Abstract
Today, we are using machine tools and a variety of technologies in almost all areas of agriculture. The drone is playing an important role in these techniques. Climate change and environmental pollution are the major global issues of the current era and severely impacting agricultural productivity. As seen, the current conditions are not favorable for Indian agriculture: first, the outbreak of corona epidemic and now the locust swarm can be seen. Working in crowded and far-flung areas is a difficult task during the the Covid pandemic. In view of these circumstances, bringing advanced changes in agriculture is becoming the need of the hour. The impact of ever-increasing technology on agriculture should be seen as a positive trend, as it can prove to be a useful means of sustenance for the growing population day by day.
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References
Bachrach, A.; De Winter, A.; He, R.; Hemann, G.; Prentice, S.; Roy, N. RANGE-Robust Autonomous Navigation in GPS-denied Environments. J. Field Robot. 2010, 28, 1096–1097.
Bellia, A.F.; Lanfranco, S. A Preliminary Assessment of the Efficiency of Using Drones in Land Cover Mapping. Xjenza 2019, 7, 18–27.
Brunner, C.; Peynot, T.; Vidal-Calleja, T.; Underwood, J. Selective combination of visual and thermal imaging for resilient localization in adverse conditions: Day and night, smoke and fire. J. Field Robot. 2013, 30, 641–666.
Cancela, J.J.; González, X.P.; Vilanova, M.; Mirás-Avalos, J.M. Water management using drones and satellites in agriculture. Water 2019, 11, 874.
Cunha, F.; Youcef-Toumi, K. Ultra-Wideband Radar for Robust Inspection Drone in Underground Coal Mines. In Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 21–25 May 2018; pp. 86–92.
Dube, T.; Sibanda, M.; Shoko, C. Examining the variability of small-reservoir water levels in semi-arid environments for integrated water management purposes, using remote sensing. Trans. R. Soc. S. Afr. 2016, 71, 115–119.
Eck, C.; Imbach, B. Aerial Magnetic Sensing With an Uav Helicopter. ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2012, XXXVIII-1/C22, 81–85.
Forooshani, A.E.; Bashir, S.; Michelson, D.G.; Noghanian, S. A survey of wireless communications and propagation modeling in underground mines. IEEE Commun. Surv. Tutorials 2013, 15, 1524–1545.
Guo, H.; Bai, D.; Chen, B.; Cao, Y.; Ju, F.; Qi, F.; Wang, Y. Continuum robot shape estimation using permanent magnets and magnetic sensors. Sensors Actuators A Phys. 2018, 285, 519–530.
Ismail, M. Remote Sensing as a Tool in Assessing Water Quality. Life Sci. J. 2012, 9, 246–252. 14. Campbell, G.; Phinn, S.R.; Dekker, A.G.; Brando, V.E. Remote sensing of water quality in an Australian tropical freshwater impoundment using matrix inversion and MERIS images. Remote Sens. Environ. 2011, 115, 2402–2414.
Jakob, S.; Gloaguen, R.; Laukamp, C. Remote sensing-based exploration of structurally-related mineralizations around Mount Isa, Queensland, Australia. Remote Sens. 2016, 8, 358.
Kalantar, B.; Mansor, S.B.; Sameen, M.I.; Pradhan, B.; Shafri, H.Z.M. Drone-based land-cover mapping using a fuzzy unordered rule induction algorithm integrated into object-based image analysis. Int. J. Remote Sens. 2017, 38, 2535–2556.
Lally, H.; O’Connor, I.; Jensen, O.; Graham, C. Can drones be used to conduct water sampling in aquatic environments? A review. Sci. Total Environ. 2019, 670, 569–575.
Lazzeri, G.; Frodella, W.; Rossi, G.; Moretti, S. Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy. Sensors 2021, 21, 3982.
Lee, Z.; Carder, K.L.; Arnone, R.A. Deriving inherent optical properties from water color: A multiband quasi-analytical algorithm for optically deep waters. Appl. Opt. 2002, 41, 5755–5772.
Lucieer, A.; Jong, S.M.D; Turner, D. Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Prog. Phys. Geogr. 2014, 38, 97–116.
Moseley, T.; Zabierek, G. Guidance on the Safe Use of Lasers in Education and Research, Aurpo Guidance, Note No. 7; Association of University Radiation Protection Officers. August 2012. Available online: https://www.gla.ac.uk/media/Media_418032_smxx.pdf (accessed on 8 July 2020).
Ovakoglou, G.; Alexandridis, T.K.; Crisman, T.L.; Skoulikaris, C.; Vergos, G.S. Use of MODIS satellite images for detailed lake morphometry: Application to basins with large water level fluctuations. Int. J. Appl. Earth Obs. Geoinf. 2016, 51, 37–46.
Ranjan, A.; Sahu, H.; Sahu, H.B. Communication Challenges in Underground Mines. Search Res. 2014, V, 23–29. 83. Pamela Drones Go Underground as Mining Applications Expand-Unmanned Systems Source. Available online: https://www.unmannedsystemssource.com/drones-go-underground-as-miningapplications-expand/ (accessed on 8 May 2020).
Rivard, B.; Harris, J.; Maloley, M.; White, H.P.; Peter, J.M.; Laakso, K.; Rogge, D. Application of Airborne, Laboratory, and Field Hyperspectral Methods to Mineral Exploration in the Canadian Arctic: Recognition and Characterization of Volcanogenic Massive Sulfide-Associated Hydrothermal Alteration in the Izok Lake Deposit Area, Nunavut. Econ. Geol. 2015, 110, 925–941.
Rossi, G.; Tanteri, L.; Tofani, V.; Vannocci, P.; Moretti, S.; Casagli, N. Multitemporal UAV surveys for landslide mapping and characterization. Landslides 2018, 15, 1045–1052.
Ruwaimana, M.; Satyanarayana, B.; Otero, V.; Muslim, A.M.; Syafiq, A.M.; Ibrahim, S.; Raymaekers, D.; Koedam, N.; Dahdouh-Guebas, F. The advantages of using drones over space-borne imagery in the mapping of mangrove forests. PLoS ONE 2018, 13, e0200288.
Sallam, A.; Alharbi, A.B.; Usman, A.R.; Hussain, Q.; Ok, Y.S.; Alshayaa, M.; Al-Wabel, M. Environmental consequences of dam construction: A case study from Saudi Arabia. Arab. J. Geosci. 2018, 11, 1–12.
Santos, J.M.; Couceiro, M.S.; Portugal, D.; Rocha, R.P. A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM. J. Intell. Robot. Syst. Theory Appl. 2015, 80, 401–422.
Small, H. Co-citation in the scientific literature: A new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 1973, 24, 265–269.
Suo, C.; McGovern, E.; Gilmer, A. Coastal Dune Vegetation Mapping Using a Multispectral Sensor Mounted on an UAS. Remote Sens. 2019, 11, 1814.
Vanamburg, L.K.; Trlica, M.J.; Hoffer, R.M.; Weltz, M.A. Ground based digital imagery for grassland biomass estimation. Int. J. Remote Sens. 2006, 27, 939–950.
Waiser, T.H.; Morgan, C.L.S.; Brown, D.J.; Hallmark, C.T. In Situ Characterization of Soil Clay Content with Visible Near-Infrared Diffuse Reflectance Spectroscopy. Soil Sci. Soc. Am. J. 2007, 71, 389.
Xiang, T.-Z.; Xia, G.-S.; Zhang, L. Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects. IEEE Geosci. Remote Sens. Mag. 2019, 7, 29–63.
Zimmermann, R.; Brandmeier, M.; Andreani, L.; Mhopjeni, K.; Gloaguen, R. Remote sensing exploration of Nb-Ta-LREE-enriched carbonatite (Epembe/Namibia). Remote Sens. 2016, 8, 620.
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Kumar, A., Rani, M., Aishwarya, Kumar, P. (2022). Drone Technology in Sustainable Agriculture: The Future of Farming Is Precision Agriculture and Mapping. In: Kumar, A., Kumar, P., Singh, S.S., Trisasongko, B.H., Rani, M. (eds) Agriculture, Livestock Production and Aquaculture. Springer, Cham. https://doi.org/10.1007/978-3-030-93262-6_1
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