Eye of the farmer in the sky: Drones

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.

Anahtar Kelimeler

• Precision agriculture
• Agriculture 5.0
• Unmanned aerial vehicles
• livestock management

Çiftçinin Gökteki Gözü: Drone

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.

Anahtar Kelimeler

• Hassas tarım
• Tarım 5.0
• İnsansız hava aracı
• Çiftlik yönetimi

Kaynakça

1. Abbas M, Ali H & Muhammad A (2019). Autonomous canal following by a micro-aerial vehicle using deep CNN. IFAC PapersOnLine, 52(30), 243–250.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. Barbedo JGA & Koenigkan LV (2018). Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook on Agriculture, 47(3), 214-222.

9. 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.
10. 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.
11. Beloev IH (2016). A review on current and emerging application possibilities for unmanned aerial vehicles. Acta Technologica Agriculturae, 19, 70–76.
12. Berckmans D (2008). Precision livestock farming (PLF). Computers and Electronics in Agriculture, 62(1), 1.
13. 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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. Cheng TM & Savkin AV (2011). Decentralized control for mobile robotic sensor network self-deployment: Barrier and sweep coverage problems. Robotica, 29 (2), 283–294.
22. 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.
23. 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.
24. 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.
25. 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.
26. Fang Y, Du S, Abdoola R, Djuani K & Richards C (2016). Motion based animal detection in aerial videos. Procedia Computer Science, 92, 13–17.
27. 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.
28. 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.
29. 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.
30. 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.
31. 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.
32. 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.
33. 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.
34. 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.
35. Hogan S, Kelly M, Stark B & Chen Y (2017). Unmanned aerial systems for agriculture and natural resources. California Agriculture, 5-14.
36. 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).
37. 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).
38. 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.
39. 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.
40. Ju C & Son H (2018). Multiple UAV systems for agricultural applications: control, implementation, and evaluation. Electronics, 7(9), 162.
41. Jung S & Ariyur KB (2017). Strategic cattle roundup using multiple quadrotor UAVs. International Journal of Aeronautical and Space Sciences, 18, 315–326.
42. Kennedy C, Ila V & Mahony R (2019). A Perception Pipeline for Robotic. IFAC PapersOnLine, 52(30), 288–293.
43. Krajník T, Vonásek V, Fišer D & Faigl J (2011). AR-Drone as a Platform for Robotic Research. In: Obdržálek D, Gottscheber A. (eds) Research and Education in Robotics - EUROBOT 2011. Communications in Computer and Information Science, 161, 172-186. Springer, Berlin, Heidelberg.
44. Kulbacki M, Segen J, Knie´c, W, Klempous R, Kluwak K, Nikodem J, Kulbacka J & Serester A (2018). Survey of Drones for Agriculture Automation from Planting to Harvest. INES 2018- 22nd IEEE International Conference on Intelligent Engineering Systems, June 21-23. Las Palmas de Gran Canaria, Spain.
45. Lagkas T, Argyriou V, Bibi S & Sarigiannidis P (2018). UAV IoT Framework Views and Challenges: Towards Protecting Drones as “Things”. Sensors, 18, 4015. doi:10.3390/s18114015.
46. Lhoest S, Linchant J, Quevauvillers S, Vermeulen C & Lejeune P (2015). How many hippos (HOMHIP): algorithm for automatic counts of animals with infra-red thermal imagery from UAV. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3(W3), 355–362.
47. Li, X & Xing L (2019). Use of unmanned aerial vehicles for livestock monitoring based on streaming K-means clustering. IFAC PapersOnLine 52(30), 324–329. Linchant J, Lisein J, Semeki J, Lejeune P & Vermeulen C (2015). Are unmanned aircraft systems (UAS) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Review, 45, 239–252.
48. Longmore S, Collins R, Pfeifer S, Fox SE, Mulero-Pazmany M, Goodwin A, de Juan-Ovelar M, Knapen JH & Wich SA (2017). Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems. International Journal of Remote Sensing, 38, 2623–2638.
49. Lottes P, Hoferlin M, Sander S & Stachniss C (2017). Effective vision-based classification for separating sugar beets and weeds for precision farming. Journal of Field Robotics, 34(6), 1160–1178.
50. Luo C, Miao W, Ullah H, McClean S, Par G & Min G (2019). Unmanned Aerial Vehicles for Disaster Management. 10.1007/978-981-13-0992-2_7.
51. Maddikunta PMR, Hakak S, Alazab M, Bhattacharya S, Gadekallu TR, Khan WZ, Pham QV. (2020). Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges. IEEE Sensors Journal, 21, 17608-17619.
52. Majeed Y, Karkee M, Zhang Q, Fu L & Whiting MD (2019). A study on the detection of visible parts of cordons using deep learning networks for automated green shoot thinning in vineyards. IFAC PapersOnLine, 52(30), 82–86.
53. Malamiri HRG, Aliabad FA, Shojaei S, Morad M & Band SS (2021). A study on the use of UAV images to improve the separation accuracy of agricultural land areas. Computers and Electronics in Agriculture 184, 106079, 1-13.
54. Miller JO, Adkins J & Tully K (2017). Providing aerial images through UAVs. Fact Sheet FS-1056. Available at: https://drum. lib.umd.edu/handle/1903/19168 (accessed date: 01 April 2021).
55. Mitsuashi T, Chida Y & Tanemura M (2019). Autonomous travel lettuce harvester using model predictive control. IFAC PapersOnLine, 52(30), 155–160.
56. Mohanty SP, Hughes DP & Salathé M (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7, 1419.
57. Mulero-Pázmány M, Stolper R, Essen L, Negro JJ & Sassen T (2014). Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS ONE, 9, e83873.
58. Nyamuryekung’e S, Cibils A, Estell R & Gonzalez A (2016). Use of an unmanned aerial vehicle-mounted video camera to assess feeding behavior of Raramuri Criollo cows. rangel. Ecol. Manag., 69, 386–389.
59. O’ Mahony N, Campell S, Carvalho A, Krpalkova L, Riordan D & Walsh J (2019). 3D vision for precision dairy farming. IFAC PapersOnLine, 52(30), 312–317. Pimenta LCA, Kumar V, Mesquita RC & Pereira GAS (2008). Sensing and coverage for a network of heterogeneous robots. In 2008, 47th IEEE Conference on Decision and Control, 3947–3952.
60. Polder G, van de Westeringh N, Kool J, Khan HA, Kootstra G & Niuwenhuizen A (2019). Automatic detection of tulip breaking virus (TBV) using a deep convolutional neural network. IFAC PapersOnLine, 52(30), 12–17.
61. Qiao Y, Su D, Kong H, Sukkarieh S, Lomax S & Clark C (2019). Individual cattle identification using a deep learning based framework. IFAC PapersOnLine, 52(30), 318–323.
62. Reinecke M & Prinsloo T (2017). The influence of drone monitoring on crop health and harvest size. 1st International Conference on Next Generation Computing Applications, 5-10.
63. Rivas A, Chamoso P, González-Briones A & Corchado J M (2019). Detection of cattle using drones and convolutional neural networks. Sensors, 18, 2048. doi:10.3390/s18072048.
64. Savkin AV, Cheng TM, Xi Z, Javed F, Matveev AS & Nguyen H (2015). Decentralized coverage control problems for mobile robotic sensor and actuator networks. John Wiley & Sons.
65. Schroeder NM, Panebianco A, Musso RG & Carmanchahi P (2020). An experimental approach to evaluate the potential of drones in terrestrial mammal research: a gregarious ungulate as a study model. Royal Society Open Science, 7, 191482.
66. Schwager M, Rus D & Slotine JJ (2009). Decentralized, adaptive coverage control for networked robots. The International Journal of Robotics Research, 28(3), 357–375.
67. Slaughter DC, Giles DK & Downey D (2008). Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61, 63-78.
68. Söffker D, Kögler F & Owino L (2019). Crop growth modelling a new data driven approach. IFAC PapersOnLine, 52(30), 132–136.
69. Terletzky P, Ramsey RD & Neale CMU (2012). Spectral characteristics of domestic and wild mammals. GIScience & Remote Sensing, 49, 597–608.
70. Tripicchio P, Satler M, Dabisias G, Ruffaldi E & Avizzano CA (2015). Towards smart farming and sustainable agriculture with drones. International Conference on Intelligent Environments, Prague, Czech Republic, 140-143. doi: 10.1109/IE.2015.29.
71. Tsiamis N, Efthymiou L & Tsagarakis KP (2019). A comparative analysis of the legislation evolution for drone use in OECD countries. Drones, 3(75), 2-15. doi:10.3390/drones3040075.
72. Trumbull TR & Myrtle SR (2017). Unmanned livestock monitoring system and methods of use. U.S. Patent Application 20170202185. Available at: https://patents.google.com/pat ent/WO2017127188A1/en (accessed date: 01 March June 2018).
73. Van der Merwe D, Burchfield DR, Witt TD, Price KP & Sharda A (2020). Chapter One- Drones in agriculture. Advances in agronomy, ed. Sparks DL. 162, 1-30. Academic Press
74. Van Henten EJ, Hemming J, Van Tuijl BAJ, Kornet JG, Meuleman J, Bontsema J & Van Os EA (2002). An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots, 13, 241–258.
75. Vermeulen C, Lejeune P, Lisein J, Sawadogo P & Bouche P (2013). Unmanned Aerial Survey of Elephants. PLoS ONE, 8, e54700.
76. Wathesa CM, Kristensen HH, Aerts J-M & Berckmans D (2008). Is precision livestock farming an engineer’s daydream or nightmare, an animal’s friend or foe, and a farmer’s panacea or pitfall?. Computers and Electronics in Agriculture, 64, 2-10.
77. Webb P, Mehlhorn SA & Smartt P (2017). Developing protocols for using a UAV to monitor herd health. In Proceedings of the 2017 ASABE Annual International Meeting, Spokane, WA, USA, 16–19, July, 1700865.
78. Witczuk J, Pagacz S, Zmarz A & Cypel M (2018). Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests - preliminary results. International Journal of Remote Sensing, 39, 15-16.
79. Xiang H & Tian L (2011). Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). In: Biosystems Engineering, 108 (2), 174-190, doi: 16/j.biosystemseng. 2010.11.010.
80. Xie W, Wang F & Yang D (2019). Research on carrot grading based on machine vision feature parameters. IFAC PapersOnLine, 52(30), 30–35.
81. Xue Y, Wang T & Skidmore AK (2017). Automatic counting of large mammals from very high-resolution panchromatic satellite imagery. Remote Sensing, 9, 878.
82. Yeşilay RB & Macit A (2020). Dünyada ve Türkiye’de drone ekonomisi: Geleceğe yönelik beklentiler. Beykoz Akademi Dergisi, 8(1), 239-251.
83. Zapotezny-Andersen, P & Lehnert C (2019). Towards Active Robotic Vision in Agriculture: A deep learning approach to visual servoing in occluded and unstructured protected cropping environments. IFAC PapersOnLine, 52(30), 120–125.
84. Zhang X, Fu L, Karkee M, Whiting MD & Zhang Q (2019). Canopy segmentation using ResNet for mechanical harvesting of apples. IFAC PapersOnLine, 52(30), 300–305.