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Comprehensive review of agriculture spraying UAVs challenges and advances: modelling and control

Published online by Cambridge University Press:  07 August 2025

M. R. Kartal Open the ORCID record for M. R. Kartal [Opens in a new window] ,
D. Ignatyev Open the ORCID record for D. Ignatyev [Opens in a new window]  and
A. Zolotas Open the ORCID record for A. Zolotas [Opens in a new window]
M. R. Kartal*
Affiliation:
Centre for Autonomous and Cyber-Physical Systems, Cranfield University, Bedford, UK Department of Aeronautical Engineering, Erciyes University, Kayseri, Türkiye
D. Ignatyev
Affiliation:
Centre for Autonomous and Cyber-Physical Systems, Cranfield University, Bedford, UK
A. Zolotas
Affiliation:
Centre for Autonomous and Cyber-Physical Systems, Cranfield University, Bedford, UK
*
Corresponding author: M. R. Kartal; Email: m.r.kartal@erciyes.edu.tr
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Abstract

The integration of unmanned aerial vehicles (UAVs) into agriculture has emerged as a transformative approach to enhance resource efficiency and enable precision farming. UAVs are used for various agricultural tasks, including monitoring, mapping and spraying of pesticides, providing detailed data that support targeted and sustainable practices. However, effective deployment of UAVs in these applications faces complex control challenges. This paper presents a comprehensive review of UAVs in agricultural applications, highlighting the sophisticated control strategies required to address these challenges. Key obstacles, such as modelling inaccuracies, unstable centre of gravity (COG) due to shifting payloads, fluid sloshing within pesticide tanks and external disturbances like wind, are identified and analysed. The review delves into advanced control methodologies, with particular focus on adaptive algorithms, backstepping control and machine learning-enhanced systems, which collectively enhance UAV stability and responsiveness in dynamic agricultural environments. Through an in-depth examination of flight dynamics, stability control and payload adaptability, this paper highlights how UAVs can achieve precise and reliable operation despite environmental and operational complexities. The insights drawn from this review underscore the importance of integrating adaptive control frameworks and real-time sensor data processing, enabling UAVs to autonomously adjust to changing conditions and ensuring optimal performance in agriculture. Future research directions are proposed, advocating for the development of control systems that enhance UAV resilience, accuracy and sustainability. By addressing these control challenges, UAVs have the potential to significantly advance precision agriculture, offering practical and environmental benefits crucial to sustaining global food production demands.

Keywords

agriculturecontroldronemappingsensingmodelpesticidesprayUAV

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Survey Paper
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The Aeronautical Journal , First View , pp. 1 - 21
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© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Focus Group on ‘Artificial Intelligence (AI) and Internet of Things (IOT) for Digital Agriculture’ (FG-AI4A). ITU. https://www.itu.int/en/ITU-T/focusgroups/ai4a/Pages/default.aspx (accessed October 3, 2024).Google Scholar
Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T. and Moscholios, I. A compilation of UAV applications for precision agriculture, Comput. Ntwrks, 2020, 172, (January), p 107148. doi: 10.1016/j.comnet.2020.107148 Google Scholar
Moschetta, J.-M. and Namuduri, K. Introduction to UAV systems, in: Namuduri, K., Chaumette, S., Kim, J.H. and Sterbenz, J.P.G. (eds), UAV Networks and Communications. Cambridge: Cambridge University Press, 2017. pp 1–25.Google Scholar
Skryzpski, D.B. Unmanned aircraft systems for civilian missions, Brand Inst. Soc. Secur. Policy Pap., 2012, 1, pp 1–28. [Online]. www.bigs-potsdam.org Google Scholar
Wu, K.J., Gregory, T.S., Moore, J., Hooper, B., Lewis, D. and Tse, Z.T.H. Development of an indoor guidance system for unmanned aerial vehicles with power industry applications, IET Radar, Sonar Navig., 2016, 11, no. 1, pp 212–218. doi: 10.1049/iet-rsn.2016.0232 CrossRefGoogle Scholar
Shukla, A. and Karki, H. Application of robotics in onshore oil and gas industry – a review Part I, Rob. Autom. Syst., 2016, 75, pp 490–507. doi: 10.1016/j.robot.2015.09.012 CrossRefGoogle Scholar
Lu, M., Bagheri, M., James, A.P. and Phung, T. Wireless charging techniques for UAVs: a review, reconceptualization, and extension, IEEE Access, 2018, 6, pp 29865–29884. doi: 10.1109/ACCESS.2018.2841376 CrossRefGoogle Scholar
Sato, A. The RMAX Helicopter UAV. Aeronautic Operations Yamaha Motor Co., Ltd., VA, USA: National Technical Information Service, 2003.Google Scholar
Nowak, M., Flis, B. and Sikora, W. Environmental benefits of agricultural aviation development, Combust. Engines, 2025, 200, (1), pp 136–144. doi: 10.19206/CE-202210 CrossRefGoogle Scholar
DJI Agras T50. https://ag.dji.com/t50 (accessed on: 25 March 2025).Google Scholar
Ping, J.T.K., Ling, A.E., Quan, T.J. and Dat, C.Y. Generic unmanned aerial vehicle (UAV) for civilian application-A feasibility assessment and market survey on civilian application for aerial imaging, in 2012 IEEE Conference Sustainable Utilization and Development in Engineering and Technology (STUDENT) 2012 – Conference Book, no. October, 2012, pp 289–294. doi: 10.1109/STUDENT.2012.6408241 CrossRefGoogle Scholar
Jones, C.L., Weckler, P.R., Maness, N.O., Stone, M.L. and Jayasekara, R. Estimating water stress in plants using hyperspectral sensing, 2004, 2013, doi: 10.13031/2013.17087 CrossRefGoogle Scholar
Tripicchio, P., Satler, M., Dabisias, G., Ruffaldi, E. and Avizzano, C.A. Towards smart farming and sustainable agriculture with drones, Proc. – 2015 Int. Conf. Intell. Environ. IE 2015, 2015, pp 140–143. doi: 10.1109/IE.2015.29 CrossRefGoogle Scholar
Listopad, C.M.C.S., Drake, J.B., Masters, R.E. and Weishampel, J.F. Portable and airborne small footprint LiDAR: forest canopy structure estimation of fire managed plots, Remote Sens., 2011, 3, (7), pp 1284–1307. doi: 10.3390/rs3071284 CrossRefGoogle Scholar
Anthony, D., Elbaum, S., Lorenz, A. and Detweiler, C. On crop height estimation with UAVs, in International Conference on Intelligent Robots and Systems, 2014, pp 4805–4812. doi: 10.1109/IROS.2014.6943245 CrossRefGoogle Scholar
Venkata Subba Rao, P. and Gorantla, S.R. Design and modelling of an affordable UAV based pesticide sprayer in agriculture applications, in 5th International Conference Electrical Energy Systems ICEES, 2019, 2019, 360, (February), pp 1–4. doi: 10.1109/ICEES.2019.8719253 CrossRefGoogle Scholar
Sabour, M.H., Jafary, P. and Nematiyan, S. Applications and classifications of unmanned aerial vehicles: a literature review with focus on multi-rotors, Aeronaut. J., 2023, 127, (1309), pp 466–490. doi: 10.1017/aer.2022.75 CrossRefGoogle Scholar
Rahman, M.H., Sejan, M.A.S., Aziz, M.A., Tabassum, R., Baik, J.-I., Song, H.-K. A comprehensive survey of unmanned aerial vehicles detection and classification using machine learning approach: challenges, solutions, and future directions, Remote Sens., 2024, 16, p 879. doi: 10.3390/rs16050879 CrossRefGoogle Scholar
de Ruiter, A.H.J. and Owlia, S. Autonomous obstacle avoidance for fixed-wing unmanned aerial vehicles, Aeronaut. J., 2015, 119, (1221), pp 1415–1436. doi: 10.1017/S0001924000011325 CrossRefGoogle Scholar
Boon, M.A., Drijfhout, A.P. and Tesfamichael, S. Comparison of a fixed-wing and multi-rotor UAV for environmental mapping applications: a case study, Int. Arch. Photogramm., Remote Sens. Spatial Inf. Sci., 2017, 42, pp 47–54.10.5194/isprs-archives-XLII-2-W6-47-2017CrossRefGoogle Scholar
Azid, S.I., Ali, S.A., Kumar, M., Cirrincione, M. and Fagiolini, A. Precise trajectory tracking of multi-rotor UAVs using wind disturbance rejection approach, IEEE Access, 2023, 11, pp 91796–91806. doi: 10.1109/ACCESS.2023.3308297 CrossRefGoogle Scholar
Elmeseiry, N., Alshaer, N. and Ismail, T. A detailed survey and future directions of unmanned aerial vehicles (UAVs) with potential applications, Aerospace, 2021, 8, p 363. doi: 10.3390/aerospace8120363 CrossRefGoogle Scholar
Chao, L., Bai, Y., Wang, Z. and Yin, Y. Neural network-based robust adaptive super-twisting sliding mode fault-tolerant control for a class of tilt tri-rotor UAVs with unmodeled dynamics, Aeronaut. J., 2024, 128, (1328), pp 2408–2427. doi: 10.1017/aer.2024.19 CrossRefGoogle Scholar
Mimouni, M.Z., Araar, O., Oudda, A. and Haddad, M. A new control scheme for an aerodynamic-surface-free tilt-rotor convertible UAV, Aeronaut. J., 2024, 128, (1324), pp 1119–1144. doi: 10.1017/aer.2023.90 Google Scholar
Klaina, H., Guembe, I.P., Lopez-Iturri, P., Campo-Bescós, M.Á., Azpilicueta, L., Aghzout, O., Alejos, A.V. and Falcone, F. Analysis of low power wide area network wireless technologies in smart agriculture for large-scale farm monitoring and tractor communications, Measurement, 2022, 187, p 110231. ISSN 0263-2241. doi: 10.1016/j.measurement.2021.110231 CrossRefGoogle Scholar
Saeed, A.S., Younes, A.B., Cai, C. and Cai, G. A survey of hybrid unmanned aerial vehicles, Progr. Aerosp. Sci., 2018, 98, pp 91–105.10.1016/j.paerosci.2018.03.007CrossRefGoogle Scholar
McDonald, R.A., German, B.J., Takahashi, T., Bil, C., Anemaat, W., Chaput, A., et al. Future aircraft concepts and design methods, Aeronaut. J., 2022, 126, (1295), pp 92–124. doi: 10.1017/aer.2021.110 CrossRefGoogle Scholar
Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S. and Sun, X. A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sens., 2021, 13, (6), p 1204.10.3390/rs13061204CrossRefGoogle Scholar
Chittoor, P.K., Chokkalingam, B., Verma, R. and Mihet-Popa, L. An assessment of shortest prioritized path-based bidirectional wireless charging approach toward smart agriculture, IEEE Access, 2023, 11, 123742–123755.10.1109/ACCESS.2023.3329976CrossRefGoogle Scholar
Aslan, M.F., Durdu, A., Sabanci, K., Ropelewska, E. and Gültekin, S.S. A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses, Appl. Sci., 2022, 12, (3), p 1047.10.3390/app12031047CrossRefGoogle Scholar
Abouelmagd, L.M., Shams, M.Y., Marie, H.S. and Hassanien, A.E. An optimized capsule neural networks for tomato leaf disease classification, EURASIP J. Image Video Process., 2024, 2024, (1), p 2.10.1186/s13640-023-00618-9CrossRefGoogle Scholar
Niemeyer, M., Renz, M., Pukrop, M., Hagemann, D., Zurheide, T., Di Marco, D., et al. Cognitive weeding: an approach to single-plant specific weed regulation, KI-Künstliche Intell., 2023, 37, (2), pp 175–181.10.1007/s13218-023-00825-6CrossRefGoogle Scholar
Rodríguez-Garlito, E.C. and Paz-Gallardo, A. Efficiently mapping large areas of olive trees using drones in Extremadura, Spain, IEEE J. Miniat. Air Space Syst., 2021, 2, (3), pp 148–156.10.1109/JMASS.2021.3067102CrossRefGoogle Scholar
Tsouros, D.C., Bibi, S. and Sarigiannidis, P.G. A review on UAV-based applications for precision agriculture, Information, 2019, 10, (11). doi: 10.3390/info10110349 CrossRefGoogle Scholar
Godfray, H.C.J. et al. Food security: the challenge of feeding 9 billion people. Science (80–.), 2010, 327, (5967), pp 812 LP–818. doi: 10.1126/science.1185383 CrossRefGoogle ScholarPubMed
Guo, S., Li, J., Yao, W., Zhan, Y., Li, Y. and Shi, Y. Distribution characteristics on droplet deposition of wind field vortex formed by multi-rotor UAV, PLoS One, 2019, 14, (7), p e0220024. [Online]. doi: 10.1371/journal.pone.0220024 CrossRefGoogle ScholarPubMed
Gratton, G.B., Williams, P.D., Padhra, A. and Rapsomanikis, S. Reviewing the impacts of climate change on air transport operations, Aeronaut. J., 2022, 126, (1295), pp 209–221. doi: 10.1017/aer.2021.109 CrossRefGoogle Scholar
Tiwari, K. and Chong, N.Y. Target environment, Multi-Robot Explor. Environ. Monit., 2020, pp 11–17. doi: 10.1016/b978-0-12-817607-8.00014-9 CrossRefGoogle Scholar
Ren, G., Lin, T., Ying, Y., Chowdhary, G. and Ting, K.C. Agricultural robotics research applicable to poultry production: a review, Comput. Electron. Agric., 2019, 169, (December), p 105216. doi: 10.1016/j.compag.2020.105216 CrossRefGoogle Scholar
Albert, S. et al. Assessing the potential of unmanned aerial vehicle spraying of aqueous ozone as an outdoor disinfectant for SARS-CoV-2, Environ. Res., 2021, 196, (February), p 110944. doi: 10.1016/j.envres.2021.110944 CrossRefGoogle Scholar
Idoje, G., Dagiuklas, T. and Iqbal, M. Survey for smart farming technologies: challenges and issues, Comput. Electr. Eng., 2021, 92, (January), p 107104, doi: 10.1016/j.compeleceng.2021.107104 CrossRefGoogle Scholar
Boursianis, A.D. et al. Internet of Things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review, Internet Things, 2020, xxxx, p 100187. doi: 10.1016/j.iot.2020.100187 Google Scholar
Zhai, Z., Martínez, J.F., Beltran, V. and Martinez, N.L. Decision support systems for agriculture 4.0: survey and challenges, Comput. Electron. Agric., 2020, 170, (January), p 105256. doi: 10.1016/j.compag.2020.105256 CrossRefGoogle Scholar
Vélez, S., Vacas, R., Martín, H., Ruano-Rosa, D. and Álvarez, S. High-resolution UAV RGB imagery dataset for precision agriculture and 3D photogrammetric reconstruction captured over a pistachio orchard (Pistacia vera L.) in Spain. Data, 2022, 7, (11), p 157.10.3390/data7110157CrossRefGoogle Scholar
Zhong, Y., Hu, X., Luo, C., Wang, X., Zhao, J. and Zhang, L. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF, Remote Sens. Environ., 2020, 250, p 112012.10.1016/j.rse.2020.112012CrossRefGoogle Scholar
Guerra, A., Guidi, F., Dardari, D. and Djurić, P.M. Reinforcement learning for joint detection and mapping using dynamic UAV networks, IEEE Trans. Aerosp. Electron. Syst., 2023, 60, (3), pp 2586–2601.10.1109/TAES.2023.3300813CrossRefGoogle Scholar
Diene, S.M., Diack, I., Audebert, A., Roupsard, O., Leroux, L., Diouf, A.A., et al. Improving pearl millet yield estimation from UAV imagery in the semiarid agroforestry system of Senegal through textural indices and reflectance normalization, IEEE Access, 2024 12, pp 132626–132643.10.1109/ACCESS.2024.3460107CrossRefGoogle Scholar
Carvajal-Rodríguez, J., Guamán, D.S., Tipantuña, C., Grijalva, F. and Urquiza, L.F. 3D placement optimization in UAV-enabled communications: a systematic mapping study, IEEE Open J. Veh. Technol.,   2024, 5, pp 523–559.10.1109/OJVT.2024.3379751CrossRefGoogle Scholar
Christiansen, M.P., Laursen, M.S., Jorgensen, R.N., Skovsen, S. and Gislum, R. Designing and testing a UAV mapping system for agricultural field surveying, Sensors, 2017, 17, (12). doi: 10.3390/s17122703 CrossRefGoogle ScholarPubMed
Liu, J., Xu, W., Guo, B., Zhou, G. and Zhu, H. Accurate mapping method for UAV photogrammetry without ground control points in the map projection frame, IEEE Trans. Geosci. Remote Sens., 2021, 59, (11), pp 9673–9681.10.1109/TGRS.2021.3052466CrossRefGoogle Scholar
Gao, G., Xiao, K. and Jia, Y. A spraying path planning algorithm based on colour-depth fusion segmentation in peach orchards, Comput. Electron. Agric., 2020, 173, p 105412.10.1016/j.compag.2020.105412CrossRefGoogle Scholar
Ibrahima, D., Mansour, D.S., Louise, L., Aziz, D.A., Benjamin, H., Olivier, R., et al. Combining UAV and sentinel-2 imagery for estimating millet FCover in a heterogeneous agricultural landscape of Senegal, IEEE J. Select. Topics Appl. Earth Obs. Remote Sens.   2024, 17, pp 7305–7322.Google Scholar
Castellanos, G., Deruyck, M., Martens, L. and Joseph, W. System assessment of WUSN using NB-IoT UAV-aided networks in potato crops, IEEE Access, 2020, 8, pp 56823–56836.10.1109/ACCESS.2020.2982086CrossRefGoogle Scholar
Bouguettaya, A., Zarzour, H., Kechida, A. and Taberkit, A.M. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images, Cluster Comput., 2023, 26, (2), pp 1297–1317.10.1007/s10586-022-03627-xCrossRefGoogle ScholarPubMed
Velez, A.F., Alvarez, C.I., Navarro, F., Guzman, D., Bohorquez, M.P., Selvaraj, M.G. and Ishitani, M. Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables, Environ. Monit. Assess., 2024, 196, (6), p 574.10.1007/s10661-024-12725-9CrossRefGoogle ScholarPubMed
Stephen, S. and Kumar, V. Detection and analysis of weed impact on sugar beet crop using drone imagery, J. Indian Soc. Remote Sens., 2023, 51, (12), pp 2577–2597.10.1007/s12524-023-01782-1CrossRefGoogle Scholar
de Castro, A.I., Peña, J.M., Torres-Sánchez, J., Jiménez-Brenes, F. and López-Granados, F. Mapping Cynodon dactylon in vineyards using UAV images for site-specific weed control, Adv. Anim. Biosci., 2017, 8, (2), pp 267–271. doi: 10.1017/S2040470017000826 CrossRefGoogle Scholar
Shafi, U., Mumtaz, R., Anwar, Z., Ajmal, M.M., Khan, M.A., Mahmood, Z., Qamar, M. and Jhanzab, H.M. Tackling food insecurity using remote sensing and machine learning based crop yield prediction, IEEE Access, 2023, 11, pp 108640–108657.10.1109/ACCESS.2023.3321020CrossRefGoogle Scholar
Mitra, A., Vangipuram, S.L., Bapatla, A.K., Bathalapalli, V.K., Mohanty, S.P., Kougianos, E. and Ray, C. Smart agriculture: a comprehensive overview, SN Comput. Sci., 2024, 5, (8), p 969.10.1007/s42979-024-03319-wCrossRefGoogle Scholar
Yallappa, D., Veerangouda, M., Maski, D., Palled, V. and Bheemanna, M. Development and evaluation of drone mounted sprayer for pesticide applications to crops, in 2017 IEEE Global Humanitarian Technology Conference (GHTC), 2017, pp 1–7. doi: 10.1109/GHTC.2017.8239330 CrossRefGoogle Scholar
Desale, R., Chougule, A., Choudhari, M., Borhade, V. and Tetali, S. Unmanned aerial vehicle for pesticides spraying,  International Journal for Science and Advance Research in Technology, 2019, 5, (April), pp 79–82.Google Scholar
Gašparović, M., Zrinjski, M., Barković, D. and Radočaj, D. An automatic method for weed mapping in oat fields based on UAV imagery, Comput. Electron. Agric., 2019, 173, (February), p 105385. doi: 10.1016/j.compag.2020.105385 CrossRefGoogle Scholar
Pajares, G. Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs), Photogramm. Eng. Remote Sens., 2015, 81, (4), pp 281–329. doi: 10.14358/PERS.81.4.281 CrossRefGoogle Scholar
Maes, W.H. and Steppe, K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture, Trends Plant Sci., 2019, 24, (2), pp 152–164. doi: 10.1016/j.tplants.2018.11.007 CrossRefGoogle ScholarPubMed
Singh, V. et al. Unmanned Aircraft Systems for Precision Weed Detection and Management: Prospects and Challenges, 1st ed., 159, Elsevier Inc., Newark, USA 2020.Google Scholar
Abioye, E.A. et al. A review on monitoring and advanced control strategies for precision irrigation, Comput. Electron. Agric., 2020, 173, (April), p 105441. doi: 10.1016/j.compag.2020.105441 CrossRefGoogle Scholar
da Silva, E.E., Rojo Baio, F.H., Ribeiro Teodoro, L.P., da Silva Junior, C.A., Borges, R.S. and Teodoro, P.E. UAV-multispectral and vegetation indices in soybean grain yield prediction based on in situ observation, Remote Sens. Appl. Soc. Environ., 2020, 18, (April). doi: 10.1016/j.rsase.2020.100318 Google Scholar
Ampatzidis, Y., Partel, V., Meyering, B. and Albrecht, U. Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence, Comput. Electron. Agric., 2019, 164, (July), p 104900. doi: 10.1016/j.compag.2019.104900 CrossRefGoogle Scholar
Shi, Q., Liu, D., Mao, H., Shen, B. and Li, M. Wind-induced response of rice under the action of the downwash flow field of a multi-rotor UAV, Biosyst. Eng., 2021, 203, pp 60–69. doi: 10.1016/j.biosystemseng.2020.12.012 CrossRefGoogle Scholar
The Food and Agriculture Organization Corporate Statistical Database (FAOSTAT). n.d. https://openknowledge.fao.org/server/api/core/bitstreams/a8a8c2c8-ee36-42e8-a619-7e73c8daf8a6/content (accessed October 2, 2024).Google Scholar
Chao, X. et al. Simulation and validation of the air flow generated by a multi-channel air-assisted sprayer, 2019, IEEE Access, 7, pp 94848–94857. doi: 10.1109/ACCESS.2019.2927377 CrossRefGoogle Scholar
Moltó, E. et al. Engineering approaches for reducing spray drift, Biosyst. Eng., 2017, 154, pp 1–2. doi: 10.1016/j.biosystemseng.2017.01.002 CrossRefGoogle Scholar
Che Ruzlan, K.A. et al. Weed control efficiency of unmanned aerial vehicle (UAV) spray in replanting oil palm plantation areas, Weed Sci., 2024, pp 1–35. doi: 10.1017/wsc.2024.91 CrossRefGoogle Scholar
Achtelik, M.C., Stumpf, J., Gurdan, D. and Doth, K.M. Design of a flexible high performance quadcopter platform breaking the MAV endurance record with laser power beaming, in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, IEEE, San Francisco, CA, USA, pp 5166–5172.10.1109/IROS.2011.6095108CrossRefGoogle Scholar
Martins, P.H.A. et al. Estimating spray application rates in cotton using multispectral vegetation indices obtained using an unmanned aerial vehicle, Crop Prot., 2020, 140, (September), pp 1–7. doi: 10.1016/j.cropro.2020.105407 Google Scholar
Hentschke, M., Pignaton de Freitas, E., Hennig, C. and Girardi da Veiga, I. Evaluation of altitude sensors for a crop spraying drone, Drones, 2018, 2, (3), p 25. doi: 10.3390/drones2030025 CrossRefGoogle Scholar
Xiongkui, H., Bonds, J., Herbst, A. and Langenakens, J. Recent development of unmanned aerial vehicle for plant protection in East Asia, Int. J. Agric. Biol. Eng., 2017, 10, (3), pp 18–30.Google Scholar
Yinka-Banjo, C. and Ajayi, O. Sky-farmers: applications of unmanned aerial vehicles (UAV) in agriculture, in Autonomous Vehicles, IntechOpen. 2019, pp 107–128, http://dx.doi.org/10.5772/intechopen.89488.CrossRefGoogle Scholar
Kant, J.K., Sripaad, M., Bharadwaj, A., Rajashekhar, V.S. and Sundaram, S. An autonomous hybrid Drone-Rover vehicle for weed removal and spraying applications in agriculture, in 2023 IEEE International Conference on Agrosystem Engineering, Technology & Applications (AGRETA), IEEE, Shah Alam, Malaysia, 2023, pp 92–97.10.1109/AGRETA57740.2023.10262416CrossRefGoogle Scholar
Qin, K., Deng, L., Liu, X., Li, Z. and Xie, F. Ag-YOLO: lightweight deep neural network for real-time crop protection UAV spraying in palm plantations, arXiv preprint arXiv:2103.04132, 2021. https://arxiv.org/abs/2103.04132 Google Scholar
Singh, E., Pratap, A., Mehta, U. and Azid, S.I. Smart agriculture Drone for crop spraying using image-processing and machine learning techniques: experimental validation, IoT, 2024, 5, pp 250–270. doi: 10.3390/iot5020013 CrossRefGoogle Scholar
Qin, Z., Wang, W., Dammer, K.H., Guo, L. and Cao, Z. A real-time low-cost artificial intelligence system for autonomous spraying in palm plantations, arXiv preprint arXiv:2103.04132, 2021.Google Scholar
Huang, Y. et al. Development and prospect of unmanned aerial vehicle technologies for agricultural production management, Int. J. Agric. Biol. Eng., 2013, 6, (3), pp 1–10.Google Scholar
Yang, S., Yang, X. and Mo, J. The application of unmanned aircraft systems to plant protection in China, Precis. Agric., 2018, 19, (2), pp 278–292.10.1007/s11119-017-9516-7CrossRefGoogle Scholar
Zang, Y. et al. Design and anti-sway performance testing of pesticide tanks in spraying UAVs, Int. J. Agric. Biol. Eng., 2019, 12, (1), pp 10–16. doi: 10.25165/j.ijabe.20191201.4338 Google Scholar
Elmokadem, T. Distributed coverage control of quadrotor multi-UAV systems for precision agriculture, IFAC-PapersOnLine, 2019, 52, (30), pp 251–256.10.1016/j.ifacol.2019.12.530CrossRefGoogle Scholar
Geronel, R.S., Botez, R.M. and Bueno, D.D. Dynamic responses due to the Dryden gust of an autonomous quadrotor UAV carrying a payload, Aeronaut. J., 2023, 127, (1307), pp 116–138. doi: 10.1017/aer.2022.35 CrossRefGoogle Scholar
Hou, C. et al. Optimization of control parameters of droplet density in citrus trees using UAVs and the Taguchi method, Int. J. Agric. Biol. Eng., 2019, 12, (4), pp 1–9. doi: 10.25165/j.ijabe.20191204.4139 Google Scholar
Ibrahim, O.A., Sciancalepore, S. and Di Pietro, R. Noise2Weight: on detecting payload weight from Drones acoustic emissions, 2020, pp 1–14. [Online]. http://arxiv.org/abs/2005.01347 Google Scholar
Wang, S., Chen, J. and He, X. An adaptive composite disturbance rejection for attitude control of the agricultural quadrotor UAV, ISA Trans., 2022, 129, 564–579.10.1016/j.isatra.2022.01.012CrossRefGoogle ScholarPubMed
Phadke, A., Medrano, F.A., Chu, T., Sekharan, C.N. and Starek, M.J. Modeling wind and obstacle disturbances for effective performance observations and analysis of resilience in UAV swarms, Aerospace, 2024, 11, (3), p 237.10.3390/aerospace11030237CrossRefGoogle Scholar
Basiri, A. et al. A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture, J. Navigat., 2022, 75, (2), pp 364–383. doi: 10.1017/S0373463321000825 CrossRefGoogle Scholar
Zhai, Z., Martínez Ortega, J.-F., Lucas Martínez, N. and Rodríguez-Molina, J. A mission planning approach for precision farming systems based on multi-objective optimization. Sensors, 2018, 18, p 1795. doi: 10.3390/s18061795 CrossRefGoogle ScholarPubMed
Apostolidis, S.D., Kapoutsis, P.C., Kapoutsis, A.C. et al. Cooperative multi-UAV coverage mission planning platform for remote sensing applications, Auton Robot, 2022, 46, pp 373–400. doi: 10.1007/s10514-021-10028-3 CrossRefGoogle Scholar
Ristorto, G., D’Incalci, P., Gallo, R., Mazzetto, F. and Guglieri, G. Mission planning for the estimation of the field coverage of unmanned aerial systems in monitoring mission in precision farming, Chem. Eng. Trans., 2017, 58, pp 649–654.Google Scholar
Mukhamediev, R.I. et al. Coverage path planning optimization of heterogeneous UAVs group for precision agriculture, IEEE Access, 2023, 11, pp 5789–5803. doi: 10.1109/ACCESS.2023.3235207 CrossRefGoogle Scholar
Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D. et al. Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms, Soft Comput., 2017, 21, pp 4883–4900. doi: 10.1007/s00500-016-2376-7 CrossRefGoogle Scholar
Pradeep, P., Park, S.G. and Wei, P. Trajectory optimization of multirotor agricultural UAVs, in 2018 IEEE Aerospace Conference, Big Sky, MT, USA, 2018, pp 1–7. doi: 10.1109/AERO.2018.8396617 CrossRefGoogle Scholar
Skobelev, P., Budaev, D., Gusev, N. and Voschuk, G. Designing Multi-agent Swarm of UAV for Precise Agriculture. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018, Commun. Comput. Inf. Sci., 2018, 887. doi: 10.1007/978-3-319-94779-25 Google Scholar
Ramirez-Atencia, C., Rodriguez-Fernandez, V. and Camacho, D. A revision on multi-criteria decision making methods for multi-UAV mission planning support, Exp. Syst. Appl., 2020, 160, p 113708. ISSN 0957-4174. doi: 10.1016/j.eswa.2020.113708 CrossRefGoogle Scholar
Li, H. Adaptive algorithms for drone flight control under communication constraints and information incompleteness, Aeronaut. J., 2025, 129, (1332), pp 282–295.10.1017/aer.2024.112CrossRefGoogle Scholar
Stefas, N., Bayram, H. and Isler, V. Vision-based monitoring of orchards with UAVs, Comput. Electron. Agric., 2019, 163, p 104814.10.1016/j.compag.2019.05.023CrossRefGoogle Scholar
Vanegas, F., Gaston, K.J., Roberts, J. and Gonzalez, F. A framework for UAV navigation and exploration in GPS-denied environments, in 2019 IEEE Aerospace Conference, IEEE, Big Sky, MT, USA, 2019, pp 1–6.10.1109/AERO.2019.8741612CrossRefGoogle Scholar
Xu, N., Li, Z., Kang, J., Meng, Q. and Niu, M. Agricultural vehicle automatic navigation positioning and obstacle avoidance technology based on ICP. IEEE Access, 2024, 12, pp 85940–85954.10.1109/ACCESS.2024.3402743CrossRefGoogle Scholar
Kumar, A. and Behera, L. Design, localization, perception, and control for GPS-denied autonomous aerial grasping and harvesting, IEEE Robot. Automat. Lett., 2024, 9, (4), pp 3538–3545.10.1109/LRA.2024.3366015CrossRefGoogle Scholar
Pleninger, S., Topkova, T. and Steiner, J. GNSS interference detection: methodology utilising ADS-B NACp indicator and GPS almanac data, Aeronaut. J., 2025, 129, (1331), pp 206–223.10.1017/aer.2024.67CrossRefGoogle Scholar
Costley, A. and Christensen, R. Landmark aided GPS-denied navigation for orchards and vineyards, in 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA, IEEE, 2020, pp 987–995. doi: https://doi.org/10.1109/PLANS46316.2020.9110130.CrossRefGoogle Scholar
Thoma, A., Thomessen, K., Gardi, A., Fisher, A. and Braun, C. Prioritising paths: an improved cost function for local path planning for UAV in medical applications, Aeronaut. J., 2023, 127, (1318), pp 2125–2142.10.1017/aer.2023.68CrossRefGoogle Scholar
Zhao, W., Xu, T., Wang, Y., Du, W. and Shen, A. Research on vision navigation and position system of agricultural unmanned aerial vehicle, Int. J. Comput. Integr. Manuf., 2020, 33, (10–11), pp 1185–1196.10.1080/0951192X.2020.1795925CrossRefGoogle Scholar
Youn, W., Ko, H., Choi, H., Choi, I., Baek, J.H. and Myung, H. Collision-free autonomous navigation of a small UAV using low-cost sensors in GPS-denied environments, Int. J. Control, Autom. Syst., 2021, 19, (2), pp 953–968.10.1007/s12555-019-0797-7CrossRefGoogle Scholar
Dlouhy, M., Lev, J. and Kroulik, M. Technical and software solutions for autonomous unmanned aerial vehicle (UAV) navigation in case of unavailable GPS signal, Agron. Res., 2016, 14, (3), pp 733–744.Google Scholar
Al-Jarrah, O.Y., Shatnawi, A.S., Shurman, M.M., Ramadan, O.A. and Muhaidat, S. Exploring deep learning-based visual localization techniques for UAVs in GPS-denied environments, IEEE Access, 2024, 12, pp 113049–113071.10.1109/ACCESS.2024.3440064CrossRefGoogle Scholar
Ahmed, S., Qiu, B., Ahmad, F., Kong, C.W. and Xin, H. A state-of-the-art analysis of obstacle avoidance methods from the perspective of an agricultural sprayer UAV’s operation scenario, Agronomy, 2021, 11, (6), p 1069.10.3390/agronomy11061069CrossRefGoogle Scholar
Uzun, M., Bilgic, H.H., Çopur, E.H. and Çoban, S. The aerodynamic force estimation of a swept-wing UAV using ANFIS based on metaheuristic algorithms, Aeronaut. J., 2024, 128, (1322), pp 739–755. doi: 10.1017/aer.2023.73 CrossRefGoogle Scholar
Kurode, S., Spurgeon, K.S., Bandyopadhyay, B. and Gandhi, P.S. Sliding mode control for slosh-free motion using a nonlinear sliding surface, IEEE/ASME Trans. Mechatron., 2013, 18, (2), pp 714–724. doi: 10.1109/TMECH.2011.2182056 CrossRefGoogle Scholar
Toumi, M., Bouazara, M. and Richard, M.J. Impact of liquid sloshing on the behaviour of vehicles carrying liquid cargo, Eur. J. Mech. A/Solids, 2009, 28, (5), pp 1026–1034. doi: 10.1016/j.euromechsol.2009.04.004 CrossRefGoogle Scholar
Dong, K., Qi, N.M., Guo, J.J. and Li, Y.Q. An estimation approach for propellant sloshing effect on spacecraft GNC, in 2008 2nd Int. Symp. Syst. Control Aerosp. Astronaut. ISSCAA 2008, 2008. doi: 10.1109/ISSCAA.2008.4776402 CrossRefGoogle Scholar
Guerrero-Sánchez, M.E., Mercado-Ravell, D.A., Lozano, R. and García-Beltrán, C.D. Swing-attenuation for a quadrotor transporting a cable-suspended payload, ISA Trans., 2017, 68, pp 433–449. doi: 10.1016/j.isatra.2017.01.027 CrossRefGoogle ScholarPubMed
Nichkwade, C., Harish, P.M. and Ananthkrishnan, N. Stability analysis of a multibody system model for coupled slosh-vehicle dynamics, J. Sound Vib., 2004, 275, (3–5), pp 1069–1083. doi: 10.1016/j.jsv.2003.07.009 CrossRefGoogle Scholar
Panferov, A.I., Nebylov, A.V. and Brodsky, S.A. Complex flexible aerospace vehicles simulation and control system design.IFAC Proceedings Volumes, 2013 46(19), pp 429–434. Würzburg, Germany, https://doi.org/10.3182/20130902-5-DE-2040.00115. CrossRefGoogle Scholar
Feddema, J.T., Dohrmann, C.R., Parker, G.G., Robinett, R.D., Romero, V.J. and Schmitt, D.J. Control for Slosh-free motion of an open container, IEEE Control Syst., 1997, 17, (1), pp 29–36. doi: 10.1109/37.569711 Google Scholar
Reyhanoglu, M. and Rubio Hervas, J. Nonlinear dynamics and control of space vehicles with multiple fuel slosh modes, Control Eng. Pract., 2012, 20, (9), pp 912–918. doi: 10.1016/j.conengprac.2012.05.011 CrossRefGoogle Scholar
Yano, K. and Terashima, K. Sloshing suppression control of liquid transfer systems considering a 3-D transfer path, IEEE/ASME Trans. Mechatron., 2005, 10, (1), pp 8–16. doi: 10.1109/TMECH.2004.839033 CrossRefGoogle Scholar
Zang, Q., Huang, J. and Liang, Z. Slosh suppression for infinite modes in a moving liquid container, IEEE/ASME Trans. Mechatron., 2015, 20, (1), pp 217–225. doi: 10.1109/TMECH.2014.2311888 CrossRefGoogle Scholar
Johnson, N. and Singhose, W. Dynamics and modeling of a quadrotor with a suspended payload, 2018 Appl. Aerodyn. Conf., 2018. doi: 10.2514/6.2018-4213 CrossRefGoogle Scholar
Lee, S., Giri, D.K. and Son, H. Modeling and control of quadrotor UAV subject to variations in center of gravity and mass, in 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence URAI 2017, 2017, pp 85–90, doi: 10.1109/URAI.2017.7992893 CrossRefGoogle Scholar
Andrew J., Stanley, Roger M., Goodall,Estimation of the Centre of Gravity of a Manoeuvring Aircraft using Kalman filters and the ADMIRE aircraft model. IFAC Proceedings Volumes, 43, (18), 2010. pp 1–7, Cambridge, MA, USA. doi: https://doi.org/10.3182/20100913-3-US-2015.00076.Google Scholar
Tan, L., Shen, Z. and Yu, S. Adaptive fault-tolerant flight control for a quadrotor UAV with slung payload and varying COG, in 3rd International Symposium on Autonomous Systems ISAS 2019, 2019, pp 227–231. doi: 10.1109/ISASS.2019.8757704 CrossRefGoogle Scholar
Sreelalitha, B., Jayalakshmi, M., Lakshmi, P. and Patel, V.V. Accelerated flight control LAW clearance using stores grouping concept, IFAC-PapersOnLine, 2018, 51, (1), pp 365–370. doi: 10.1016/j.ifacol.2018.05.051 Google Scholar
Iqbal, F., Javed, A. and Shahzad, A. Sloshing analysis of a supersonic fuel tank of an aircraft, in ICAEM 2021-2021 Int. Conf. Appl. Eng. Math. Proc., 2021, pp 7–12. doi: 10.1109/ICAEM53552.2021.9547074 CrossRefGoogle Scholar
Tumari, M.Z.M. et al. Liquid slosh control by implementing model-free PID controller with derivative filter based on PSO, Indones. J. Electr. Eng. Comput. Sci., 2020, 18, (2), pp 750–758. doi: 10.11591/ijeecs.v18.i2 Google Scholar
Mohammadi, K. et al. Decentralized motion control in a cabled-based multi-drone load transport system, IEEE Int. Conf. Intell. Robot. Syst., 2018, pp 4198–4203. doi: 10.1109/IROS.2018.8593952 CrossRefGoogle Scholar
Aboudonia, A., El-Badawy, A. and Rashad, R. Active anti-disturbance control of a quadrotor unmanned aerial vehicle using the command-filtering backstepping approach, Nonlinear Dyn., 2017, 90, (1), pp 581–597. doi: 10.1007/s11071-017-3683-y CrossRefGoogle Scholar
Xinyu, C., Yongsheng, Z. and Yunsheng, F. Adaptive integral backstepping control for a quadrotor with suspended flight, in 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), IEEE, Dalian, China, 2020, pp 226–234.10.1109/CACRE50138.2020.9229913CrossRefGoogle Scholar
Bhatia, A.K., Jiang, J., Zhen, Z., Ahmed, N. and Rohra, A. Projection modification based robust adaptive backstepping control for multipurpose quadcopter UAV, IEEE Access, 2019, 7, pp 154121–154130. doi: 10.1109/ACCESS.2019.2946416 CrossRefGoogle Scholar
Zhou, Y., Chen, Y., Zhang, L. and Pan, C. Distributed finite-time prescribed performance for multiple unmanned aerial vehicle with time-varying external disturbance, IEEE Internet Things J., 2024, 11, (9), pp 16969–16980. doi: 10.1109/JIOT.2024.3367172.CrossRefGoogle Scholar
Fu, C., Sun, H., Dai, L. and Xia, Y. Robust MPC-based trajectory tracking control for quadrotor UAV-slung load system, in 2024 43rd Chinese Control Conference (CCC), IEEE, 2024, pp 2826–2833.10.23919/CCC63176.2024.10661941CrossRefGoogle Scholar
Schreier, M. Modeling and adaptive control of a quadrotor, in 2012 IEEE International Conference on Mechatronics and Automation, Chengdu, China, 2012, pp 383–390. doi: 10.1109/ICMA.2012.6282874 CrossRefGoogle Scholar
Faíçal, B.S. et al. Fine-tuning of UAV control rules for spraying pesticides on crop fields: an approach for dynamic environments, Int. J. Artif. Intell. Tools, 2016, 25, (1), pp 1–19. doi: 10.1142/S0218213016600034 CrossRefGoogle Scholar
Menebo, M., Negash, L. and Shiferaw, D. Neural network based model reference adaptive control of quadrotor UAV for precision agriculture, in  Debelee, T.G., Ibenthal, A., Schwenker, F., Megersa Ayano, Y. (eds) Pan African Conference on Artificial Intelligence. Cham: Springer Nature Switzerland, 2023, pp 171–193.Google Scholar
Estevez, J., Manuel Lopez-Guede, J., del Valle-Echavarri, J. and Graña, M. Reinforcement learning based trajectory planning for multi-UAV load transportation, IEEE Access, 2024, 12, pp 144009–144016. doi: 10.1109/ACCESS.2024.3470509 CrossRefGoogle Scholar
Kartal, M. Sparse online Gaussian process with backstepping integration for sloshing disturbance rejection in agricultural UAVs, J Aerosp Eng., 2024, 31, (3), pp 450–460.Google Scholar
Geronel, R.S. and Bueno, D.D. Adaptive sliding mode control for vibration reduction on UAV carrying a payload, J. Vib. Control, 2024, 0(0). doi: 10.1177/10775463241231845 Google Scholar
Zhang, Y., Zhuang, B., Ma, C. and Zhang, C. Backstepping sliding mode control algorithm for unmanned aerial vehicles based on fractional-order theory, J. Robot., 2023, 2023, (1), p 1388072.Google Scholar
Chaumette, S. Collaboration between autonomous drones and swarming, in Namuduri, K., Chaumette, S., Kim, J.H. and Sterbenz, J.P.G. (eds), UAV Networks and Communications. Cambridge: Cambridge University Press, 2017, pp 177–193.10.1017/9781316335765.009CrossRefGoogle Scholar
Ju, C. and Son, H.I. Multiple UAV systems for agricultural applications: control, implementation, and evaluation, Electronics, 2018, 7, 162. doi: 10.3390/electronics7090162 CrossRefGoogle Scholar
Bakar Siddik, M.A., Deb, M., Pinki, P.D., Kanti dhar, M. and Faruk, M.O. Modern agricultural farming based on robotics and server-synced automation system, in 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), Bhubaneswar, India, 2018, pp 323–326. doi: 10.1109/ICRIEECE44171.2018.9009238 CrossRefGoogle Scholar
Khursheed, S.A. Cooperative payload transportation by UAVs: a model-based deep reinforcement learning (MBDRL) application (Doctoral dissertation, Virginia Tech).Google Scholar
Xu, D. and Chen, G. Autonomous and cooperative control of UAV cluster with multi-agent reinforcement learning, Aeronaut. J., 2022, 126, (1300), pp 932–951. doi: 10.1017/aer.2021.112 CrossRefGoogle Scholar
Ding, Y., Wang, L., Li, Y. and Li, D. Model predictive control and its application in agriculture: a review, Comput. Electron. Agric., 2018, 151, pp 104–117.10.1016/j.compag.2018.06.004CrossRefGoogle Scholar