Tarımda Drone Kullanımı ve Geleceği

Yıl 2022, Cilt: 12 Sayı: 1, 64 - 83, 30.06.2022

Mehmet Metin Özgüven , Ziya Altaş , Derya Güven , Arif Çam

https://doi.org/10.54370/ordubtd.1097519

Cited By: 2

Öz

Tarım, yaşamın sürdürebilmesi için hayati bir faaliyet alanı olmakla birlikte, tarım dışı diğer sektörlere hammadde sağlaması, milli gelir ve istihdama katkısı nedeniyle de stratejik bir faaliyet alanıdır. Teknolojinin hızla gelişmesiyle ortaya çıkan yeni teknikler veya cihazların tarımda kullanılması tarımsal uygulamaların daha kolay ve etkin yapılabilmesini sağlamaktadır. Son yıllardaki en popüler teknolojik gelişmelerden biri olan drone’ların tarımda kullanımı yaygınlaşmakta ve yeni uygulama alanlarının da eklenmesiyle daha da popüler hale gelmektedir. Drone’ların popüler olması ve tarımda kullanımı, tarım dışı farklı disiplinlerden olanların da ilgisini çekmektedir. Farklı disiplinlerde olanların tarım konusundaki bazı teknik bilgilerinin yetersiz olmasından dolayı, drone’un tarımda kullanımı ile ilgili yanlış bilgiler veya efektif olmayan kullanımlar da oluşabilmektedir. Bu çalışmada, drone ve bileşenleri, drone’un avantaj ve dezavantajları, drone ile kullanılabilen kamera ve sensörler hakkında bilgiler verilmiştir. Daha sonra günümüzde tarımda drone kullanım alanları örnek uygulamalar ile açıklanmış ve gelecekte tarımda drone kullanımı ile öngörüler sunulmuştur. Ayrıca drone’un tarımda kullanımı ile bazı yanlış bilgiler ve efektif olmayan kullanımlar hakkında açıklamalar yapılmıştır.

Anahtar Kelimeler

tarım, drone, multispektral kamera, hiperspektral kamera, termal kamera

Use of Drones in Agriculture and Its Future

Öz

Agriculture is both a vital sector of activity for the sustainability of life and strategic field of activity for provides raw materials to non-agricultural sectors and contributes to national income and employment. The use of new techniques or devices in agriculture, which emerged with the rapid development of technology, makes agricultural applications easier and more effective. The use of drones in agriculture, which is one of the most popular technological developments in recent years, has become widespread and its use is increasing even more with the addition of new application areas. The popularity of drones and their use in agriculture also attract the attention of those from different disciplines other than agriculture. Due to the insufficient technical knowledge of those in different disciplines on agriculture, false information or ineffective use of drones in agriculture may occur. In this study, information is given about the drone and its components, the advantages and disadvantages of the drone, the cameras and sensors that can be used with the drone. Then, the use of drones in agriculture today is explained with sample applications and predictions are presented with the use of drones in agriculture in the future. In addition, explanations were made about the use of drones in agriculture, some misinformation and ineffective use.

Anahtar Kelimeler

agriculture, drone, multispectral camera, hyperspectral camera, thermal camera

Kaynakça

• Allred, B., Martinez, L., Fessehazion, M. K., Rouse, G., Williamson, T. N., Wishart, D., Koganti, T., Freeland, R., Eash, N., Batschelet, A. ve Featheringill, R. (2020). Overall results and key findings on the use of uav visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes. Agricultural Water Management, 232, 106036. https://doi.org/10.1016/j.agwat.2020.106036
• Altas, Z., Ozguven, M. M. ve Yanar, Y. (2018). Determination of sugar beet leaf spot disease level (cercospora beticola sacc.) with image processing technique by using drone. Current Investigations In Agriculture and Current Research, 5(3), 621-631. https://doi.org/10.32474/CIACR.2018.05.000214
• Altaş, Z., Özgüven, M. M. ve Yanar, Y. (2019, Nisan, 24-27). Bitki hastalık ve zararlı düzeylerinin belirlenmesinde görüntü işleme tekniklerinin kullanımı: Şeker pancarı yaprak leke hastalığı örneği [Sözlü sunum]. International Erciyes Agriculture Animal & Food Sciences Conference, Kayseri, Turkiye.
• Andrew, W., Greatwood, C. ve Burghardt, T. (2020). Fusing animal biometrics with autonomous robotics: Drone-based search and ındividual id of friesian cattle. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 38-43. https://openaccess.thecvf.com/content_WACVW_2020/papers/w2/Andrew_Fusing_Animal_Biometrics_with_Autonomous_Robotics_Drone-based_Search_and_Individual_WACVW_2020_paper.pdf
• Apolo-Apolo, O. E., Martínez-Guanter, J., Egea, G., Raja, P. ve Pérez-Ruiz M. (2020). Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. European Journal of Agronomy, 115, 126030. https://doi.org/10.1016/j.eja.2020.126030
• Babu, S. J., Shyam, M., Sivakumar, A., Vignesh, R. S. ve Yogapriya J. (2020). Ergonomic heavy-lift pesticide dispeller drone instilled with an intelligent atomizer to achieve optimal spray and improved pest control. European Journal of Molecular & Clinical Medicine, 7 (4). https://www.ejmcm.com/article_1825_555a87f707d98b4aa087cc961699a2a2.pdf
• Behmann, J., Acebron, K., Emin, D., Bennertz, S., Matsubara, S., Matsubara, S., Bohnenkamp, D., Kuska, M. T., Jussila, J., Salo, H., Mahlein. A. ve Rascher, U. (2018). Specim iq: Evaluation of a new, miniaturized handheld hyperspectral camera and ıts application for plant phenotyping and disease detection. Sensors 18, 441. https://doi.org/10.3390/s18020441
• Böhler, J. E., Schaepman, M. E. ve Kneubühler, M. (2020). Crop separability from individual and combined airborne imaging spectroscopy and uav multispectral data. Remote Sensing, 12(8), 1256. https://doi.org/10.3390/rs12081256
• Buters, T. M., Belton, D. ve Cross, A. T. (2019). Multi-sensor uav tracking of ındividual seedlings and seedling communities at millimetre accuracy. Drones, 3 (4), 81. https://doi.org/10.3390/drones3040081
• Chen, C. J., Huang, Y. Y., Lu, Y. S., Chen, Y. C., Chang, C. Y. ve Huang, Y.M. (2021). Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access, 9, 21986 - 21997. https://doi.org/10.1109/ACCESS.2021.3056082
• Çetinsoy, E., Sırımoğlu, E., Öner, K. T., Ayken, T., Hançer, C., Ünel, M., Akşit, M. F., Kandemir, İ. ve Gülez, K. (2009). Yeni bir insansız hava aracının (suavi) prototip üretimi ve algılayıcı-eyleyici entegrasyonu. Otomatik Kontrol Ulusal Toplantısı 2009 (TOK'09), İstanbul, Türkiye. https://research.sabanciuniv.edu/id/eprint/12663/1/88.pdf
• Dantas, R. A. S., Neto, M. V. G., Zyrianoff, I. D. ve Kamienski, C. A. (2020). The swamp farmer app for IoT-based smart water status monitoring and ırrigation control. 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 20258013. https://doi.org/10.1109/MetroAgriFor50201.2020.9277588
• D’Odorico, P., Besik, A., Wong, C. Y. S., Isabel, N. ve Ensminger, I. (2020). High-throughput drone-based remote sensing reliably tracks phenology in thousands of conifer seedlings. New Phytologist 226, 1667–1681. https://doi.org/10.1111/nph.16488
• Etigowni, S., Hossain-McKenzie, S., Kazerooni, M., Davis, K. Ve Zonouz, S. (2018). Crystal (ball): I look at physics and predict control flow! just-ahead-of-time controller recovery. Proceedings of the 34th Annual Computer Security Applications Conference, 553–565. https://doi.org/10.1145/3274694.3274724
• Fawcett, D., Bennie, J. ve Anderson, K. (2020). Monitoring spring phenology of individual tree crowns using drone-acquired ndvı data. Remote Sensing in Ecology and Conservation, 7(2), 227-244. https://doi.org/10.1002/rse2.184
• Gago, J., Douthe, C., Coopman, R. E., Gallego, P. P., Ribas-Carbo, M., Flexas, J., Escalona, J. ve Medrano, H. (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
• Garre, P. ve Harish, A. (2018). Autonomous agricultural pesticide spraying uav. Materials Science and Engineering, 455, 012030. https://doi.org/10.1088/1757-899X/455/1/012030
• Gašparović, M., Zrinjski, M., Barković, D. ve 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
• İnan, M. ve Karcı, A. (2021). Tarımda ağaç ilaçlamanın drone’larla yapılmasında yeni bir yöntemin geliştirilmesi ve uygulanması. Computer Science, 6(2), 72-89. https://dergipark.org.tr/en/pub/bbd/issue/62530/928229
• Jensen, J. R. (2006). Remote sensing of environment: An earth ressource perspective. Pearson.
• Jin, H., Köppl, C. J., Fischer, B. M. C., Rojas-Conejo, J., Johnson, M. S., Morillas, L., Lyon, S. W., Durán-Quesada, A. M., Suárez-Serrano, A., Manzoni, S. ve Garcia, M. (2021). Drone-based hyperspectral and thermal imagery for quantifying upland rice productivity and water use efficiency after biochar application. Remote Sensing, 13(20), 1866. https://doi.org/10.3390/rs13101866
• Kerkech, M., Hafiane, A. ve 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. https://doi.org/10.1016/j.compag.2020.105446
• Kılıçoğlu, A. ve Şengün, Y. S. (2007). İnterferometrik yapay açıklıklı radar tekniğinin jeodezide kullanılması. Harita Dergisi, 72(133), 53-62. https://www.acarindex.com/harita-dergisi/interferometrik-yapay-aciklikli-radar-tekniginin-jeodezide-kullanilmasi-239529
• Kitpo, N. ve Inoue, M. (2018). Early rice disease detection and position mapping system using drone and ıot architecture. 2018 12th South East Asian Technical University Consortium (SEATUC), 18888153. https://doi.org/10.1109/SEATUC.2018.8788863
• Li, X. ve Xing, L. (2019). Reactive deployment of autonomous drones for livestock monitoring based on density-based clustering. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 19283603. https://doi.org/10.1109/ROBIO49542.2019.8961763
• Li, H., Li, G., Ye, Y. ve Lin, L. (2021). A high-efficiency acquisition method of led-multispectral images based on frequency-division modulation and rgb camera. Optics Communications, 480, 126492. https://doi.org/10.1016/j.optcom.2020.126492
• Lopez, J. J. ve Pázmány, M. M. (2019). Drones for conservation in protected areas: Present and future. Drones, 3(1), 10. https://doi.org/10.3390/drones3010010
• Maimaitijiang, M., Sagan, V., Sidike, P., Daloye, A. M., Erkbol, H. ve Fritschi, F. B. (2020). Crop monitoring using satellite/uav data fusion and machine learning. Remote Sensing, 12, 1357. https://doi.org/10.3390/rs12091357
• Maddikunta, P. K. R., Hakak, S., Alazab, M., Member. S., Bhattacharya, S., Gadekallu, T. R., Khan, W. Z. ve Pham, Q. (2021). Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges. IEEE Sensors Journal, 21(16), 17608-17619. https://doi.org/10.1109/JSEN.2021.3049471
• Matsuura, Y., Heming, Z., Kawai, S. ve Nobuhara, H. (2020). High-precision/throughput growth measurement of crops by drone with stereo matching based on rtk-gnss and single camera. 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), 20300745. https://doi.org/10.1109/GCCE50665.2020.9292033
• Mattivi, P., Pappalardo, S. E., Nikolic, N., Mandolesi, L., Persichetti, A., Marchi, M. D. ve Masin, R. (2021). Can commercial low-cost drones and open-source gıs technologies be suitable for semi-automaticweed mapping for smart farming? A case study in ne Italy. Remote Sensing, 13(10), 1869 https://doi.org/10.3390/rs13101869
• Meivel, S., Maguteeswaran, R., Gandhiraj, N. ve Srinivasan, G. (2016). Quadcopter uav based fertilizer and pesticide spraying system. International Academic Research Journal of Engineering Sciences. 1(1),8-12. http://acrpub.com/article/publishedarticles/24102016IARJES343.pdf
• Messina, G. ve Modica, G. (2020). Applications of uav thermal ımagery in precision agriculture: state of the art and future research outlook. Remote Sensing, 12, 1491. https://doi.org/10.3390/rs12091491
• Mihalache, D. B., Vanghele, N. A., Petre A.A. ve Matache, A. (2021). The use of drones in modern agriculture. Annals of the University of Craiova-Agriculture, Montanology, Cadastre Series, 50 (2), 349-354. https://anale.agro-craiova.ro/index.php/aamc/article/view/1133/1065
• Milics, G. (2019). Application of uavs in precision agriculture. Palocz-Andresen, M., Szalay, D., Gosztom, A., Sípos, L., Taligás, T. (Ed.) International Climate Protection (s. 93-97) içinde. Springer. https://doi.org/10.1007/978-3-030-03816-8_13
• Moreira, L., Castro, F., Góes, J. A., Bins, L., Teruel, B., Fracarolli, J., Castro, V., Alcântara, M., Oré G, Luebeck, D., Oliveira, L, P., Gabrielli, L. ve Hernandez-Figueroa, H. E. (2019). A drone-borne multiband dınsar: results and applications. 2019 IEEE Radar Conference (RadarConf). 1,6. https://doi.org/10.1109/RADAR.2019.8835653
• Neumann, C., Behling, R., Schindhelm, A., Itzerott, S., Weiss, G., Wichmann, M. ve Muller, J. (2020). The colors of heath flowering-quantifying spatial patterns of phenology in calluna life-cycle phases using high-resolution drone imagery. Remote Sensing in Ecology and Conservation, 6(1), 35–51. https://doi.org/10.1002/rse2.121
• Ore, G., Alcântara, M. S., Góes, J. A., Oliveira, L. P., Yepes, J., Teruel, B., Castro, V., Bins, L. S., Castro, F., Luebeck, D., Moreira, L. F., Gabrielli, L. H. ve Hernandez-Figueroa, H. E. (2020). Crop growth monitoring with drone-borne dınsar. Remote Sensing, 12, 615. https://doi.org/10.3390/rs12040615
• Ozguven, M. M. (2018). The newest agricultural technologies. Current Investigations in Agriculture and Current Research, 5(1), 573-580. https://doi.org/10.32474/CIACR.2018.05.000201
• Özgüven, M. M. (2018). Hassas tarım. Akfon Yayınları.
• Özgüven, M. M. (2020). Tarımda dijital dönüşüm ve akıllı makineler. Yeni Türkiye Dergisi, Tarım Politikaları Özel Sayısı, 114(2), 105-132
• Özgüven, M. M., Türker, U., Akdemir, B., Çolak, A., Acar, A. İ., Öztürk, R. ve Eminoğlu, M. B. (2020). Tarımda dijital çağ. Türkiye Ziraat Mühendisliği IX. Teknik Kongresi, 55-74. http://www.sonerkazaz.com/wp-content/uploads/1_Dunyada-ve-Turkiyede-Sus-Bitkileri-Sektoru-2020.pdf
• Özgüven, M. M. ve Közkurt, C. (2021, Şubat, 22-25). Agricultural robots and smart agricultural machinery. International Symposium of Scientific Research and Innovative Studies [Sözlü sunum]. Bandırma, Turkiye.
• Parra, L., Marin, J., Yousfi, S., Rincón, G., Mauri, P. V. ve Lloret, J. (2020). Edge detection for weed recognition in lawns. Computers and Electronics in Agriculture, 176, 105684. https://doi.org/10.1016/j.compag.2020.105684
• Reza, M. N., Na, I. S., Baek, S.W. ve Lee, K. H. (2019). Rice yield estimation based on k-means clustering with graph-cut segmentation using low-altitude uav images. Biosystems engineering 177, 109 -121. https://doi.org/10.1016/j.biosystemseng.2018.09.014
• Sarwar, F., Griffin, A., Periasamy, P., Portas, K. ve Law, J. (2018). Detecting and counting sheep with a convolutional neural network. 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). 1-6, 18455885. https://doi.org/10.1109/AVSS.2018.8639306
• Sebbane, Y. B. (2018). Intelligent autonomy of uavs, advanced missions and future use. CRC Press Taylor & Francis Group.
• Singh, N. ve Singh, A. N. (2020). Odysseys of agriculturee sensors: Current challenges and forthcoming prospects. Computers and Electronics in Agriculture, 171, 105328, 2020. https://doi.org/10.1016/j.compag.2020.105328
• Skacev, H., Micovic, A., Gutic, B., Dotilic, D., Vesic, A., Ignjatovic, V., Lakicevic, S., Jakovljevic, M. M. ve Zivkovic, M. (2020). On the development of the automatic weed detection tool. IEEE Zooming Innovation in Consumer Technologies Conference (ZINC). 123-126. https://doi.org/10.1109/ZINC50678.2020.9161802
• Stavrakoudis, D., Katsantonis, D., Kadoglidou, K., Kalaitzidis, A. ve Gitas, I. Z. (2019). Estimating rice agronomic traits using drone-collected multispectral ımagery. Remote Sensing, 11, 545. https://doi.org/10.3390/rs11050545
• Su, J., Liu, C, Coombes, M., Hu, X., Wang, C., Xu, X., Li, Q., Guo, L. ve Chen, W, H. (2018). Wheat yellow rust monitoring by learning from multispectral uav aerial imagery. Computers and Electronics in Agriculture 155, 157–166. https://doi.org/10.1016/j.compag.2018.10.017
• Syifa, M., Park, S. J. ve Lee, C. W. (2020). Detection of the pine wilt disease tree candidates for drone remote sensing using artificial ıntelligence techniques. Engineering, 6(8), 919–926. https://doi.org/10.1016/j.eng.2020.07.001
• Tan, M., Özgüven, M. M. ve Tarhan, S. (2015, Eylül,2-5). Drone sistemlerin hassas tarımda kullanımı. 29. Tarımsal Mekanizasyon Kongresi ve Enerji Kongresi [Sözlü Sunum], Diyarbakır, Türkiye.
• Tao, H., Feng, H., Xu, L., Miao, M., Yang, G., Yang, X. ve Fan, L. (2020). Estimation of the yield and plant height ofwinter wheat using uav-based hyperspectral images. Sensors, 20, 1231. https://doi.org/10.3390/s20041231
• Turgut, M. N. (2011). Dört rotorlu insansız hava aracının modellenmesi ve simülasyonu. [Yüksek Lisans Tezi]. Yıldız Teknik Üniversitesi.
• Um, J. S. (2019). Drones as cyber-physical systems. Springer Nature.
• Uygun, T., Özgüven, M. M. ve Altaş, Z. (2019, Nisan, 24-27). Lidar (Light detection and ranging) sensörlerin tarımda kullanımı. International Erciyes Agriculture, Animal & Food Sciences Conference [Sözlü sunum]. Kayseri, Türkiye.
• Vayssade, J. A., Arquet, R. ve 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
• Wang, G., Han, Y., Li, X., Andaloro, J., Chen, P., Hoffmann, W., Han, X., Chen, S. ve Lan, Y. (2020). Field evaluation of spray drift and environmental ımpact using an agricultural unmanned aerial vehicle (uav) sprayer. Science of the Total Environment, 737, 139793. https://doi.org/10.1016/j.scitotenv.2020.139793
• Xu, B., Wang, W., Falzon, G., Kwan, P., Guo, L., Chen, G., Teit, A. ve Schneider, D. (2020). Automated cattle counting using mask r-cnn in quadcopter vision system. Computers and Electronics in Agriculture, 171, 105300. https://doi.org/10.1016/j.compag.2020.105300
• Yallappa, D., Veerangouda, M., Maski, D., Palled, V. ve Bheemanna, M. (2017). Development and evaluatıon of drone mounted sprayer for pestıcıde applıcatıons to crops. 2017 IEEE Global Humanitarian Technology Conference (GHTC), 1-7. https://doi.org/10.1109/GHTC.2017.8239330
• Zhang, L., Zhang, H., Niu, Y. ve Han, W. (2019). Mapping maizewater stress- based on uav multispectral remote sensing. Remote Sensing, 11, 605. https://doi.org/10.3390/rs11060605