Quadcopters in Smart Agriculture: Applications and Modelling
Source abstract
Abstract
Despite technological growth and worldwide advancements in various fields, the agriculture sector continues to face numerous challenges such as desertification, environmental pollution, resource scarcity, and the excessive use of pesticides and inorganic fertilizers. These unsustainable problems in agricultural field can lead to land degradation, threaten food security, affect the economy, and put human health at risk. To mitigate these global issues, it is essential for researchers and agricultural professionals to promote advancements in smart agriculture by integrating modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensor Networks (WSNs), and more. Among these technologies, this paper focuses on UAVs, particularly quadcopters, which can assist in each phase of the agricultural cycle and improve productivity, quality, and sustainability. With their diverse capabilities, quadcopters have become the most widely used UAVs in smart agriculture and are frequently utilized by researchers in various projects. To explore the different aspects of quadcopters’ use in smart agriculture, this paper focuses on the following: (a) the unique advantages of quadcopters over other UAVs, including an examination of the quadcopter types particularly used in smart agriculture; (b) various agricultural missions where quadcopters are deployed, with examples highlighting their indispensable role; (c) the modelling of quadcopters, from configurations to the derivation of mathematical equations, to create a well-modelled system that closely represents real-world conditions; and (d) the challenges that must be addressed, along with suggestions for future research to ensure sustainable development. Although the use of UAVs in smart agriculture has been discussed in other papers, to the best of our knowledge, none have specifically examined the most popular among them, “quadcopters”, and their particular use in smart agriculture in terms of types, applications, and modelling techniques. Therefore, this paper provides a comprehensive survey of quadcopters’ use in smart agriculture and offers researchers and engineers valuable insights into this evolving field, presenting a roadmap for future enhancements and developments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: UAV Applications and Optimization · Smart Agriculture and AI · IoT Networks and Protocols
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Lab-STICC, UMR 8265 CNRS, ENSTA Bretagne, 29806 Brest Cedex 9, France
Department of Electrical Engineering, Faculty of Engineering, University of Balamand, Tripoli P.O. Box 100, Lebanon
References (100+)
Show source-provided references
1. Rehman, A., Saba, T., Kashif, M., Fati, S.M., Bahaj, S.A., and Chaudhry, H. (2022). A revisit of internet of things technologies for monitoring and control strategies in smart agriculture. Agronomy, 12.10.3390/agronomy12010127 ↗
2. Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., and Sun, X. (2021). A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sens., 13.10.3390/rs13061204 ↗
3. Eicher, C.K., and Staatz, J.M. (1998). International Agricultural Development, JHU Press.10.56021/9780801858789 ↗
4. Karam, K., Mansour, A., Khaldi, M., Clement, B., and Ammad, M. (2024, January 15–17). A Survey for Unmanned Aerial Vehicles in Smart Agriculture: Types and Modelling Perspectives. Proceedings of the 2024 IEEE 7th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.10.1109/IAEAC59436.2024.10503803 ↗
5. Darwin, B., Dharmaraj, P., Prince, S., Popescu, D.E., and Hemanth, D.J. (2021). Recognition of bloom/yield in crop images using deep learning models for smart agriculture: A review. Agronomy, 11.10.3390/agronomy11040646 ↗
6. Martinho, V.J.P.D., and Guine, R.d.P.F. (2021). Integrated-smart agriculture: Contexts and assumptions for a broader concept. Agronomy, 11.10.3390/agronomy11081568 ↗
7. Ciruela-Lorenzo, A.M., Del-Aguila-Obra, A.R., Padilla-Meléndez, A., and Plaza-Angulo, J.J. (2020). Digitalization of agri-cooperatives in the smart agriculture context. proposal of a digital diagnosis tool. Sustainability, 12.10.3390/su12041325 ↗
8. Haseeb, K., Ud Din, I., Almogren, A., and Islam, N. (2020). An energy efficient and secure IoT-based WSN framework: An application to smart agriculture. Sensors, 20.10.3390/s20072081 ↗
9. Quy, V.K., Hau, N.V., Anh, D.V., Quy, N.M., Ban, N.T., Lanza, S., Randazzo, G., and Muzirafuti, A. (2022). IoT-enabled smart agriculture: Architecture, applications, and challenges. Appl. Sci., 12.10.3390/app12073396 ↗
10. Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk10.1109/ACCESS.2019.2932609 ↗
11. Almalki, F.A., Soufiene, B.O., Alsamhi, S.H., and Sakli, H. (2021). A low-cost platform for environmental smart farming monitoring system based on IoT and UAVs. Sustainability, 13.10.3390/su13115908 ↗
12. Islam, N., Rashid, M.M., Pasandideh, F., Ray, B., Moore, S., and Kadel, R. (2021). A review of applications and communication technologies for internet of things (Iot) and unmanned aerial vehicle (uav) based sustainable smart farming. Sustainability, 13.10.3390/su13041821 ↗
13. Tsouros, D.C., Bibi, S., and Sarigiannidis, P.G. (2019). A review on UAV-based applications for precision agriculture. Information, 10.10.3390/info10110349 ↗
14. Popescu, D., Stoican, F., Stamatescu, G., Ichim, L., and Dragana, C. (2020). Advanced UAV–WSN system for intelligent monitoring in precision agriculture. Sensors, 20.10.3390/s20030817 ↗
15. Quadrotors unmanned aerial vehicles: A review
16. Sarris, Z., and Atlas, S. (2001, January 27–29). Survey of UAV applications in civil markets. Proceedings of the IEEE Mediterranean Conference on Control and Automation, Dubrovnik, Croatia.
17. Gupte, S., Mohandas, P.I.T., and Conrad, J.M. (2012, January 15–18). A survey of quadrotor unmanned aerial vehicles. Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, USA.10.1109/SECon.2012.6196930 ↗
18. Review of unmanned aircraft system (UAS)
19. A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control10.1109/MAES.2018.160246 ↗
20. An improved nonlinear multibody dynamic model for a parafoil-UAV system10.1109/ACCESS.2019.2943496 ↗
21. Practical model construction and stable control of an unmanned aerial vehicle with a parafoil-type wing10.1109/TSMC.2017.2707393 ↗
22. Mission-oriented miniature fixed-wing UAV swarms: A multilayered and distributed architecture10.1109/TSMC.2020.3033935 ↗
23. Velusamy, P., Rajendran, S., Mahendran, R.K., Naseer, S., Shafiq, M., and Choi, J.G. (2021). Unmanned Aerial Vehicles (UAV) in precision agriculture: Applications and challenges. Energies, 15.10.3390/en15010217 ↗
24. Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges10.1109/ACCESS.2019.2909530 ↗
25. An adaptive fast terminal sliding-mode controller with power rate proportional reaching law for quadrotor position and altitude tracking10.1109/TSMC.2021.3072099 ↗
26. Self-tuning sliding mode control for an uncertain coaxial octorotor UAV10.1109/TSMC.2022.3193377 ↗
27. Design and testing of a crop growth sensor aboard a fixed-wing unmanned aerial vehicle10.1016/j.compag.2022.106762 ↗
28. Development of unmanned aerial vehicle (UAV) fixed-wing for monitoring, mapping and dropping applications on agricultural land10.1088/1742-6596/2111/1/012051 ↗
29. Ammad-Udin, M., Mansour, A., Le Jeune, D., Aggoune, E.H.M., and Ayaz, M. (September, January 29). UAV routing protocol for crop health management. Proceedings of the 2016 24th European signal processing conference (EUSIPCO), Budapest, Hungary.10.1109/EUSIPCO.2016.7760562 ↗
30. Ammad Uddin, M., Mansour, A., Le Jeune, D., Ayaz, M., and Aggoune, E.H.M. (2018). UAV-assisted dynamic clustering of wireless sensor networks for crop health monitoring. Sensors, 18.10.3390/s18020555 ↗
31. Matouk, D., Ahmad, F., Kumar, P., Merzouki, R., Singh, M., and Abdessemed, F. (2018, January 24–26). Bond Graph Model-Based Control of the Quadcopter Dynamics. Proceedings of the 2018 7th International Conference on Systems and Control (ICSC), Valencia, Spain.10.1109/ICoSC.2018.8587627 ↗
32. Elruby, A.Y., El-Khatib, M., El-Amary, N.H., and Hashad, A. (2012, January 29–31). Dynamic modeling and control of quadrotor vehicle. Proceedings of the The International Conference on Applied Mechanics and Mechanical Engineering. Military Technical College, Cairo, Egypt.10.21608/amme.2012.37092 ↗
33. Hybrid PD-fuzzy and PD controllers for trajectory tracking of a quadrotor unmanned aerial vehicle: Autopilot designs and real-time flight tests
34. Neural network-based finite-time distributed formation-containment control of two-layer quadrotor UAVs10.1109/TSMC.2021.3103013 ↗
35. Budnyaev, V.A., Filippov, I.F., Vertegel, V.V., and Dudnikov, S.Y. (2020, January 10–11). Simulink-based quadcopter control system model. Proceedings of the 2020 1st International Conference Problems of Informatics, Electronics, and Radio Engineering (PIERE), Novosibirsk, Russia.10.1109/PIERE51041.2020.9314676 ↗
36. (2024, August 01). Agriculture Drones Market. Available online: https://www.fortunebusinessinsights.com/agriculture-drones-market-102589.
37. (2024, August 01). Agriculture Drones Market. Available online: https://www.marketsandmarkets.com/Market-Reports/agriculture-drones-market-23709764.html.
38. Cano, E., Horton, R., Liljegren, C., and Bulanon, D.M. (2017). Comparison of small unmanned aerial vehicles performance using image processing. J. Imaging, 3.10.3390/jimaging3010004 ↗
39. Nintanavongsa, P., Yaemvachi, W., and Pitimon, I. (2016, January 24–27). A self-sustaining unmanned aerial vehicle routing protocol for smart farming. Proceedings of the 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Phuket, Thailand.10.1109/ISPACS.2016.7824758 ↗
40. Alzate, D.F., Fajardo, A.E., Santa, J.D., Álvarez, J., Alarcón, A., and Soto-Suárez, M. (October, January 24). NDVI index and K-means clustering in multispectral images to calculate the severity of affectation by Phytophtora infestans in potato crops. Proceedings of the DAAD Alumni Seminar 2017, Hue, Vietnam.
41. Φραγκoυλóπoυλoς, E.G. (2017). Agricultural Robotics and Automation Robot Collaboration for Precision Agriculture. [Ph.D. Thesis, University of Thessaly].
42. Creation of a digital model of fields with application of DJI phantom 3 drone and the opportunities of its utilization in agriculture10.1016/j.aasci.2018.03.006 ↗
43. UAV-based Wireless data collection from underground sensor nodes for precision agriculture10.3390/agriengineering5010022 ↗
44. Bulanon, D.M., and Fallahi, E. (2017, January 29–30). A Smart Vision System for Monitoring Specialty Crops. Proceedings of the Future Technologies Conference (FTC) 2017, Vancouver, BC, Canada.
45. Rao, A., Shao, H., and Yang, X. (2019, January 10–13). The design and implementation of smart agricultural management platform based on UAV and wireless sensor network. Proceedings of the 2019 IEEE 2nd International Conference on Electronics Technology (ICET), Chengdu, China.10.1109/ELTECH.2019.8839480 ↗
46. Ghimire, S. (2017). Monitoring Crop Health Growth and Its Stand Count Attributes Using UAV Based Precision Agriculture: A Study in Tropical Farmland of Thailand. [Ph.D. Thesis, Thammasat University].
47. Use of unmanned aerial vehicles for agricultural applications with emphasis on crop protection: Three novel case-studies
48. Gao, P., Zhang, Y., Zhang, L., Noguchi, R., and Ahamed, T. (2019). Development of a recognition system for spraying areas from unmanned aerial vehicles using a machine learning approach. Sensors, 19.10.3390/s19020313 ↗
49. 3D Robotics, Inc. (2024, August 20). IRIS+ Operation Manual. Available online: https://www.manualslib.com/manual/855670/3dr-IrisPlus.html?page=2#manual.
50. DJI Technology Co., Ltd. (2024, August 20). Phantom 3 Standard User Manual v1.4. Available online: https://dl.djicdn.com/downloads/phantom_3/User%20Manual/Phantom_3_Standard_User_Manual_v1.4_en.pdf.
51. DJI Technology Co., Ltd. (2024, August 20). DJI Matrice 100 User Manual v1.6. Available online: https://dl.djicdn.com/downloads/m100/M100_User_Manual_EN.pdf.
52. DJI Technology Co., Ltd. (2024, August 20). DJI Inspire 1 Pro User Manual v1.4. Available online: https://dl.djicdn.com/downloads/INSPIRE%201%20series/20201123/INSPIRE_1_PRO_User_Manual_20201123.pdf.
53. DJI Technology Co., Ltd. (2024, August 20). Phantom 4 User Manual v1.6. Available online: https://dl.djicdn.com/downloads/phantom_4/20170706/Phantom_4_User_Manual_v1.6.pdf.
54. DJI Technology Co., Ltd. (2024, August 20). DJI Matrice 200 Series User Manual v1.4. Available online: https://dl.djicdn.com/downloads/M200/20201120/Matrice_210_210_RTK_User_Manual_EN_20201120.pdf.
55. DJI Technology Co., Ltd. (2024, August 20). DJI Mavic 2 PRO/ZOOM User Manual v2.2. Available online: https://dl.djicdn.com/downloads/Mavic_2/Mavic_2_Pro_Zoom_User_Manual_v2.2_en.pdf.
56. Bradford, C. (2024, August 01). Unsplash. Available online: https://unsplash.com/photos/dji-phantom-drone-flying-midair-l1Zt6OyfNfo.
57. Aerial visual perception in smart farming: Field study of wheat yellow rust monitoring10.1109/TII.2020.2979237 ↗
58. Der Yang, M., Tseng, H.H., Hsu, Y.C., and Tseng, W.C. (2020, January 10–13). Real-time crop classification using edge computing and deep learning. Proceedings of the 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA.10.1109/CCNC46108.2020.9045498 ↗
59. Bento, N.L., Ferraz, G.A.e.S., Amorim, J.d.S., Santana, L.S., Barata, R.A.P., Soares, D.V., and Ferraz, P.F.P. (2023). Weed detection and mapping of a coffee farm by a remotely piloted aircraft system. Agronomy, 13.10.3390/agronomy13030830 ↗
60. Christiansen, M.P., Laursen, M.S., Jørgensen, R.N., Skovsen, S., and Gislum, R. (2017). Designing and testing a UAV mapping system for agricultural field surveying. Sensors, 17.10.3390/s17122703 ↗
61. Yeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A., and Landivar, J. (2019). Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture. Remote Sens., 11.10.3390/rs11131548 ↗
62. Jemaa, H., Bouachir, W., Leblon, B., LaRocque, A., Haddadi, A., and Bouguila, N. (2023). UAV -based computer vision system for orchard apple tree detection and health assessment. Remote Sens., 15.10.3390/rs15143558 ↗
63. Hegarty-Craver, M., Polly, J., O’Neil, M., Ujeneza, N., Rineer, J., Beach, R.H., Lapidus, D., and Temple, D.S. (2020). Remote crop mapping at scale: Using satellite imagery and UAV-acquired data as ground truth. Remote Sens., 12.10.3390/rs12121984 ↗
64. Kumar, A., Shreeshan, S., Tejasri, N., Rajalakshmi, P., Guo, W., Naik, B., Marathi, B., and Desai, U. (2020, January 1–4). Identification of water-stressed area in maize crop using uav based remote sensing. Proceedings of the 2020 IEEE India geoscience and remote sensing symposium (InGARSS), Ahmedabad, India.10.1109/InGARSS48198.2020.9358930 ↗
65. Tejasri, N., Pachamuthu, R., Naik, B., and Desai, U.B. (2023, January 13). Intelligent drought stress monitoring on spatio-spectral-temporal drone based crop imagery using deep networks. Proceedings of the 2nd AAAI Workshop on AI for Agriculture and Food Systems, Washington, DC, USA.
66. McGhee, S. (2024, August 01). Unsplash. Available online: https://unsplash.com/photos/quadcopter-drone-flying-in-mid-air-during-daytime-UC5FpqofFOk.
67. Nhamo, L., Van Dijk, R., Magidi, J., Wiberg, D., and Tshikolomo, K. (2018). Improving the accuracy of remotely sensed irrigated areas using post-classification enhancement through UAV capability. Remote Sens., 10.10.3390/rs10050712 ↗
68. Yang, C.Y., Yang, M.D., Tseng, W.C., Hsu, Y.C., Li, G.S., Lai, M.H., Wu, D.H., and Lu, H.Y. (2020). Assessment of rice developmental stage using time series UAV imagery for variable irrigation management. Sensors, 20.10.3390/s20185354 ↗
69. Risal, A., Niu, H., Landivar-Scott, J.L., Maeda, M.M., Bednarz, C.W., Landivar-Bowles, J., Duffield, N., Payton, P., Pal, P., and Lascano, R.J. (2024). Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains. Water, 16.10.3390/w16091300 ↗
70. Li, X., Ba, Y., Zhang, M., Nong, M., Yang, C., and Zhang, S. (2022). Sugarcane nitrogen concentration and irrigation level prediction based on UAV multispectral imagery. Sensors, 22.10.3390/s22072711 ↗
71. Bannari, A., Selouani, A., El-Basri, M., Rhinane, H., El-Harti, A., and El-Ghmari, A. (2021, January 11–16). Multi-scale analysis of DEMS derived from unmanned aerial vehicle (UAV) in precision agriculture context. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.10.1109/IGARSS47720.2021.9554020 ↗
72. Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., and Stachniss, C. (June, January 29). UAV-based crop and weed classification for smart farming. Proceedings of the 2017 IEEE international conference on robotics and automation (ICRA), Singapore.10.1109/ICRA.2017.7989347 ↗
73. A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard10.1016/j.rse.2020.111679 ↗
74. SPAGRI-AI: Smart precision agriculture dataset of aerial images at different heights for crop and weed detection using super-resolution10.1016/j.agsy.2024.103876 ↗
75. Tagarakis, A.C., Filippou, E., Kalaitzidis, D., Benos, L., Busato, P., and Bochtis, D. (2022). Proposing UGV and UAV systems for 3D mapping of orchard environments. Sensors, 22.10.3390/s22041571 ↗
76. Catania, P., Roma, E., Orlando, S., and Vallone, M. (2023). Evaluation of Multispectral Data Acquired from UAV Platform in Olive Orchard. Horticulturae, 9.10.3390/horticulturae9020133 ↗
77. Hassan, S.I., Alam, M.M., Zia, M.Y.I., Rashid, M., Illahi, U., and Su’ud, M.M. (2022). Rice crop counting using aerial imagery and GIS for the assessment of soil health to increase crop yield. Sensors, 22.10.3390/s22218567 ↗
78. Ye, H., Huang, W., Huang, S., Cui, B., Dong, Y., Guo, A., Ren, Y., and Jin, Y. (2020). Recognition of banana fusarium wilt based on UAV remote sensing. Remote Sens., 12.10.3390/rs12060938 ↗
79. Megat Mohamed Nazir, M.N., Terhem, R., Norhisham, A.R., Mohd Razali, S., and Meder, R. (2021). Early monitoring of health status of plantation-grown eucalyptus pellita at large spatial scale via visible spectrum imaging of canopy foliage using unmanned aerial vehicles. Forests, 12.10.3390/f12101393 ↗
80. Garza, B.N., Ancona, V., Enciso, J., Perotto-Baldivieso, H.L., Kunta, M., and Simpson, C. (2020). Quantifying citrus tree health using true color UAV images. Remote Sensing, 12.10.3390/rs12010170 ↗
81. Freeman, B. (2024, August 01). Unsplash. Available online: https://unsplash.com/photos/white-and-red-drone-flying-during-daytime-BFqKcWMEXP4.
82. Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence10.1016/j.compag.2020.105457 ↗
83. Unmanned aerial vehicle (UAV) imaging and machine learning applications for plant phenotyping10.1016/j.compag.2023.108064 ↗
84. Lambertini, A., Mandanici, E., Tini, M.A., and Vittuari, L. (2022). Technical challenges for multi-temporal and multi-sensor image processing surveyed by UAV for mapping and monitoring in precision agriculture. Remote Sens., 14.10.3390/rs14194954 ↗
85. UAV time-series imagery show diversity treatment effects on cabbage growth10.1016/j.atech.2024.100443 ↗
86. Atanasov, A., Bankova, A., and Zhecheva, G. (2023). Observation of the vegetation processes of agricultural crops using small unmanned aerial vehicles in Dobrudja region. Bulg. J. Agric. Sci., 29.
87. Atanasov, A., and Bankova, A. (2024, January 27–29). The influence of location accuracy on the estimation of crops with a budget UAV in Dobrudja. Proceedings of the 2024 9th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE), Ruse, Bulgaria.10.1109/EEAE60309.2024.10600617 ↗
88. Possibilities of small robotic UAVS for surveillance of agricultural areas in Southern Dobruja10.61308/TAGD6115 ↗
89. Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields10.1016/j.compag.2022.107388 ↗
90. McCarthy, C., Nyoni, Y., Kachamba, D.J., Banda, L.B., Moyo, B., Chisambi, C., Banfill, J., and Hoshino, B. (2023). Can drones help smallholder farmers improve agriculture efficiencies and reduce food insecurity in Sub-Saharan Africa? Local perceptions from Malawi. Agriculture, 13.10.3390/agriculture13051075 ↗
91. Du, X., Huang, D., Dai, L., and Du, X. (2024). Recognition of Plastic Film in Terrain-Fragmented Areas Based on Drone Visible Light Images. Agriculture, 14.10.3390/agriculture14050736 ↗
92. Ichim, L., Ciciu, R., and Popescu, D. (2022, January 17–22). Using drones and deep neural networks to detect halyomorpha halys in ecological orchards. Proceedings of the IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.10.1109/IGARSS46834.2022.9883742 ↗
93. Hobba, B., Akıncı, S., and Göktogan, A.H. (2021, January 6–8). Efficient herbicide spray pattern generation for site-specific weed management practices using semantic segmentation on UAV imagery. Proceedings of the Australasian Conference on Robotics and Automation (ACRA-2021), Online.
94. Using the Mavic 2 Pro drone for basic water quality assessment
95. Buchhave, J. (2024, August 01). Unsplash. Available online: https://unsplash.com/photos/black-and-gray-drone-with-brown-heart-shaped-heart-shaped-pendant-lj00ODz01b4.
96. Long, D., McCarthy, C., and Jensen, T. (2016, January 12–15). Row and water front detection from UAV thermal-infrared imagery for furrow irrigation monitoring. Proceedings of the 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Banff, AB, Canada.10.1109/AIM.2016.7576783 ↗
97. Choudhury, T., Kaur, A., and Verma, U.S. (2016, January 29–30). Agricultural aid to seed cultivation: An Agribot. Proceedings of the 2016 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India.10.1109/CCAA.2016.7813860 ↗
98. Ajith, G., Bharadwaj, C.N., Naveen kumar, T.S., Nag, T.S., and Gururaj, C. (2018, January 11–12). UAV aided irrigation using object detection through wireless communication technology. Proceedings of the 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.10.1109/ICOEI.2018.8553787 ↗
99. Rao, V.P.S., and Rao, G.S. (2019, January 21–22). Design and modelling of anaffordable uav based pesticide sprayer in agriculture applications. Proceedings of the 2019 Fifth International Conference on Electrical Energy Systems (ICEES), Chennai, India.
100. Development of UAV technology in seed dropping for aerial revegetation practices in Indonesia10.1088/1755-1315/308/1/012051 ↗