Eye of the farmer in the sky: Drones
Yıl 2021,
, 69 - 77, 30.12.2021
Sabri Gül
,
Yusuf Ziya Güzey
,
Hakan Yıldırım
,
Mahmut Keskin
Öz
Mankind develops new technics and technologies constantly to have a better life. In this way, powerful machines and robotic systems replace human and animal labour in agriculture. Animal husbandry, which is a part of agricultural activity in our country, is mostly carried out in rural areas due to its nature. Goat breeding, in particular, is carried out in highlands, scrub and forest lands and under extensive conditions. Qualified shepherd employment is an important handicap in sheep and goat breeding. Agricultural enterprises are also faced with a manpower deficit due to the decrease in the rural population. Remote sensing systems have been developed and used for about 100 years to support and enhance agricultural activities. In this study, the importance of unmanned aerial vehicles in terms of animal husbandry is mentioned and it is emphasized that they should be taken into consideration in future agricultural projections.
Kaynakça
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Çiftçinin Gökteki Gözü: Drone
Yıl 2021,
, 69 - 77, 30.12.2021
Sabri Gül
,
Yusuf Ziya Güzey
,
Hakan Yıldırım
,
Mahmut Keskin
Öz
İnsanoğlu, daha iyi bir yaşama sahip olmak için sürekli olarak yeni teknikler ve teknolojiler geliştirmektedir. Böylelikle güçlü makineler ve robotik sistemler, tarımda insan ve hayvan işgücünün yerini almaktadır. Ülkemizde tarımsal faaliyetin bir parçası olan hayvancılık, doğası gereği daha çok kırsal kesimde yapılmaktadır. Küçükbaş hayvan yetiştiriciliği özellikle yaylalarda, maki ve ormanlık alanlarda ve geniş koşullarda yapılmaktadır. Koyun ve keçi yetiştiriciliğinde nitelikli çoban istihdamı önemli bir sorundur. Tarımsal işletmelerde kırsal nüfusun azalması nedeniyle insan gücü açığı ile karşı karşıyadır. Uzaktan algılama sistemleri, tarımsal faaliyetleri desteklemek ve iyileştirmek için 1930'lardan beri geliştirilmiş ve kullanılmaktadır. Bu çalışmada insansız hava araçlarının hayvancılık açısından öneminden bahsedilmiş ve gelecekteki tarımsal projeksiyonlarda dikkate alınması hususu vurgulanmıştır.
Kaynakça
- Abbas M, Ali H & Muhammad A (2019). Autonomous canal following by a micro-aerial vehicle using deep CNN. IFAC PapersOnLine, 52(30), 243–250.
- Afonso M, Blok PM, Polder G, M J, van der Wolf & Kamp J (2019). Blackleg detection in potato plants using convolutional neural networks. IFAC PapersOnLine, 52(30), 6-11.
- Albani D, Youssef A, Suriani V, Nardi, D, Bloisi DD (2017). A deep learning approach for object recognition with NAO soccer robots. 20. RoboCup International Symposium, 4 July, Leipzig, Germany.
- Alsalam BHY, Morton K, Campell D & Gonzalez F (2017). Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture. EEE Aerospace Conference, 3-11 March, 1-11.
- Andrew W, Greatwood C & Burghardt T (2019). Aerial animal biometrics: Individual friesian cattle recovery and visual identification via an autonomous UAV with on board deep inference. arXiv:1907.05310v1.
- Aydemir Ş (2019). Yaban keçisi envanterinde kullanılan yöntemlerden noktada sayım tekniği ile dron kullanımının karşılaştırılması. Yüksek Lisans Tezi, Artvin Çoruh Üniversitesi, Fen Bilimleri Enstitüsü, Orman Mühendisliği, Anabilim Dalı, 54.
- Banhazi TM & Black JL (2009). Precision livestock farming: a suite of electronic systems to ensure the application of best practice management on livestock farms. Australian Journal of Multi-disciplinary Engineering, 7(1), 1-14.
- Barbedo JGA & Koenigkan LV (2018). Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook on Agriculture, 47(3), 214-222.
- Barbedo JGA, Koenigkan LV, Santos TT & Santos PM (2019). A study on the detection of cattle in UAV images using deep learning. Sensors, 19, 5436. doi:10.3390/s19245436.
- Barbedo JGA, Koenigkan LV, Santos PM & Ribeiro ARB (2020). Counting cattle in UAV images-dealing with clustered animals and animal/background contrast changes. Sensors, 20, 2126. doi:10.3390/s20072126.
- Beloev IH (2016). A review on current and emerging application possibilities for unmanned aerial vehicles. Acta Technologica Agriculturae, 19, 70–76.
- Berckmans D (2008). Precision livestock farming (PLF). Computers and Electronics in Agriculture, 62(1), 1.
- Bhusal S, Bhattarai U & Karkee M (2019). Improving pest bird detection in a vineyard environment using super-resolution and deep learning. IFAC -PapersOnLine, 52, 18-23.
- Bramley RGV (2009). Lessons from nearly 20 years of Precision Agriculture research, development, and adoption as a guide to its appropriate application. Crop & Pasture Science, 60(3), 197-217.
- Brisson-Curadeau É, Bird D, Burke C, Fifield DA, Pace P, Sherley RB & Elliott KH (2017). Seabird species vary in behavioural response to drone census. Scientific Reports, 7, 17884. Doi:10.1038/s41598-017-18202-3.
- Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN & Smith V H (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological Applications 8(3), 559-568.
- Chabot D, Craik S R & Bird DM (2015). Population census of a large common tern colony with a small-unmanned aircraft. PLoS ONE, 10, e0122588.
- Chabot D & Bird DM (2015). Wildlife research and management methods in the 21st century: where do unmanned aircraft fit in?. Journal of Unmanned Vehicle Systems, 3, 137–155.
- Chamoso P, Raveane W, Parra V & González A (2014). UAVs Applied to the counting and monitoring of animals. Advances in Intelligent Systems and Computing, 291, 71–80.
- Cheng TM & Savkin AV (2009). A distributed self-deployment algorithm for the coverage of mobile wireless sensor networks. IEEE Communications Letters, 13(11), 877–879.
- Cheng TM & Savkin AV (2011). Decentralized control for mobile robotic sensor network self-deployment: Barrier and sweep coverage problems. Robotica, 29 (2), 283–294.
- Chrétien LP, Théau J & Ménard P (2015). Wildlife multispecies remote sensing using visible and thermal infrared imagery acquired from an unmanned aerial vehicle (UAV). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W4, International Conference on Unmanned Aerial Vehicles in Geomatics, 30 Aug–02 Sep, Toronto, Canada.
- Chrétien LP, Théau J & Ménard P (2016). Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system. Wildlife Society Bulletin, 40(1), 181–191.
- Cortes J, Martinez S, Karatas T & Bullo F (2004). Coverage control for mobile sensing networks. IEEE Transactions on robotics and Automation, 20(2), 243–255.
- De Castro AI, Jiménez-Brenes FM, Torres-Sánchez J, Peña JM, Borra-Serrano I & López-Granados F (2018). 3-D characterization of vineyards using a novel UAV imagery-based OBIA procedure for precision viticulture applications. Remote Sensing, 584, doi:10.3390/rs10040584.
- Fang Y, Du S, Abdoola R, Djuani K & Richards C (2016). Motion based animal detection in aerial videos. Procedia Computer Science, 92, 13–17.
- Franke U, Goll B, Hohmann U & Heurich M (2012). Aerial ungulate surveys with a combination of infrared and high-resolution natural colour images. Animal Biodiversity and Conservation, 35, 285–293.
- Frost AR, Schofield CP, Beaulah SA, Mottram TT, Lines JA & Wathes CM (1997). A review of livestock monitoring and the need for integrated systems. Comput. Electron. Agric. 17, 139-159.
- Gnip P, Charvat K & Krocan M (2008). Analysis of external drivers for agriculture. World conference on agricultural information and IT, LAAID AFITA WCCA 797-801.
- Gonzalez LF, Montes GA, Puig E, Johnson S, Mengersen K & Gaston KJ (2016). Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors, 16, 97. doi:10.3390/s16010097.
- Gonzalez de Santos P, Ribeiro A, Fernandez Quintanilla C, Lopez Granados F, Brandstoetter M, Tomic S, Pedrazzi S, Peruzzi A, Pajares G & Kaplanis G (2017). Fleets of robots for environmentally safe pest control in agriculture. Precis. Agric., 18, 574–614.
- Harris JM, Nelson JA, Rieucau G & Broussard W (2019). Use of unmanned aircraft systems in fishery science. Transactions of the American Fisheries Society. 148. 10.1002/tafs.10168.
- Hussein II & Stipanovic DM (2007). Effective coverage control using dynamic sensor networks with flocking and guaranteed collision avoidance. IEEE Transactions on Control Systems Technology, 15 (4), 642–657.
- Hodgson JC, Mott R, Baylis SM, Pham PP, Wotherspoon S, Kilpatrick AD, Segaran RR, Reid, I, Terauds A & Koh LP (2018). Drones count wildlife more accurately and precisely than humans. Methods in Ecology Evolution, 9, 1160–1167.
- Hogan S, Kelly M, Stark B & Chen Y (2017). Unmanned aerial systems for agriculture and natural resources. California Agriculture, 5-14.
- Horton CV & Vorpahl SR (2017a). Agricultural drone for use in livestock feeding. U.S. Patent Application 20170086429. Available at: https://patents.google.com/patent/US20170086429 (accessed date: 01 March 2021).
- Horton CV & Vorpahl SR (2017b). Agricultural drone for use in livestock monitoring. U.S. Patent Application 20170086428. Available at: https://patents.google.com/patent/WO2017053135A1/en (accessed date: 01 March 2021).
- Hunt ER Jr, Daughtry CST, Mirsky SB & Hively D (2014). Remote sensing with simulated unmanned aircraft imagery for precision agriculture applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, 4566–4571.
- Israel M (2011). A UAV-based roe deer fawn detection system. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Munich, Germany, 5–7 October, 51–55.
- Ju C & Son H (2018). Multiple UAV systems for agricultural applications: control, implementation, and evaluation. Electronics, 7(9), 162.
- Jung S & Ariyur KB (2017). Strategic cattle roundup using multiple quadrotor UAVs. International Journal of Aeronautical and Space Sciences, 18, 315–326.
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