Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Review Articles

Vol. 11 No. sp4 (2024): Recent Advances in Agriculture by Young Minds - I

Prospects and challenges of drone technology in sustainable agriculture

DOI
https://doi.org/10.14719/pst.5761
Submitted
13 October 2024
Published
27-12-2024

Abstract

Drones have emerged as a viable precision agriculture technology that can help achieve sustainable development goals (SDGs) by enhancing sustainable farming practices, increasing food security and reducing environmental impact. This review paper aims to thoroughly examine the various applications of drone technology, including crop health monitoring, pesticide and fertilizer spraying, weed control and data-driven decision-making for farm optimization. It emphasizes the role of drones in precision spraying, promoting targeted interventions and minimizing environmental impact compared to conventional methods. Drones play a vital role in weed management and crop health assessment. The study emphasizes the relevance of data collected by drones for decision-making concerning irrigation, fertilization and overall farm management. However, using Unmanned aerial vehicles (UAVs) in agriculture faces challenges caused by batteries and their life, flight time and connectivity, particularly in remote areas. There are legal challenges whereby regulatory frameworks and restrictions are present in different regions that affect the operation of drones. With the help of continuous research and development initiatives, the challenges depicted above could be solved and the fullest potential of drones can be tapped for achieving sustainable agriculture.

References

  1. Zhang XQ, Song XP, Liang YJ, Qin ZQ, Zhang BQ, Wei JJ, et al. Effects of spray parameters of drone on the droplet deposition in sugarcane canopy. Sugar Tech. 2020;22:583–8. https://doi.org/10.1007/s12355–019–00792–z
  2. Hafeez A, Husain MA, Singh S, Chauhan A, Khan MT, Kumar N, et al. Implementation of drone technology for farm monitoring & pesticide spraying: A review. Information processing in Agriculture. 2022. https://doi.org/10.1016/j.inpa.2022.02.002
  3. Rejeb A, Abdollahi A, Rejeb K, Treiblmaier H. Drones in agriculture: A review and bibliometric analysis. Computers and electronics in agriculture. 2022;198:107017. https://doi.org/10.1016/j.compag.2022.107017
  4. Puri V, Nayyar A, Raja L. Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems. 2017;20(4):507–18. https://doi.org/10.1080/09720510.2017.1395171
  5. Nhamo L, Magidi J, Nyamugama A, Clulow AD, Sibanda M, Chimonyo VG, et al. Prospects of improving agricultural and water productivity through unmanned aerial vehicles. Agriculture. 2020;10(7):256. https://doi.org/10.3390/agriculture10070256
  6. Nhamo L, Mabhaudhi T, Modi A. Preparedness or repeated short–term relief aid? Building drought resilience through early warning in southern Africa. Water Sa. 2019;45(1):75–85.
  7. https://doi.org/10.4314/wsa.v45i1.09
  8. Olson D, Anderson J. Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal. 2021;113(2):971–92. https://doi.org/10.1002/agj2.20595
  9. Pongnumkul S, Chaovalit P, Surasvadi N. Applications of smartphone–based sensors in agriculture: a systematic review of research. Journal of Sensors. 2015;195308:1-18. https://doi.org/10.1155/2015/195308
  10. Delavarpour N, Koparan C, Nowatzki J, Bajwa S, Sun X. A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sensing. 2021;13(6):1204. https://doi.org/10.3390/rs13061204
  11. Barbedo JGA. A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones. 2019;3(2):40. https://doi.org/10.3390/drones3020040
  12. Ishihara M, Inoue Y, Ono K, Shimizu M, Matsuura S. The impact of sunlight conditions on the consistency of vegetation indices in croplands—Effective usage of vegetation indices from continuous ground–based spectral measurements. Remote Sensing. 2015;7(10):14079–98. https://doi.org/10.3390/rs71014079
  13. Hoffmann H, Nieto H, Jensen R, Guzinski R, Zarco–Tejada P, Friborg T. Estimating evaporation with thermal UAV data and two–source energy balance models. Hydrology and Earth System Sciences. 2016;20(2):697–713. https://doi.org/10.5194/hess–20–697–2016
  14. Mulero–Pázmány M, Stolper R, Van Essen L, Negro JJ, Sassen T. Remotely piloted aircraft systems as a rhinoceros anti–poaching tool in Africa. PloS one. 2014;9(1):e83873. https://doi.org/10.1371/journal.pone.0083873
  15. Barbedo JGA, Koenigkan LV. Perspectives on the use of unmanned aerial systems to monitor cattle. Outlook on agriculture. 2018;47(3):214–22. https://doi.org/10.1177/0030727018781876
  16. Gabriel JL, Zarco–Tejada PJ, López–Herrera PJ, Pérez–Martín E, Alonso–Ayuso M, Quemada M. Airborne and ground level sensors for monitoring nitrogen status in a maize crop. Biosystems Engineering. 2017;160:124–33. https://doi.org/10.1016/j.biosystemseng.2017.06.003
  17. Dileep M, Navaneeth A, Ullagaddi S, Danti A, editors. A study and analysis on various types of agricultural drones and its applications. 2020 fifth international conference on research in computational intelligence and communication networks (ICRCICN); 2020: IEEE. https://doi.org/10.1109/ICRCICN50933.2020.9296195
  18. Ren Q, Zhang R, Cai W, Sun X, Cao L, editors. Application and development of new drones in agriculture. IOP conference series: earth and environmental science; 2020: IOP Publishing. https://doi.org/10.1088/1755–1315/440/5/052041
  19. Ukhurebor KE, Adetunji CO, Olugbemi OT, Nwankwo W, Olayinka AS, Umezuruike C, et al. Precision agriculture: Weather forecasting for future farming. AI, Edge and IoT–based Smart Agriculture: Elsevier;2022. p. 101–21. https://doi.org/10.1016/B978–0–12–823694–9.00008–6
  20. Debangshi U. Drone –Applications in Agriculture. Chronicle of Bioresource Management 2021, 5(3):115-120
  21. Atukunda P, Eide WB, Kardel KR, Iversen PO, Westerberg AC. Unlocking the potential for achievement of the UN Sustainable Development Goal 2–'Zero Hunger'–in Africa: targets, strategies, synergies and challenges. Food & Nutrition Research. 2021;65. https://doi: 10.29219/fnr.v65.7686
  22. Mohamed M. Agricultural Sustainability in the Age of Deep Learning: Current Trends, Challenges, and Future Trajectories. Sustainable Machine Intelligence Journal. 2023 Sep 18;4:2–1.
  23. https://doi.org/10.61185/SMIJ.2023.44102
  24. Sayer J, Sheil D, Galloway G, Riggs RA, Mewett G, MacDicken KG, Arts BJ, Boedhihartono AK, Langston J, Edwards DP. In: Katila P, Pierce Colfer CJ, de Jong W, Galloway G, Pacheco P, Winkel G, eds. Sustainable Development Goals: Their Impacts on Forests and People. Cambridge University Press; 2019. p. 482–509. https://doi.org/10.1017/9781108765015.017
  25. Tomaselli MF, Timko J, Kozak R, Bull J, Kearney S, Saddler J, Zhu X. SDG 9: industry, innovation and infrastructure–anticipating the potential impacts on forests and forest–based livelihoods. Sustainable Development Goals. 2019 Dec 12;279.
  26. Singhal G, Bansod B, Mathew L. Unmanned aerial vehicle classification, applications and challenges: A review. Preprints. 2018; 2018110601. https://doi.org/10.20944/preprints201811.0601.v1
  27. Liebisch F, Kirchgessner N, Schneider D, Walter A, Hund A. Remote, aerial phenotyping of maize traits with a mobile multi–sensor approach. Plant Methods. 2015;11:1–20. https://doi.org/10.1186/s13007–015–0048–8
  28. Marinello F, Pezzuolo A, Chiumenti A, Sartori L. Technical analysis of unmanned aerial vehicles (drones) for agricultural applications. Engineering for rural development. 2016;15(2):870–5.
  29. Herwitz S, Johnson L, Dunagan S, Higgins R, Sullivan D, Zheng J, et al. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Computers and Electronics in Agriculture. 2004;44(1):49–61. https://doi.org/10.1016/j.compag.2004.02.006
  30. Chapman SC, Merz T, Chan A, Jackway P, Hrabar S, Dreccer MF, et al. Pheno–copter: a low–altitude, autonomous remote–sensing robotic helicopter for high–throughput field–based phenotyping. Agronomy. 2014;4(2):279–301. https://doi.org/10.3390/agronomy4020279
  31. Peña JM, Torres–Sánchez J, de Castro AI, Kelly M, López–Granados F. Weed mapping in early–season maize fields using object–based analysis of unmanned aerial vehicle (UAV) images. PloS One. 2013;8(10):e77151. https://doi.org/10.1371/journal.pone.0077151
  32. Sinha J. Aerial robot for smart farming and enhancing farmers' net benefit. Indian Journal of Agricultural Sciences. 2020;90(2):258–67. https://doi.org/10.56093/ijas.v90i2.98997
  33. Subramanian K, Pazhanivelan S, Srinivasan G, Santhi R, Sathiah N. Drones in insect pest management. Frontiers in Agronomy. 2021;3:640885. https://doi.org/10.3389/fagro.2021.640885
  34. Qin W–C, Qiu B–J, Xue X–Y, Chen C, Xu Z–F, Zhou Q–Q. Droplet deposition and control effect of insecticides sprayed with an unmanned aerial vehicle against plant hoppers. Crop Protection. 2016;85:79–88. https://doi.org/10.1016/j.cropro.2016.03.018
  35. Panjaitan SD, Dewi YSK, Hendri MI, Wicaksono RA, Priyatman H. A drone technology implementation approach to conventional paddy fields application. IEEE Access. 2022;10:120650–8. https://doi.org/10.1109/ACCESS.2022.3221188
  36. Kaniska K, Jagadeeswaran R, Kumaraperumal R, Ragunath KP, Kannan B, Muthumanickam D, et al. Impact of Drone Spraying of Nutrients on Growth and Yield of Maize Crop. International Journal of Environment and Climate Change. 2022;12(11):274–82. https://doi.org/10.9734/ijecc/2022/v12i1130972.
  37. Chen P, Ouyang F, Wang G, Qi H, Xu W, Yang W, et al. Droplet distributions in cotton harvest aid applications vary with the interactions among the unmanned aerial vehicle spraying parameters. Industrial Crops and Products. 2021;163:113324. https://doi.org/10.1016/j.indcrop.2021.113324
  38. Lou Z, Xin F, Han X, Lan Y, Duan T, Fu W. Effect of unmanned aerial vehicle flight height on droplet distribution, drift and control of cotton aphids and spider mites. Agronomy. 2018;8(9):187. https://doi.org/10.3390/agronomy8090187
  39. Zhang P, Zhang W, Sun H–T, He F–G, Fu H–B, Qi L–Q, et al. Effects of Spray Parameters on the Effective Spray Width of Single–Rotor Drone in Sugarcane Plant Protection. Sugar Tech. 2021;23:308–15. https://doi.org/10.1007/s12355–020–00890–3
  40. Freeman PK, Freeland RS. Agricultural UAVs in the US: potential, policy, and hype. Remote Sensing Applications: Society and Environment. 2015;2:35– 43. https://doi.org/10.1016/j.rsase.2015.10.002
  41. Nandhini P, Muthumanickam D, Pazhanivelan RS, Kumaraperumal R, Ragunath KP, Sudarmanian NS. Intercomparision of Drone and Conventional Spraying Nutrients on Crop Growth and Yield in Black Gram. International Journal of Plant & Soil Science. 2022;34(20):845–52. https://doi.org/10.9734/ijpss/2022/v34i2031231
  42. Dayana K, Ramesh T, Avudaithai s, Sebastian P, Selvaraj R. Feasibility of using drone for foliar spraying of nutrients in irrigated green gram (Vigna radiata L.). Ecology, Environment and Conservation. 2022;28:64. https://doi.org/10.53550/EEC.2022.v28i01s.064
  43. Ribeiro LFO, Vitória ELd, Soprani Júnior GG, Chen P, Lan Y. Impact of Operational Parameters on Droplet Distribution Using an Unmanned Aerial Vehicle in a Papaya Orchard. Agronomy. 2023;13(4):1138. https://doi.org/10.3390/agronomy13041138
  44. Koondee P, Saengprachatanarug K, Posom J, Watyotha C, Wongphati M. Study of field capacity and variables of UAV operation time during spraying hormone fertilizer in sugarcane field. IOP Conference Series: Earth and Environmental Science. 2019;301:012020. https://doi.org/10.1088/1755–1315/301/1/012020
  45. Chokkalingam V, Kamble B, Durgude A, Ingle S, K P. Hyperspectral Imaging of Soil and Crop: A Review. Journal of Experimental Agriculture International. 2024;46:48–61. https://doi.org/10.9734/jeai/2024/v46i12290
  46. Dutta S, Singh AK, Mondal BP, Paul D, Patra K. Digital Inclusion of the Farming Sector Using Drone Technology. 2023. https://doi.org/10.5772/intechopen.108740
  47. Shafi U, Mumtaz R, García–Nieto J, Hassan S, Zaidi SAR, Iqbal N. Precision Agriculture Techniques and Practices: From Considerations to Applications. Sensors. 2019;19:3796. https://doi.org/10.3390/s19173796
  48. Ali M, Al–Ani A, Eamus D, Tan DK. Leaf nitrogen determination using non–destructive techniques–A review. Journal of Plant Nutrition. 2017;40(7):928–53. https://doi.org/10.1080/01904167.2016.1143954
  49. Capolupo A, Kooistra L, Berendonk C, Boccia L, Suomalainen J. Estimating plant traits of grasslands from UAV–acquired hyperspectral images: a comparison of statistical approaches. ISPRS International Journal of Geo–Information. 2015;4(4):2792–820. https://doi.org/10.3390/ijgi4042792
  50. Simelli I, Tsagaris A, editors. The Use of Unmanned Aerial Systems (UAS) in Agriculture. HAICTA; 2015.
  51. Colomina I, Molina P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 2014;92:79–97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
  52. Ni J, Yao L, Zhang J, Cao W, Zhu Y, Tai X. Development of an unmanned aerial vehicle–borne crop–growth monitoring system. Sensors. 2017;17(3):502. https://doi.org/10.3390/s17030502
  53. Ma Z, Zhu X, Zhou Z, Zou X, Zhao X. A lateral–directional control method for high aspect ratio full–wing UAV and flight tests. Applied Sciences. 2019;9(20):4236. https://doi.org/10.3390/app9204236
  54. Ennouri K, Kallel A. Remote sensing: an advanced technique for crop condition assessment. Mathematical Problems in Engineering. 2019;2019. https://doi.org/10.1155/2019/9404565
  55. Noulas C, Torabian S, Qin R. Crop nutrient requirements and advanced fertilizer management strategies. Agronomy. 2023; 13(8), p.2017. https://doi.org/10.3390/agronomy13082017
  56. Himanshu, Sharma S, Rana VS, Ankit, Thakur V, Kumar A, Prachi, Thakur S, Sharma N. Unlocking the sustainable role of melatonin in fruit production and stress tolerance: a review. CABI Agriculture and Bioscience. 2024 Nov 4;5(1):103.
  57. Dezordi LR, Aquino LAd, Aquino RFBdA, Clemente JM, Assunção NS. Diagnostic methods to assess the nutritional status of the carrot crop. Revista Brasileira de Ciência do Solo. 2016;40. https://doi.org/10.1590/18069657rbcs20140813
  58. Balasubramaniam P, Ananthi V. Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C–means clustering algorithm. Nonlinear Dynamics. 2016;83:849–66. https://doi.org/10.1007/s11071–015–2372–y
  59. Jia L, Chen X, Zhang F, Buerkert A, Römheld V. Use of digital camera to assess nitrogen status of winter wheat in the northern China plain. Journal of Plant Nutrition. 2004;27(3):441–50. https://doi.org/10.1081/PLN–120028872
  60. Nauš J, Prokopová J, ?ebí?ek J, Špundová M. SPAD chlorophyll meter reading can be pronouncedly affected by chloroplast movement. Photosynthesis Research. 2010;105:265–71. https://doi.org/10.1007/s11120–010–9587–z
  61. Severtson D, Callow N, Flower K, Neuhaus A, Olejnik M, Nansen C. Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture. 2016;17:659–77. https://doi.org/10.1007/s11119–016–9442–0
  62. Yakushev V, Kanash E. Evaluation of wheat nitrogen status by colorimetric characteristics of crop canopy presented in digital images. Journal of Agricultural Informatics. 2016;7(1). https://doi.org/10.17700/jai.2016.7.1.268
  63. Berni JA, Zarco–Tejada PJ, Suárez L, Fereres E. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing. 2009;47(3):722–38. https://doi.org/10.1109/TGRS.2008.2010457
  64. Mogili UR, Deepak B. Review on application of drone systems in precision agriculture. Procedia Computer Science. 2018;133:502–9. https://doi.org/10.1016/j.procs.2018.07.063
  65. Chen CJ, Huang YY, Li YS, Chen YC, Chang CY, Huang YM. Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access. 2021;9:21986–97. https://doi.org/10.1109/access.2021.3056082
  66. Sarghini F, De Vivo A. Interference analysis of an heavy lift multirotor drone flow field and transported spraying system. Chemical Engineering Transactions. 2017;58:631–6.
  67. Kedari S, Lohagaonkar P, Nimbokar M, Palve G, Yevale P. Quadcopter–a smarter way of pesticide spraying. Imperial Journal of Interdisciplinary Research. 2016;2(6):1257–60.
  68. S. M V. Importance of Drones in Agriculture. In: Bholanath M, Sanjay KB and Subhrangsu S, editors. New Delhi: New publishing House. 2024. p. 112–28.
  69. Bongiovanni R, Lowenberg–DeBoer J. Precision agriculture and sustainability. Precision Agriculture. 2004;5:359–87. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa
  70. Talaviya T, Shah D, Patel N, Yagnik H, Shah M. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture. 2020;4:58–73. https://doi.org/10.1016/j.aiia.2020.04.002
  71. Dutta G, Goswami P. Application of drone in agriculture: A review. International Journal of Chemical Studies. 2020;8(5):181–7. https://doi.org/10.22271/chemi.2020.v8.i5d.10529
  72. M. Tahat M, M. Alananbeh K, A. Othman Y, I. Leskovar D. Soil health and sustainable agriculture. Sustainability. 2020;12(12):4859. https://doi.org/10.3390/su12124859
  73. Merwe D, Burchfield D, Witt T, Price K, Sharda A. Chapter OneDrones in agriculture. Adv Agron. 2020;162:1–30. https://doi.org/10.1016/bs.agron.2020.03.001
  74. Niu H, Zhao T, Wang D, Chen Y, editors. Estimating evapotranspiration with UAVs in agriculture: A review. 2019 ASABE Annual International Meeting; 2019: American Society of Agricultural and Biological Engineers. https://doi.org/10.20944/preprints201907.0124.v1
  75. Xia T, Kustas WP, Anderson MC, Alfieri JG, Gao F, McKee L, et al. Mapping evapotranspiration with high–resolution aircraft imagery over vineyards using one–and two–source modeling schemes. Hydrology and Earth System Sciences. 2016;20(4):1523–45. https://doi.org/10.20944/preprints201907.0124.v1
  76. Weiss M, Jacob F, Duveiller G. Remote sensing for agricultural applications: A meta–review. Remote Sensing of Environment. 2020;236:111402. https://doi.org/10.1016/j.rse.2019.111402
  77. Martos V, Ahmad A, Cartujo P, Ordoñez J. Ensuring agricultural sustainability through remote sensing in the era of agriculture 5.0. Applied Sciences. 2021;11(13):5911. https://doi.org/10.3390/app11135911
  78. Kamilaris A, Prenafeta–Boldú FX. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture. 2018;147:70–90. https://doi.org/10.1016/j.compag.2018.02.016
  79. Gopal R, Singh V, Aggarwal A. Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19. Education and Information Technologies. 2021;26(6):6923–47. https://doi.org/10.1007/s10639–021–10523–1
  80. Sishodia RP, Ray RL, Singh SK. Applications of remote sensing in precision agriculture: A review. Remote Sensing. 2020;12(19):3136. https://doi.org/10.3390/rs12193136
  81. Ziya A, Mehmet M, Yusuf Y. Determination of Sugar Beet Leaf Spot Disease Level (Cercospora beticola Sacc.) with Image Processing Technique by Using drone. Curr Inves Agri Curr Res 5 (3)–2018. 149–56. https://doi.org/10.32474/CIACR.2018.05.000214
  82. Dash JP, Watt MS, Pearse GD, Heaphy M, Dungey HS. Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing. 2017;131:1–14. https://doi.org/10.1016/j.isprsjprs.2017.07.007
  83. Calderón Madrid R, Navas Cortés JA, Lucena León C, Zarco–Tejada PJ. High–resolution hyperspectral and thermal imagery acquired from UAV platforms for early detection of Verticillium wilt using fluorescence, temperature and narrow–band indices. 2013. https://doi.org/10.1016/j.rse.2013.07.031
  84. Hardin PJ, Jensen RR. Small–scale unmanned aerial vehicles in environmental remote sensing: Challenges and opportunities. GIScience & Remote Sensing. 2011;48(1):99–111. https://doi.org/10.2747/1548–1603.48.1.99
  85. Manfreda S, McCabe MF, Miller PE, Lucas R, Pajuelo Madrigal V, Mallinis G, et al. On the use of unmanned aerial systems for environmental monitoring. Remote Sensing. 2018;10(4):641. https://doi.org/10.3390/rs10040641
  86. Huang Y, Hoffmann WC, Lan Y, Wu W, Fritz BK. Development of a spray system for an unmanned aerial vehicle platform. Applied Engineering in Agriculture. 2009;25(6):803–9. https://doi.org/10.13031/2013.29229
  87. Huang Y, Reddy KN, Fletcher RS, Pennington D. UAV low–altitude remote sensing for precision weed management. Weed Technology. 2018;32(1):2–6. https://doi.org/10.1017/wet.2017.89
  88. Yao L, Jiang Y, Zhiyao Z, Shuaishuai Y, Quan Q, editors. A pesticide spraying mission assignment performed by multi–quadcopters and its simulation platform establishment. 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC); 2016
  89. Li L, Fan Y, Huang X, Tian L, editors. Real–time UAV weed scout for selective weed control by adaptive robust control and machine learning algorithm. ASABE Annual International Meeting; 2016: American Society of Agricultural and Biological Engineers. 2016
  90. Malenovský Z, Lucieer A, King DH, Turnbull JD, Robinson SA. Unmanned aircraft system advances health mapping of fragile polar vegetation. Methods in Ecology and Evolution. 2017;8(12):1842–57. https://doi.org/10.1111/2041–210X.12833
  91. Ajakaiye OB. Drone Agricultural Technology: Implications for Sustainable Food Production in Africa. African Journal of Agricultural Science and Food Research. 2023;9(1):36–44.
  92. Rathod PD, Shinde GU. Autonomous Aerial System (UAV) for Sustainable Agriculture: A Review. International Journal of Environment and Climate Change. 2023;13(8):1343–55. https://doi.org/10.9734/ijecc/2023/v13i82080
  93. Mohsan SAH, Othman NQH, Li Y, Alsharif MH, Khan MA. Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends. Intelligent Service Robotics. 2023;16(1):109–37. https://doi.org/10.1007/s11370–022–00452–4
  94. Dutta G, Goswami P. Application of drone in agriculture: A review. International Journal of Chemical Studies. 2020;8:181–7. https://doi.org/10.22271/chemi.2020.v8.i5d.10529
  95. Elouarouar S, Medromi H, editors. Multi–Rotors Unmanned Aerial Vehicles Power Supply and Energy Management. E3S Web of Conferences; 2022: EDP Sciences. https://doi.org/10.1051/e3sconf/202233600068
  96. Jayasinghe SL, Thomas DT, Anderson JP, Chen C, Macdonald BC. Global Application of Regenerative Agriculture: A Review of Definitions and Assessment Approaches. Sustainability. 2023;15(22):15941. https://doi.org/10.3390/su152215941
  97. Dündar Ö, Bilici M, Ünler T. Design and performance analyses of a fixed wing battery VTOL UAV. Engineering Science and Technology, an International Journal. 2020;23(5):1182–93. https://doi.org/10.1016/j.jestch.2020.02.002
  98. Rajabi MS, Beigi P, Aghakhani S. Drone delivery systems and energy management: a review and future trends. Handbook of Smart Energy Systems. 2023:1–19. https://doi.org/10.1007/978–3–030–72322–4_196–1
  99. Emimi M, Khaleel M, Alkrash A. The current opportunities and challenges in drone technology. International Journal of Electrical Engineering and Sustainability. 2023:74–89.
  100. Singh P. Drones in Indian Agriculture: Trends, Challenges, and Policy Implications2023.
  101. Yang JM, Yu PT, Kuo BC. A nonparametric feature extraction and its application to nearest neighbor classification for hyperspectral image data. IEEE Transactions on Geoscience and Remote Sensing. 2009;48(3):1279–93. https://doi.org/10.1109/TGRS.2009.2031812
  102. Islam N, Rashid MM, Pasandideh F, Ray B, Moore S, Kadel R. A review of applications and communication technologies for internet of things (Iot) and unmanned aerial vehicle (uav) based sustainable smart farming. Sustainability. 2021;13(4):1821. https://doi.org/10.3390/su13041821
  103. Furlan LM, Moreira CA, de Alencar PG, Rosolen V. Environmental monitoring and hydrological simulations of a natural wetland based on high–resolution unmanned aerial vehicle data (Paulista Peripheral Depression, Brazil). Environmental Challenges. 2021;4:100146. https://doi.org/10.1016/j.envc.2021.100146
  104. Frouin RJ, Franz BA, Ibrahim A, Knobelspiesse K, Ahmad Z, Cairns B, et al. Atmospheric correction of satellite ocean–color imagery during the PACE era. Frontiers in Earth Science. 2019;7:145. https://doi.org/10.3389/feart.2019.00145
  105. D'Sa R, Jenson D, Henderson T, Kilian J, Schulz B, Calvert M, et al., editors. SUAV: Q–An improved design for a transformable solar–powered UAV. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2016: IEEE. https://doi.org/10.1109/IROS.2016.7759260
  106. Saeed AS, Younes AB, Cai C, Cai G. A survey of hybrid unmanned aerial vehicles. Progress in Aerospace Sciences. 2018;98:91–105. https://doi.org/10.1016/j.paerosci.2018.03.007
  107. Hunt ER, Cavigelli M, Daughtry CS, Mcmurtrey JE, Walthall CL. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture. 2005;6:359–78. https://doi.org/10.1007/s11119–005–2324–5
  108. Kallimani C, Heidarian R, van Evert FK, Rijk B, Kooistra L. UAV–based Multispectral & Thermal dataset for exploring the diurnal variability, radiometric & geometric accuracy for precision agriculture. Open Data Journal for Agricultural Research. 2020;6:1–7. https://doi.org/10.18174/odjar.v6i0.16317
  109. Stöcker C, Bennett R, Nex F, Gerke M, Zevenbergen J. Review of the current state of UAV regulations. Remote sensing. 2017;9(5):459. https://doi.org/10.3390/rs9050459
  110. Memisoglu O. Justification of Civilian Use of Drones and International Security: Comparison between the The United States and the European Union. 2019.
  111. Aviation MoC. Annual Report 2022. Available from: https://www.civilaviation.gov.in/sites/default/files/2023-07/Annual%20Report%20of%20MoCA%202022-2023%20English.pdf
  112. Pathak H, Kumar G, Mohapatra S, Gaikwad B, Rane J. Use of drones in agriculture: Potentials, Problems and Policy Needs. ICAR–National Institute of Abiotic Stress Management. 2020;300:4–15.
  113. Altawy R, Youssef AM. Security, privacy, and safety aspects of civilian drones: A survey. ACM Transactions on Cyber–Physical Systems. 2016;1(2):1–25. https://doi.org/10.1145/3001836
  114. Singh P, Singh P. Drones in Indian Agriculture: Trends, Challenges, and Policy Implications. 2023.
  115. Rajagopalan RP, Krishna R. Drones: Guidelines, regulations, and policy gaps in India. 2018.

Downloads

Download data is not yet available.