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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2023

13.07.2023

Techniques, Answers, and Real-World UAV Implementations for Precision Farming

verfasst von: Ashish Srivastava, Jay Prakash

Erschienen in: Wireless Personal Communications | Ausgabe 4/2023

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Abstract

In adopting state-of-the-art technologies, a domain known in time is Agriculture for fertility optimization, expense saving, assistance, and environmental safeguard. In this aspect, deploying UAVs remains a modern example in the Agriculture sector, encompassing various possibilities at ease. Concerning innovations, UAV (drone) invention remains the standard talked-about technology. UAV’s broader view includes drone ranges like micro, mini, small, and medium aerial vehicles. Initially developed applications for the military now have used assistance like firefighting, courier services, mob surveillance, facial recognition, and many more. Within the paper, UAV applications in agriculture are of primary interest, with a notable centre of attention being crop farming. This paper presents a comprehensive survey on UAV types, crop health, agricultural sensors, remote sensing with UAVs, animal marking, pesticide sprinkling, other possible agricultural use, and Precision Agriculture. We explore the approach utilized for each UAV type application and the UAV technical characteristics and payload. Beyond uses, UAV’s services and implied advantages within agriculture are further exhibited beside talks on business correlated hurdles and additional apparent difficulties limiting the broad adoption of UAVs into agriculture. The belief of the work done in the paper will prove worthwhile to Researchers working on an amalgamation of UAVs in PA. With our work, they can make a necessary spontaneous understanding of the agricultural aspect and how it should work. Those farmers attempting modernized agricultural method optimization approaches on various levels by this work benefit from new ways of using UAVs within their farming. UAV businesses exploring innovative UAV use capacities can examine the future concerning the UAV run in agriculture furthermore strengthen their endeavours within the trend.
Vorheriger Artikel Reinforcement Learning Based Active Attack Detection and Blockchain Technique to Protect the Data from the Passive Attack in the Autonomous Mobile Network
Nächster Artikel TDOA/AOA Hybrid Localization Based on Improved Dandelion Optimization Algorithm for Mobile Location Estimation Under NLOS Simulation Environment

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Literatur
1.
Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712.
2.
Pierce, F. J., & Nowak, P. (1999). Aspects of precision agriculture. Advances in Agronomy, 67, 1–85.
3.
R. Raj, S. Kar, R. Nandan, A. Jagarlapudi, Precision agriculture and unmanned aerial vehicles (uavs), in: Unmanned aerial vehicle: Applications in agriculture and environment, Springer, 2020, pp. 7–23.
4.
Carolan, M. (2017). Publicising food: Big data, precision agriculture, and co-experimental techniques of addition. Sociologia Ruralis, 57(2), 135–154.
5.
6.
Lillesand, T. Kiefer, R. W. Chipman, J. (2015) Remote sensing and image interpretation, John Wiley & Sons.
7.
Pajares, G. (2015). Overview and current status of remote sensing applications based on unmanned aerial vehicles (uavs). Photogrammetric Engineering & Remote Sensing, 81(4), 281–330.
8.
Gongal, A., Amatya, S., Karkee, M., Zhang, Q., & Lewis, K. (2015). Sensors and systems for fruit detection and localization: A review. Computers and Electronics in Agriculture, 116, 8–19.
9.
Chen, S., Laefer, D. F., & Mangina, E. (2016). State of technology review of civilian uavs. Recent Patents on Engineering, 10(3), 160–174.
10.
Brisco, B., Brown, R., Hirose, T., McNairn, H., & Staenz, K. (1998). Precision agriculture and the role of remote sensing: A review. Canadian Journal of Remote Sensing, 24(3), 315–327.
11.
Xue, J., Su, B. (2017) Significant remote sensing vegetation indices: A review of developments and applications, Journal of sensors 2017.
12.
Tucker, C. J., Slayback, D. A., Pinzon, J. E., Los, S. O., Myneni, R. B., & Taylor, M. G. (2001). Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. International Journal of Biometeorology, 45(4), 184–190.
13.
Townshend, J. R., & Justice, C. (1986). Analysis of the dynamics of african vegetation using the normalized difference vegetation index. International Journal of Remote Sensing, 7(11), 1435–1445.
14.
Fulton, J. P. Port, K., Shannon, D., Clay, D., Kitchen, N. (2018) Precision agriculture data management, Precision Agriculture Basics (precisionagbasics)169–188.
15.
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37.
16.
Suakanto, S., Engel, V. J., Hutagalung, M., Angela, D. Sensor networks data acquisition and task management for decision support of smart farming, in: 2016 International conference on information technology systems and innovation (ICITSI), IEEE, 2016, pp. 1–5.
17.
Khanna, A., & Kaur, S. (2019). Evolution of internet of things (iot) and its significant impact in the field of precision agriculture. Computers and Electronics in Agriculture, 157, 218–231.
18.
Zamora-Izquierdo, M. A., Santa, J., Martínez, J. A., Martínez, V., & Skarmeta, A. F. (2019). Smart farming iot platform based on edge and cloud computing. Biosystems Engineering, 177, 4–17.
19.
Giacomin, J. C., Vasconcelos, F. H. (2006) Wireless sensor network as a measurement tool in precision agriculture, in: XVIII IMEKO WORLD CONGRESS-metrology for a sustainable development, pp. 17–22.
20.
Persson, D., Andersson, J. Real-time image processing on handheld devices and uav (2016).
21.
Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778–782.
22.
Trogo, R., Ebardaloza, J. B., Sabido, D. J., Bagtasa, G., Tongson, E., Balderama, O., & Sms-based smarter agriculture decision support system for yellow corn farmers in isabela, in,. (2015). IEEE Canada international humanitarian technology conference (IHTC2015). IEEE, 2015, 1–4.
23.
Tellaeche, A., Burgos Artizzu, X. P., Pajares, G., Ribeiro, A., Fernández-Quintanilla, C. (2008) A new vision-based approach to differential spraying in precision agriculture, Computers and Electronics in Agriculture, 60(2): 144–155.
24.
G. Sylvester, E-agriculture in action: Drones for agriculture, Food and Agriculture Organization of the United Nations and International, 2018.
25.
Tsouros, D. C., Bibi, S., & Sarigiannidis, P. G. (2019). A review on uav-based applications for precision agriculture. Information, 10(11), 349.
26.
Sugiura, R. Noguchi, N., Ishii, K., Terao, H. (2002) The development of remote sensing system using unmanned helicopter, in: Automation technology for off-road equipment proceedings of the 2002 conference, American Society of Agricultural and Biological Engineers, 2002, p. 120.
27.
Fukagawa, T., Ishii, K., Noguchi, N., Terao, H. (2003) Detecting crop growth by a multi-spectral imaging sensor, in: 2003 ASAE Annual Meeting. American Society of Agricultural and Biological Engineers, 1.
28.
Xiang, H., Tian, L., (2006) Development of autonomous unmanned helicopter based agricultural remote sensing system, in: 2006 ASAE annual meeting American society of agricultural and biological engineers, 2006, 1.
29.
Yang, C., Fernandez, C. J., Everitt, J. H., (2009) Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot, in: 2009 Reno, Nevada, June 21-June 24, 2009. American society of agricultural and biological engineers, 2009, 1.
30.
Hung, C., Bryson, M., & Sukkarieh, S. (2012). Multi-class predictive template for tree crown detection. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 170–183.
31.
Swain, K. C., Thomson, S. J., & Jayasuriya, H. P. (2010). Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. Transactions of the ASABE, 53(1), 21–27.
32.
Aldana-Jague, E., Heckrath, G., Macdonald, A., van Wesemael, B., & Van Oost, K. (2016). Uas-based soil carbon mapping using vis-nir (480–1000 nm) multi-spectral imaging: Potential and limitations. Geoderma, 275, 55–66.
33.
Huang, Y., Reddy, K. N., Fletcher, R. S., & Pennington, D. (2018). Uav low-altitude remote sensing for precision weed management. Weed Technology, 32(1), 2–6.
34.
Di Martini, D. R., Tetila, E. C., Junior, J. M., Matsubara, E. T. Siqueira, H., de Castro Junior, A. A. Araujo, M. S. Monteiro, C. H., Pistori, H., Liesenberg, V. (2019) Machine learning applied to uav imagery in precision agriculture and forest monitoring in brazililian savanah, in: IGARSS 2019-2019 IEEE International geoscience and remote sensing symposium, IEEE, 2019, pp. 9364–9367.
35.
Costa, F. G., Ueyama, J., Braun, T., Pessin, G., Osório, F. S., Vargas, P. A. (2012) The use of unmanned aerial vehicles and wireless sensor network in agricultural applications, in: 2012IEEE international geoscience and remote sensing symposium. IEEE, 2012, 5045–5048.
36.
Arnold, T. De Biasio, M., Fritz, A., Leitner, R. (2013) Uav-based measurement of vegetation indices for environmental monitoring, in: 2013 Seventh international conference on sensing technology (ICST), IEEE, 2013, pp. 704–707.
37.
Ju, C., & Son, H. I. (2018). Multiple uav systems for agricultural applications: Control, implementation, and evaluation. Electronics, 7(9), 162.
38.
Skobelev, P., Budaev, D., Gusev, N., Voschuk, G. (2018) Designing multi-agent swarm of uav for precise agriculture, in: International conference on practical applications of agents and multi-agent systems, Springer, pp. 47–59.
39.
De Rango, F., Potrino, G., Tropea, M., Santamaria, A. F., & Fazio, P. (2019). Scalable and ligthway bio-inspired coordination protocol for fanet in precision agriculture applications. Computers & Electrical Engineering, 74, 305–318.
40.
Pederi, Y., Cheporniuk, H. (2015) Unmanned aerial vehicles and new technological methods of monitoring and crop protection in precision agriculture, in: IEEE International conference actual problems of unmanned aerial vehicles developments (APUAVD). IEEE, 2015, 298–301.
41.
Devraj, R., Deep, V. (2015) Expert systems for management of insect-pests in pulse crop, in: 2nd Int. conference on computing for sustainable global development (INDIACom), 2015, pp. 1144–1150.
42.
Mogili, U. R., & Deepak, B. (2018). Review on application of drone systems in precision agriculture. Procedia Computer Science, 133, 502–509.
43.
Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., & Guizani, M. (2019). Unmanned aerial vehicles (uavs): A survey on civil applications and key research challenges. IEEE Access, 7, 48572–48634.
44.
Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., et al. (2017). Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Frontiers in plant science, 8, 1111.
45.
Aslan, M. F., Durdu, A., Sabanci, K., Ropelewska, E., & Gültekin, S. S. (2022). A comprehensive survey of the recent studies with uav for precision agriculture in open fields and greenhouses. Applied Sciences, 12(3), 1047.
46.
Velusamy, P., Rajendran, S., Mahendran, R. K., Naseer, S., Shafiq, M., & Choi, J.-G. (2021). Unmanned aerial vehicles (uav) in precision agriculture: Applications and challenges. Energies, 15(1), 217.
47.
Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., & Sun, X. (2021). A technical study on uav characteristics for precision agriculture applications and associated practical challenges. Remote Sensing, 13(6), 1204.
48.
Liaghat, S., Balasundram, S. K., et al. (2010). A review: The role of remote sensing in precision agriculture. American Journal of Agricultural and Biological Sciences, 5(1), 50–55.
49.
Tokekar, P., Vander Hook, J., Mulla, D., & Isler, V. (2016). Sensor planning for a symbiotic uav and ugv system for precision agriculture. IEEE Transactions on Robotics, 32(6), 1498–1511.
50.
Comparetti, A. (2011) Precision agriculture: Past, present and future, in: International scientific conference AGRICULTURAL ENGINEERING AND ENVIRONMENT-2011, Aleksandras Stulginskis University, 2011, pp. 216–230.
51.
Tuyishimire, E., Bagula, A., Rekhis, S., Boudriga, N. (2017) Cooperative data muling from ground sensors to base stations using uavs, in: 2017 IEEE symposium on computers and communications (ISCC). IEEE, 2017, 35–41.
52.
Quaritsch, M., Kruggl, K., Wischounig-Strucl, D., Bhattacharya, S., Shah, M., & Rinner, B. (2010). Networked uavs as aerial sensor network for disaster management applications. e & i Elektrotechnik und Informationstechnik, 127(3), 56–63.
53.
54.
Edokossi, K., Calabia, A., Jin, S., & Molina, I. (2020). Gnss-reflectometry and remote sensing of soil moisture: A review of measurement techniques, methods, and applications. Remote Sensing, 12(4), 614.
55.
Zhang, J., He, L., Karkee, M., Zhang, Q., Zhang, X., & Gao, Z. (2018). Branch detection for apple trees trained in fruiting wall architecture using depth features and regions-convolutional neural network (r-cnn). Computers and Electronics in Agriculture, 155, 386–393.
56.
Yasin, J. N., Mohamed, S. A., Haghbayan, M.-H., Heikkonen, J., Tenhunen, H., & Plosila, J. (2020). Unmanned aerial vehicles (uavs): Collision avoidance systems and approaches. IEEE access, 8, 105139–105155.
57.
Reddy, T., RM, S. P., Parimala, M., Chowdhary, C. L., Hakak, S., & Khan, W. Z. (2020). A deep neural networks based model for uninterrupted marine environment monitoring. Computer Communications, 157, 64–75.
58.
Matolak, D. W., Sun, R. (2014) Initial results for air-ground channel measurements & modeling for unmanned aircraft systems: Over-sea, in: (2014) IEEE aerospace conference. IEEE, 2014, 1–15.
59.
Sun, R., & Matolak, D. W. (2016). Air-ground channel characterization for unmanned aircraft systems part ii: Hilly and mountainous settings. IEEE Transactions on Vehicular Technology, 66(3), 1913–1925.
60.
Matolak, D. W., & Sun, R. (2017). Air-ground channel characterization for unmanned aircraft systems-part iii: The suburban and near-urban environments. IEEE Transactions on Vehicular Technology, 66(8), 6607–6618.
61.
62.
Malik, A. W., Rahman, A. U., Qayyum, T., & Ravana, S. D. (2020). Leveraging fog computing for sustainable smart farming using distributed simulation. IEEE Internet of Things Journal, 7(4), 3300–3309.
63.
Lee, S.-W., & Mase, K. (2002). Activity and location recognition using wearable sensors. IEEE Pervasive Computing, 1(3), 24–32.
64.
Bayrakdar, M. E. (2020). Employing sensor network based opportunistic spectrum utilization for agricultural monitoring. Sustainable Computing: Informatics and Systems, 27, 100404.
65.
Salam, A. (2020) Internet of things in agricultural innovation and security, in: Internet of things for sustainable community development, Springer, pp. 71–112.
66.
Singh, R. K., Aernouts, M., De Meyer, M., Weyn, M., & Berkvens, R. (2020). Leveraging lorawan technology for precision agriculture in greenhouses. Sensors, 20(7), 1827.
67.
Alvar-Beltrán, J., Fabbri, C., Verdi, L., Truschi, S., Dalla Marta, A., & Orlandini, S. (2020). Testing proximal optical sensors on quinoa growth and development. Remote Sensing, 12(12), 1958.
68.
von Bueren, S. K., Burkart, A., Hueni, A., Rascher, U., Tuohy, M. P., & Yule, I. (2015). Deploying four optical uav-based sensors over grassland: Challenges and limitations. Biogeosciences, 12(1), 163–175.
69.
Singh, N., & Singh, A. N. (2020). Odysseys of agriculture sensors: Current challenges and forthcoming prospects. Computers and Electronics in Agriculture, 171, 105328.
70.
Nhamo, L., Ebrahim, G. Y., Mabhaudhi, T., Mpandeli, S., Magombeyi, M., Chitakira, M., Magidi, J., & Sibanda, M. (2020). An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing. Physics and Chemistry of the Earth, Parts A/B/C, 115, 102810.
71.
Allred, B., Martinez, L., Fessehazion, M. K., Rouse, G., Williamson, T. N., Wishart, D., Koganti, T., Freeland, R., Eash, N., Batschelet, A., et al. (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.
72.
Gadekallu, T. R., Rajput, D. S., Reddy, M. P. K., Lakshmanna, K. S. Bhattacharya, Singh, S., Jolfaei, A., Alazab, M. (2020) A novel pca–whale optimization-based deep neural network model for classification of tomato plant diseases using gpu, Journal of Real-Time Image Processing, 1–14.
73.
Cerro, J., Cruz Ulloa, C., & de León Rivas, J. (2021). Unmanned aerial vehicles in agriculture: A survey. Agronomy, 11(2), 203.
74.
Oettershagen, P., Stastny, T., Mantel, T., Melzer, A., Rudin, K., Gohl, P., Agamennoni, G. Alexis, K., Siegwart, R. (2016), Long-endurance sensing and mapping using a hand-launchable solar-powered uav, in: Field and Service Robotics, Springer, pp. 441–454.
75.
Czyba, R., Lemanowicz, M., Gorol, Z., Kudala, T., Construction prototyping, flight dynamics modeling, and aerodynamic analysis of hybrid vtol unmanned aircraft, Journal of Advanced Transportation 2018 (2018).
76.
Srivastava, A., & Prakash, J. (2021). Future fanet with application and enabling techniques: Anatomization and sustainability issues. Computer Science Review, 39, 100359.MathSciNet
77.
Wang, W., Guan, X., Wang, B., & Wang, Y. (2010). A novel mobility model based on semi-random circular movement in mobile ad hoc networks. Information Sciences, 180(3), 399–413.
78.
Bouachir, O., Abrassart, A., Garcia, F. et al (2014). A mobility model for uav ad hoc network [c] international conference on unmanned aircraft systems.
79.
Bouachir, O., Abrassart, A., Garcia, F., Larrieu, N. (2014) A mobility model for uav ad hoc network, in: 2014 International conference on unmanned aircraft systems (ICUAS). IEEE, 2014, 383–388.
80.
López, A., Jurado, J. M., Ogayar, C. J., & Feito, F. R. (2021). A framework for registering uav-based imagery for crop-tracking in precision agriculture. International Journal of Applied Earth Observation and Geoinformation, 97, 102274.
81.
Basiri, A., Mariani, V., Silano, G., Aatif, M., Iannelli, L., & Glielmo, L. (2022). A survey on the application of path-planning algorithms for multi-rotor uavs in precision agriculture. The Journal of Navigation, 75(2), 364–383.
82.
Singh, P. K., & Sharma, A. (2022). An intelligent wsn-uav-based iot framework for precision agriculture application. Computers and Electrical Engineering, 100, 107912.
83.
Peña, J. M., Torres-Sánchez, J., de Castro, A. I., Kelly, M., & López-Granados, F. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (uav) images. PloS One, 8(10), e77151.
84.
Torres-Sánchez, J., López-Granados, F., De Castro, A. I., & Peña-Barragán, J. M. (2013). Configuration and specifications of an unmanned aerial vehicle (uav) for early site specific weed management. PloS one, 8(3), e58210.
85.
Lv, M., Xiao, S., Yu, T., & He, Y. (2019). Influence of uav flight speed on droplet deposition characteristics with the application of infrared thermal imaging. International Journal of Agricultural and Biological Engineering, 12(3), 10–17.
86.
Yallappa, D., Veerangouda, M., Maski, D., Palled, V., Bheemanna, M., (2017) Development and evaluation of drone mounted sprayer for pesticide applications to crops, in: IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 2017, 1–7.
87.
Hentschke, M., Pignaton de Freitas, E., Hennig, C. H., & Girardi da Veiga, I. C. (2018). Evaluation of altitude sensors for a crop spraying drone. Drones, 2(3), 25.
88.
Xiongkui, H., Bonds, J., Herbst, A., & Langenakens, J. (2017). Recent development of unmanned aerial vehicle for plant protection in east asia. International Journal of Agricultural and Biological Engineering, 10(3), 18–30.
89.
Guo, T., Kujirai, T., & Watanabe, T. (2012). Mapping crop status from an unmanned aerial vehicle for precision agriculture applications, ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial. Information Sciences, 39, 485–490.
90.
Baluja, J., Diago, M. P., Balda, P., Zorer, R., Meggio, F., Morales, F., & Tardaguila, J. (2012). Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (uav). Irrigation Science, 30(6), 511–522.
91.
Khanal, S., Fulton, J., & Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22–32.
92.
Gonzalez-Dugo, V., Zarco-Tejada, P., Nicolás, E., Nortes, P. A., Alarcón, J., Intrigliolo, D. S., & Fereres, E. (2013). Using high resolution uav thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agriculture, 14(6), 660–678.
93.
Yue, J., Lei, T., Li, C., & Zhu, J. (2012). The application of unmanned aerial vehicle remote sensing in quickly monitoring crop pests. Intelligent Automation & Soft Computing, 18(8), 1043–1052.
94.
Chamoso, P., Raveane, W., Parra, V., González, A. (2014) Uavs applied to the counting and monitoring of animals, in: Ambient intelligence-software and applications, Springer, 2014, pp. 71–80.
95.
Chamoso, P., González-Briones, A., Rivas, A., Bueno De Mata, F., & Corchado, J. M. (2018). The use of drones in spain: Towards a platform for controlling uavs in urban environments. Sensors, 18(5), 1416.
96.
Havens, K. J., Sharp, E. J. (2015) Thermal imaging techniques to survey and monitor animals in the wild: a methodology, Academic Press, 2015.
97.
Vayssade, J.-A., Arquet, R., & Bonneau, M. (2019). Automatic activity tracking of goats using drone camera. Computers and Electronics in Agriculture, 162, 767–772.
98.
Yinka-Banjo, C., Ajayi, O. (2019) Sky-farmers: applications of unmanned aerial vehicles (uav) in agriculture, in: Autonomous Vehicles, IntechOpen, 2019, pp. 767–772.
99.
Webb, P., Mehlhorn, S. A., Smartt, P. (2017) Developing protocols for using a uav to monitor herd health, in: 2017 ASABE annual international meeting American society of agricultural and biological engineers, 1.
100.
Ma, Y. Selby, N., Adib, F. Drone relays for battery-free networks, in: Proceedings of the conference of the ACM special interest group on data communication, 2017, pp. 335–347.
101.
Nyamuryekung’e, S., Cibils, A. F., Estell, R. E., & Gonzalez, A. L. (2016). Use of an unmanned aerial vehicle- mounted video camera to assess feeding behavior of raramuri criollo cows. Rangeland ecology & management, 69(5), 386–389.
102.
103.
Hovhannisyan, T., Efendyan, P., & Vardanyan, M. (2018). Creation of a digital model of fields with application of dji phantom 3 drone and the opportunities of its utilization in agriculture. Annals of Agrarian Science, 16(2), 177–180.
104.
105.
Prakash, S., Kumar, M., Jat, D., Jyoti, B., Subeesh, A., Agrawal, K., Tiwari, P., Mehta, C., Singh, P., Singh, K. K. (2022) Applications of drones in agriculture: Status and scope, Research Gate (2022).
Metadaten
Titel
Techniques, Answers, and Real-World UAV Implementations for Precision Farming
verfasst von
Ashish Srivastava
Jay Prakash
Publikationsdatum
13.07.2023
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2023
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-023-10577-z

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Wireless Personal Communications 4/2023 Zur Ausgabe

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Analyzing the Performance of Nature-Inspired Optimization Algorithms with Modified Grey Wolf Optimization for VM Migration Problems

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TDOA/AOA Hybrid Localization Based on Improved Dandelion Optimization Algorithm for Mobile Location Estimation Under NLOS Simulation Environment

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A PDF Based Scale-Free Topology Construction Model for Wireless Sensor Networks

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