essay on role and potential of drone technology in agriculture

The Use of Drones in Agriculture: Perspectives and Limitations

Conference paper
First Online: 19 September 2024
Cite this conference paper

essay on role and potential of drone technology in agriculture

Paweł Karpiński ORCID: orcid.org/0000-0001-5786-1248 13

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 609))

Included in the following conference series:

International Symposium on Farm Machinery and Processes Management in Sustainable Agriculture

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The use of computer-aided techniques in farm management, including for performing agricultural practices, known as precision agriculture, has become increasingly popular in recent years. This new and rapidly developing field allows for increased crop yields, improved crop quality, and reduced negative impact on the environment compared to conventional agriculture. A special category within precision agriculture is work performed using drones. The most common activities include monitoring crop health using multispectral cameras, spraying, and seeding. This paper presents a review analysis of the use of drones in agriculture, considering both the perspectives and limitations associated with it. Drones for monitoring and mapping crops and for performing agrotechnical treatments were analyzed separately. The most important perspectives include the widespread automation associated with agricultural work and an increase in the efficiency of the work performed. Significant limitations are legal issues, the cost of purchasing equipment and the need to obtain a drone operator’s license.

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Monteiro, A., Santos, S., Gonçalves, P.: Precision agriculture for crop and livestock farming—Brief review. Animals 11 (8), 2345 (2021)

Article Google Scholar

Hafeez, A., et al.: Implementation of drone technology for farm monitoring & pesticide spraying: a review. Inform. Process. Agric. 10 (2), 192–203 (2023)

Google Scholar

Yamaha Precision Agriculture Website: https://www.yamahamotorsports.com/Precision-Agriculture.php . Last accessed 8 May 2024

Supersonic Aviation Technology (SAT) Website: https://www.satuav.com/helicopter-drone/helicopter-sprayer-drone.html . Last accessed 8 May 2024

DeltaQuad Website, https://www.deltaquad.com/vtol-drones/map/ , last accessed 2024/05/08

BZB UAS Website, https://bzbuas.com/sklep/samoloty-bezzalogowe/rtf/koliber-vtol-linia-agro/ , last accessed 2024/05/08

Del Cerro, J., Cruz Ulloa, C., Barrientos, A., de León Rivas, J.: Unmanned aerial vehicles in agriculture: a survey. Agronomy 11 (2), 203 (2021)

People’s Daily Online, China’s agri-tech industry abuzz as agricultural drones facilitate domestic proliferation of smart farming. http://en.people.cn/n3/2022/0701/c90000-10117605.html . Last accessed 8 May 2024

CNBC Website, N. Anwar, World’s largest drone maker is unfazed — even if it’s blacklisted by the U.S. https://www.cnbc.com/2023/02/08/worlds-largest-drone-maker-dji-is-unfazed-by-challenges-like-us-blacklist.html . Last accessed 8 May 2024

XAG Website: https://www.xa.com/en/plant-protection-uas . Last accessed 8 May 2024

Pyka Website. https://www.flypyka.com/pelican-spray . Last accessed 8 May 2024

Fortune Business Insights Website. Agriculture Drones Market Report, https://www.fortunebusinessinsights.com/agriculture-drones-market-102589 . Last accessed 8 May 2024

Drone Deploy Website, DroneDeploy’s State of the Drone Industry Report 2022. https://www.dronedeploy.com/resources/ebooks/state-of-the-drone-industry-report-2022/ . Last accessed 8 May 2024

Choubey, A., Reddy, B.C.: Drones in agriculture: multispectral analysis. In: Computational Intelligence in Robotics and Automation, pp. 217–241. CRC Press (2023)

Awais, M., et al.: UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review. Int. J. Env. Sci. Technol. 20 , 1135–1152 (2022)

Yee Kit, C.H.A.N., Voon Chet, K.O.O., Edmund Hou Kheat Choong, C.S.: The drone based hyperspectral imaging system for precision agriculture. NVEO-Natural Volatiles & Essential Oils J.| NVEO 8 (5), 5561–5573 (2021)

Panday, U.S., Pratihast, A.K., Aryal, J., Kayastha, R.B.: A review on drone-based data solutions for cereal crops. Drones 4 (3), 41 (2020)

Bansod, B., Singh, R., Thakur, R., Singhal, G.: A comparision between satellite based and drone based remote sensing technology to achieve sustainable development: a review. J. Agric. Env. Int. Dev. 111 (2), 383–407 (2017)

Budiharto, W., Irwansyah, E., Suroso, J.S., Chowanda, A., Ngarianto, H., Gunawan, A.A.S.: Mapping and 3D modelling using quadrotor drone and GIS software. J. Big Data 8 , 1–12 (2021)

Sikora, J., Szeląg-Sikora, A., Gródek-Szostak, Z., Niemiec, M., Stuglik, J.: Support of internal transport optimization in farms with the use of spatial information systems. Agric. Eng. 24 (4), 87–94 (2020)

Kurbanov, R., Litvinov, M.: Development of a gimbal for the Parrot Sequoia multispectral camera for the UAV DJI Phantom 4 Pro. IOP Conf. Ser.: Mater. Sci. Eng. 1001 (1), 012062 (2020). https://doi.org/10.1088/1757-899X/1001/1/012062

Sentera Website. https://sentera.com/hardware/drones/ . Last accessed 8 May 2024

Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., Gattelli, M.: Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 7 (4), 4026–4047 (2015)

Voitik, A., Kravchenko, V., Pushka, O., Kutkovetska, T., Shchur, T., Kocira, S.: Comparison of NDVI, NDRE, MSAVI and NDSI indices for early diagnosis of crop problems. Agric. Eng. 27 (1), 47–57 (2023)

Radočaj, D., Šiljeg, A., Marinović, R., Jurišić, M.: State of major vegetation indices in precision agriculture studies indexed in web of science: a review. Agriculture 13 (3), 707 (2023)

Kharuf-Gutierrez, S., Orozco-Morales, R., Díaz, O.D.L.C.A., Ruiz, E.P.: Multispectral aerial image processing system for precision agriculture. Sistemas y Telemática 16 (47), 45–58 (2018)

Zhang, L., Zhang, H., Niu, Y., Han, W.: Mapping maize water stress based on UAV multispectral remote sensing. Remote Sens. 11 (6), 605 (2019)

Qi, H., et al.: Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction. Comput. Electron. Agric. 187 , 106292 (2021)

Ren, D.D., Tripathi, S., Li, L.K.: Low-cost multispectral imaging for remote sensing of lettuce health. J. Appl. Remote Sens. 11 (1), 016006 (2017)

Barzin, R., Pathak, R., Lotfi, H., Varco, J., Bora, G.C.: Use of UAS multispectral imagery at different physiological stages for yield prediction and input resource optimization in corn. Remote Sens. 12 (15), 2392 (2020)

Zhou, J., Pavek, M.J., Shelton, S.C., Holden, Z.J., Sankaran, S.: Aerial multispectral imaging for crop hail damage assessment in potato. Comput. Electron. Agric. 127 , 406–412 (2016)

DJI Agriculture Website. https://ag.dji.com/mavic-3-m . Last accessed 8 May 2024

Su, J., Liu, C., Hu, X., Xu, X., Guo, L., Chen, W.H.: Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery. Comput. Electron. Agric. 167 , 105035 (2019)

Vanegas, F., Bratanov, D., Weiss, J., Powell, K., Gonzalez, F.: Multi and hyperspectral UAV remote sensing: Grapevine phylloxera detection in vineyards. In: 2018 IEEE Aerospace Conference, pp. 1–9. IEEE (2018)

Gao, C., et al.: Monitoring of wheat fusarium head blight on spectral and textural analysis of UAV multispectral imagery. Agriculture 13 (2), 293 (2023)

Lu, N., et al.: Estimation of nitrogen nutrition status in winter wheat from unmanned aerial vehicle based multi-angular multispectral imagery. Front. Plant Sci. 10 , 1601 (2019)

Rivas, A., Chamoso, P., González-Briones, A., Corchado, J.M.: Detection of cattle using drones and convolutional neural networks. Sensors 18 (7), 2048 (2018)

Xu, B., et al.: Livestock classification and counting in quadcopter aerial images using Mask R-CNN. Int. J. Remote Sens. 41 (21), 8121–8142 (2020)

Assmann, J.J., Kerby, J.T., Cunliffe, A.M., Myers-Smith, I.H.: Vegetation monitoring using multispectral sensors—best practices and lessons learned from high latitudes. J. Unmanned Veh. Syst. 7 (1), 54–75 (2018)

Hanif, A.S., Han, X., Yu, S.H.: Independent control spraying system for UAV-based precise variable sprayer: a review. Drones 6 (12), 383 (2022)

Worakuldumrongdej, P., Maneewam, T., Ruangwiset, A.: Rice seed sowing drone for agriculture. In: 2019 19th International Conference on Control, Automation and Systems (ICCAS), pp. 980–985. IEEE (2019)

Song, C., et al.: Variable-rate control system for UAV-based granular fertilizer spreader. Comput. Electron. Agric. 180 , 105832 (2021)

Song, C., et al.: Test and comprehensive evaluation for the performance of UAV-based fertilizer spreaders. IEEE Access 8 , 202153–202163 (2020)

González-García, J., Swenson, R.L., Gómez-Espinosa, A.: Real-time kinematics applied at unmanned aerial vehicles positioning for orthophotography in precision agriculture. Comput. Electron. Agric. 177 , 105695 (2020)

Carvalho, F.K., Chechetto, R.G., Mota, A.A., Antuniassi, U.R.: Challenges of aircraft and drone spray applications. Outlooks Pest Manag. 31 (2), 83–88 (2020)

Emergen Research Website, Drone Spraying Services Market Report. https://www.emergenresearch.com/industry-report/drone-spraying-services-market . Last accessed 8 May 2024

Shahrooz, M., Talaeizadeh, A., Alasty, A.:Agricultural spraying drones: advantages and disadvantages. In: 2020 Virtual symposium in plant omics sciences (OMICAS), pp. 1–5. IEEE (2020).

Zhou, H., et al.: Application of a centrifugal disc fertilizer spreading system for UAVs in rice fields. Heliyon 10 (8), e29837 (2024)

Tevyashov, G.K., et al.: Algorithm for multi-drone path planning and coverage of agricultural fields. In: Ronzhin, A., Berns, K., Kostyaev, A. (eds.) Agriculture Digitalization and Organic Production. SIST, vol. 245, pp. 299–310. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-3349-2_25

Chapter Google Scholar

Chen, P., et al.: Characteristics of unmanned aerial spraying systems and related spray drift: a review. Front. Plant Sci. 13 , 870956 (2022)

Yang, Z., Yu, J., Duan, J., Xu, X., Huang, G.: Optimization-design and atomization-performance study of aerial dual-atomization centrifugal atomizer. Agriculture 13 (2), 430 (2023)

Hu, H., et al.: Design and performance test of a novel UAV air-assisted electrostatic centrifugal spraying system. Int. J. Agric. Biol. Eng. 15 (5), 34–40 (2022)

DJI Agriculture Website. https://ag.dji.com/t50 . Last accessed 8 May 2024

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Department of Machine Operation and Production Processes Management, University of Life Sciences in Lublin, Akademicka 13, 20-950, Lublin, Poland

Paweł Karpiński

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Correspondence to Paweł Karpiński .

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Faculty of Production Engineering, University of Life Sciences, Lublin, Poland

Edmund Lorencowicz

Walloon Agricultural Research Centre, Gembloux, Belgium

Bruno Huyghebaert

College of Management and Enterprise, Wałbrzych, Poland

Jacek Uziak

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Karpiński, P. (2024). The Use of Drones in Agriculture: Perspectives and Limitations. In: Lorencowicz, E., Huyghebaert, B., Uziak, J. (eds) Farm Machinery and Processes Management in Sustainable Agriculture. FMPMSA 2024. Lecture Notes in Civil Engineering, vol 609. Springer, Cham. https://doi.org/10.1007/978-3-031-70955-5_24