Wheat drought assessment by remote sensing imagery using unmanned aerial vehicle. In 2018 Chinese Control Conference (CCC) (pp. 10340–10344). IEEE. https://doi.org/10.23919/CHICC.2018.8484005
  • Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., & Bareth, G. (2014). Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6(11), 10395–10412. https://doi.org/10.3390/rs61110395
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  • Gašparović, M., Zrinjski, M., Barković, Đ., & Radočaj, D. (2020). An automatic method for weed mapping in oat fields based on UAV imagery. Computers and Electronics in Agriculture, 173, 105385. https://doi.org/10.1016/j.compag.2020.105385
  • Kulbacki, M., et al. (2018). Survey of drones for agriculture automation from planting to harvest. In 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES) (pp. 353–358). IEEE. https://doi.org/10.1109/INES.2018.8523943
  • 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
  • Puri, V., Nayyar, A., & Raja, L. (2017). Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems, 20(4), 507–518. https://doi.org/10.1080/09720510.2017.1395171
  • Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349. https://doi.org/10.3390/info10110349
  • Al-Thani, N., Albuainain, A., Alnaimi, F., & Zorba, N. (2020). Drones for sheep livestock monitoring. In 2020 20th IEEE Mediterranean Electrotechnical Conference (MELECON) (pp. 672–676). IEEE. https://doi.org/10.1109/MELECON48756.2020.9140588
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Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi

Yıl 2025, Cilt: 37 Sayı: 3, 340 - 355, 24.09.2025

Öz

Tarım faaliyetleri insanlığın devamı için önemli olup üretimde sürdürülebilirliğin sağlanmasında büyük rol oynamaktadır. Teknolojik gelişmeler sayesinde tarım faaliyetlerinde ve arazi çalışmalarında iyileşme ve verimlilik artmıştır. Hassas tarım uygulamaları sayesinde uzaktan kontrol ve algılama sistemleri ile insansız hava araç (İHA) teknolojileri kullanılarak daha etkili tarım yönetim stratejileri geliştirilmiştir. Güncel teknolojiler sayesinde hem verimlilik artmış hem de ekonomik ve güvenilir biçimde uydu uzay teknoloji entegrasyonu ile geleneksel yöntemlerin dışına çıkılmaktadır. Bu çalışmada, İHA’ların çok farklı alanlardaki başarısı nedeniyle tarımsal faaliyetlerdeki etkinliğin artırılması nedeniyle hangi İHA modelinin en uygun olduğunun değerlendirilmesi önemli bir karar problemidir. Problemin karmaşık yapısı, birden fazla kriter ve alternatifi barındırması, verilerdeki belirsizlik nedeniyle karar verme ve gri ilişkisel analiz (GİA) yaklaşımı kullanılarak başarılı sonuçlar elde edilmektedir. Öncelikle İHA teknolojisinin tarım sektörü üzerindeki etkilerini daha geniş bir perspektiften değerlendirmek için PEST (politik, ekonomik, sosyal ve teknolojik) analizi yapılmıştır. Ardından çok boyutlu ve stratejik önemde bir karar problemi olması nedeniyle daha güvenilir sonuç sunması beklendiği için iki aşamalı bir yaklaşım olan entropi tabanlı gri ilişkisel analiz yaklaşımı ile ideal İHA modeli araştırılmaktadır. Buna göre A1 modeli en uygun İHA tipi olarak ortaya çıkmış bunu A6, A4, A2, A3 ve A5 modelleri izlemektedir. Bu çalışmadan elde edilen sonuçların araştırmacılara ve tarım faaliyetlerinde aktif rol alanlara, sunduğu analitik yaklaşımıyla yardımcı olması beklenmektedir.

Etik Beyan

Bir çıkar çatışması yoktur

Kaynakça

  • Özgüven, D. M., & Altaş, Z. (2022). Tarımda drone kullanımı ve geleceği. Ordu Üniversitesi Bilim ve Teknoloji Dergisi, 12(1), 64–83. https://doi.org/10.54370/ORDUBTD.1097519
  • Kutlu, G., Avaroğlu, E., & Yazar, S. (2024). İHA’ların batarya seviyelerinin makine öğrenmesi ile tahmini. Türkiye İnsansız Hava Araçları Dergisi, 6(2), 56–62. https://doi.org/10.51534/TIHA.1437254
  • Gupta, A., Afrin, T., Scully, E., & Yodo, N. (2021). Advances of UAVs toward future transportation: The state-of-the-art, challenges, and opportunities. Future Transportation, 1(2), 326–350. https://doi.org/10.3390/futuretransp1020019
  • AUAV. (2016). İHA türleri: Çok rotorlu, sabit kanatlı, tek rotorlu, hibrit VTOL. Retrieved June 11, 2025, from https://www.auav.com.au/articles/drone-types/
  • Liu, Z., & Li, J. (2023). Application of unmanned aerial vehicles in precision agriculture. Agriculture, 13(7), 1375. https://doi.org/10.3390/agriculture13071375
  • Ritchie, H., & Roser, M. (2018). Water use and stress. Our World in Data. https://doi.org/10.4060/CB6241EN
  • Abbas, A., et al. (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
  • Akkamış, M., & Çalışkan, S. (2020). İnsansız hava araçları ve tarımsal uygulamalarda kullanımı. Türkiye İnsansız Hava Araçları Dergisi, 2(1), 8–16. https://dergipark.org.tr/tr/pub/tiha/issue/54200/707831
  • Wang, L., et al. (2022). Progress in agricultural unmanned aerial vehicles (UAVs) applied in China and prospects for Poland. Agriculture, 12(3), 397. https://doi.org/10.3390/agriculture12030397
  • Narzari, R., Burhan, U., Choudhury, G., Singhal, K., & Choudhary, K. K. (2025). A critical review of how UAVs can transform precision agriculture in the realm of agroecology. Discover Soil, 2(1), 1–26. https://doi.org/10.1007/s44378-025-00055-2
  • Inoue, Y. (2020). Satellite- and drone-based remote sensing of crops and soils for smart farming – A review. Soil Science and Plant Nutrition, 66(6), 798–810. https://doi.org/10.1080/00380768.2020.1738899
  • Gago, J., et al. (2015). UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153, 9–19. https://doi.org/10.1016/j.agwat.2015.01.020
  • Kalischuk, M., et al. (2019). An improved crop scouting technique incorporating unmanned aerial vehicle-assisted multispectral crop imaging into conventional scouting practice for gummy stem blight in watermelon. Plant Disease, 103(7), 1642–1650. https://doi.org/10.1094/PDIS-08-18-1373-RE
  • Maimaitijiang, M., et al. (2017). Unmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 43–58. https://doi.org/10.1016/j.isprsjprs.2017.10.011
  • López-Granados, F., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A. I., Mesas-Carrascosa, F. J., & Peña, J. M. (2016). Early season weed mapping in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds. Precision Agriculture, 17(2), 183–199. https://doi.org/10.1007/s11119-015-9415-8
  • Melville, B., Lucieer, A., & Aryal, J. (2019). Classification of lowland native grassland communities using hyperspectral unmanned aircraft system (UAS) imagery in the Tasmanian Midlands. Drones, 3(1), 5. https://doi.org/10.3390/drones3010005
  • Moharana, S., & Dutta, S. (2016). Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 17–29. https://doi.org/10.1016/j.isprsjprs.2016.09.002
  • Deng, L., Mao, Z., Li, X., Hu, Z., Duan, F., & Yan, Y. (2018). UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 124–136. https://doi.org/10.1016/j.isprsjprs.2018.09.008
  • Guan, S., et al. (2019). Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing, 11(2), 112. https://doi.org/10.3390/rs11020112
  • Fawcett, D., et al. (2020). Multi-scale evaluation of drone-based multispectral surface reflectance and vegetation indices in operational conditions. Remote Sensing, 12(3), 514. https://doi.org/10.3390/rs12030514
  • 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
  • Su, J., Coombes, M., Liu, C., Guo, L., & Chen, W. H. (2018). Wheat drought assessment by remote sensing imagery using unmanned aerial vehicle. In 2018 Chinese Control Conference (CCC) (pp. 10340–10344). IEEE. https://doi.org/10.23919/CHICC.2018.8484005
  • Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., & Bareth, G. (2014). Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6(11), 10395–10412. https://doi.org/10.3390/rs61110395
  • Negash, L., Kim, H. Y., & Choi, H. L. (2019). Emerging UAV applications in agriculture. In 2019 7th International Conference on Robot Intelligence Technology and Applications (RiTA) (pp. 254–257). IEEE. https://doi.org/10.1109/RITAPP.2019.8932853
  • Gašparović, M., Zrinjski, M., Barković, Đ., & Radočaj, D. (2020). An automatic method for weed mapping in oat fields based on UAV imagery. Computers and Electronics in Agriculture, 173, 105385. https://doi.org/10.1016/j.compag.2020.105385
  • Kulbacki, M., et al. (2018). Survey of drones for agriculture automation from planting to harvest. In 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES) (pp. 353–358). IEEE. https://doi.org/10.1109/INES.2018.8523943
  • 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
  • Puri, V., Nayyar, A., & Raja, L. (2017). Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems, 20(4), 507–518. https://doi.org/10.1080/09720510.2017.1395171
  • Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), 349. https://doi.org/10.3390/info10110349
  • Al-Thani, N., Albuainain, A., Alnaimi, F., & Zorba, N. (2020). Drones for sheep livestock monitoring. In 2020 20th IEEE Mediterranean Electrotechnical Conference (MELECON) (pp. 672–676). IEEE. https://doi.org/10.1109/MELECON48756.2020.9140588
  • Dutta, P. K., & Mitra, S. (2021). Application of agricultural drones and IoT to understand food supply chain during post COVID-19. In Agricultural Informatics Automation Using IoT and Machine Learning (pp. 67–87). Wiley. https://doi.org/10.1002/9781119769231.ch4
  • Nayyar, A., Nguyen, B. L., & Nguyen, N. G. (2020). The internet of drone things (IoDT): Future envision of smart drones. In Advances in Intelligent Systems and Computing (Vol. 1045, pp. 563–580). Springer. https://doi.org/10.1007/978-981-15-0029-9_45
  • Suomalainen, J., et al. (2013). A light-weight hyperspectral mapping system for unmanned aerial vehicles – The first results. In 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE. https://doi.org/10.1109/WHISPERS.2013.8080721
  • Aslan, M. F., Durdu, A., Sabanci, K., Ropelewska, E., & Gültekin, S. S. (2022). A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses. Applied Sciences, 12(3), 1047. https://doi.org/10.3390/app12031047
  • Diaz-Gonzalez, F. A., Vuelvas, J., Correa, C. A., Vallejo, V. E., & Patino, D. (2022). Machine learning and remote sensing techniques applied to estimate soil indicators – Review. Ecological Indicators, 135, 108517. https://doi.org/10.1016/j.ecolind.2021.108517
  • Awais, M., et al. (2022). UAV-based remote sensing in plant stress imaging using high-resolution thermal sensor for digital agriculture practices: A meta-review. International Journal of Environmental Science and Technology, 20(1), 1135–1152. https://doi.org/10.1007/s13762-021-03801-5
  • Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F., & Pugliese, C. (2022). Review: Precision livestock farming technologies in pasture-based livestock systems. Animal, 16(1), 100429. https://doi.org/10.1016/j.animal.2021.100429
  • Marin, D. B., Ferraz, G. A. S., Schwerz, F., Barata, R. A. P., de Oliveira Faria, R., & Dias, J. E. L. (2021). Unmanned aerial vehicle to evaluate frost damage in coffee plants. Precision Agriculture, 22(6), 1845–1860. https://doi.org/10.1007/s11119-021-09815-w
  • Vayssade, J. A., Arquet, R., & Bonneau, M. (2019). Automatic activity tracking of goats using drone camera. Computers and Electronics in Agriculture, 162, 767–772. https://doi.org/10.1016/j.compag.2019.05.021
  • Xu, R., Li, C., & Paterson, A. (2017). Cotton flower detection using aerial color images. In 2017 ASABE Annual International Meeting. ASABE. https://doi.org/10.13031/aim.201701080
  • Lu, N., Zhou, J., Han, Z., Li, D., Cao, Q., Yao, X., Tian, Y., Zhu, Y., & Cao, W. (2019). Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low‐cost unmanned aerial vehicle system. Plant Methods, 15, 17. https://doi.org/10.1186/s13007-019-0391-9
  • Zhou, X., Zheng, H. B., Xu, X. Q., He, J. Y., Ge, X. K., Yao, X., Cheng, T., Zhu, Y., Cao, W. X., & Tian, Y. C. (2017). Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 246–255. https://doi.org/10.1016/j.isprsjprs.2017.05.003
  • Yue, J., Feng, H., Jin, X., Yuan, H., Li, Z., Zhou, C., Yang, G., Tian, Q., & Feng, H. (2019). A comparison of crop parameters estimation using images from UAV-mounted snapshot hyperspectral sensor and high-definition digital camera. Remote Sensing, 11(2), 166. https://doi.org/10.3390/rs11020166
  • Gao, F., Jin, Y., & Masek, J. G. (2009). Calibration and validation of BRDF and albedo products derived from MODIS data for crop and pasture sites in the USA. Agricultural and Forest Meteorology, 149(5), 672–686. https://doi.org/10.1016/j.agrformet.2008.10.015
  • Maes, W. H., & Steppe, K. (2019). Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science, 24(2), 152–164. https://doi.org/10.1016/j.tplants.2018.11.007
  • Wan, L., Li, Y., Cen, H., Zhu, J., Yin, W., Wu, W., Wu, C., & Zhu, H. (2018). Combining UAV-based vegetation indices and image classification to estimate wheat grain yield. International Journal of Remote Sensing, 39(21), 6693–6717. https://doi.org/10.1080/01431161.2018.1466089
  • Li, W., Niu, Z., Chen, H., Li, D., Wu, M., & Zhao, W. (2016). Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecological Indicators, 67, 637–648. https://doi.org/10.1016/j.ecolind.2016.03.036
  • Cen, H., Wan, L., Zhu, J., Li, Y., Li, X., Zhu, Y., & Weng, H. (2019). Dynamic monitoring of biomass of rice under different nitrogen treatments using UAV-based multispectral imagery. Frontiers in Plant Science, 10, 1176. https://doi.org/10.3389/fpls.2019.01176
  • Duan, T., Zheng, B., Guo, W., Ninomiya, S., Guo, Y., & Chapman, S. C. (2017). Comparison of ground cover estimates from experiment plots in cotton, sorghum, and sugarcane based on images and ortho-mosaics captured by UAV. Functional Plant Biology, 44(1), 169–183. https://doi.org/10.1071/FP16123
  • Bareth, G., & Schellberg, J. (2018). Replacing manual rising plate meter measurements with UAV-based biomass monitoring in grassland for high-throughput applications. Computers and Electronics in Agriculture, 150, 226–238. https://doi.org/10.1016/j.compag.2018.04.007
  • Khaliq, A., Comba, L., Biglia, A., Ricauda Aimonino, D., Chiaberge, M., & Gay, P. (2019). Comparison of satellite and UAV-based multispectral imagery for vineyard variability assessment. Remote Sensing, 11(4), 436. https://doi.org/10.3390/rs11040436
  • Vibhute, A., & Bodhe, S. K. (2012). Applications of image processing in agriculture: A survey. International Journal of Computer Applications, 52(2), 34–40. https://doi.org/10.5120/8176-1295
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Çok Ölçütlü Karar Verme, Endüstri Mühendisliği, Üretim ve Hizmet Sistemleri
Bölüm Araştırma Makaleleri
Yazarlar

Beyza Çayır Ervural 0000-0002-0861-052X

Erken Görünüm Tarihi 15 Eylül 2025
Yayımlanma Tarihi 24 Eylül 2025
Gönderilme Tarihi 18 Mart 2025
Kabul Tarihi 15 Ağustos 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 37 Sayı: 3

Kaynak Göster

APA Çayır Ervural, B. (2025). Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi. International Journal of Advances in Engineering and Pure Sciences, 37(3), 340-355.
AMA Çayır Ervural B. Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi. JEPS. Eylül 2025;37(3):340-355.
Chicago Çayır Ervural, Beyza. “Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi”. International Journal of Advances in Engineering and Pure Sciences 37, sy. 3 (Eylül 2025): 340-55.
EndNote Çayır Ervural B (01 Eylül 2025) Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi. International Journal of Advances in Engineering and Pure Sciences 37 3 340–355.
IEEE B. Çayır Ervural, “Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi”, JEPS, c. 37, sy. 3, ss. 340–355, 2025.
ISNAD Çayır Ervural, Beyza. “Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi”. International Journal of Advances in Engineering and Pure Sciences 37/3 (Eylül2025), 340-355.
JAMA Çayır Ervural B. Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi. JEPS. 2025;37:340–355.
MLA Çayır Ervural, Beyza. “Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi”. International Journal of Advances in Engineering and Pure Sciences, c. 37, sy. 3, 2025, ss. 340-55.
Vancouver Çayır Ervural B. Entropi Tabanlı Gri İlişkisel Analiz Modeliyle En Uygun İHA Modelinin Tarım Faaliyetleri İçin Değerlendirilmesi. JEPS. 2025;37(3):340-55.