Smart Farming: How Drones Are Transforming the Future of Food Production?

Document Type : Original Article

Authors

Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

10.22084/best.2025.30490.1004

Abstract

 Agriculture in the 21st century confronts numerous obstacles, such as increasing population, climate alterations, and resource depletion. Addressing these challenges requires innovative approaches to guarantee food security and sustainability. Drones, or unmanned aerial vehicles (UAVs), have emerged as transformative tools in precision agriculture, offering capabilities such as crop monitoring, pesticide spraying, planting, and soil analysis. Equipped with advanced sensors, GPS, and artificial intelligence (AI), drones optimize resource use, minimize environmental impact, and enhance productivity. They are categorized into fixed-wing, multi-rotor, and hybrid types, each designed for specific agricultural tasks. Despite their potential, widespread adoption faces barriers such as high costs, limited flight duration, and the need for technical expertise. Future advancements in AI, the Internet of Things (IoT), and battery technology are expected to address these limitations, paving the way for more efficient and extensive use of drones in agriculture. Government policies, training programs, and technological innovations play a critical role in promoting drone adoption, particularly in developing regions. By integrating drones into farming practices, agriculture can achieve greater efficiency, sustainability, and resilience, ultimately contributing to global food security and the reduction of hunger.

Keywords


[1] Abrougui, K., Guebsi, R., Ouni, A., Boughattas, N. E., Habel, F., Barkaoui, Y., Amami, R., Khemis, C., & Kefauver, S. (2022). Contribution of UAV-airborne imagery in the study of machine-soil-plant interaction in potato cultivation, 4(2), 71-78. 10.56027/JOASD.spiss102022|
[2] Ali, M. A., Dhanaraj, R. K., & Kadry, S. (2024). AIenabled IoT-based pest prevention and controlling system using sound analytics in large agricultural field. Computers and Electronics in Agriculture, 220, 108844. https://doi.org/10.1016/j.compag.2024.108844
[3] Altan, A., & Hacıoğlu, R. (2020). Model predictive control of three-axis gimbal system mounted on UAV for real-time target tracking under external disturbances. Mechanical Systems and Signal Processing, 138, 106548. https://doi.org/10.1016/j.ymssp.2019.106548
[4] Awais, M., Naqvi, S. M. Z. A., Zhang, H., Li, L., Zhang, W., Awwad, F. A., Ismail, E. A. A., Khan, M. I., Vijaya, R., & Hu, J. (2023). AI and machine learning for soil analysis: an assessment of sustainable agricultural practices. Bioresources and Bioprocessing, 10(1), 90. https://doi.org/10.1186/s40643-023-00710-y
[5] Balaji, K., Babu, V., & Sulthan, S. (2022). Design and development of multipurpose agriculture drone using lightweight materials. SAE International Journal of Aerospace, 16(01-16-02-0012), 177-183. https://doi.org/10.4271/01-16-02-0012
[6] Beniwal, G., & Singhrova, A. (2022). A systematic literature review on IoT gateways. Journal of King Saud University-Computer and Information Sciences, 34(10), 9541-9563. https://doi.org/10.1016/j.jksuci.2021.11.007
[7] Borikar, G. P., Gharat, C., & Deshmukh, S. R. (2022, October). Application of drone systems for spraying pesticides in advanced agriculture: A Review. In IOP Conference Series: Materials Science and Engineering (Vol. 1259, No. 1, p. 012015). IOP Publishing. 10.1088/1757-899X/1259/1/012015
[8] Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., & Goudos, S. K. (2022). Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18, 100187. https://doi.org/10.1016/j.iot.2020.100187
[9] Canicattì, M., & Vallone, M. (2024). Drones in vegetable crops: a systematic literature review. Smart Agricultural Technology, 7(1), 100396. https://doi.org/10.1016/j.atech.2024.100396
[10] Channe, H., Kothari, S., & Kadam, D. (2015). Multidisciplinary model for smart agriculture using internet-of-things (IoT), sensors, cloud-computing, mobile-computing & big-data analysis. Int. J. Computer Technology & Applications, 6(3), 374-382. https://www.researchgate.net/publication/323187556
[11] Chen, G., Du, W., Xu, T., Wang, S., Qi, X., & Wang, Y. (2024). Investigating Enhanced YOLOv8 Model Applications for Large-Scale Security Risk Management and Drone-Based Low-Altitude Law Enforcement. Highlights in Science, Engineering and Technology, 98(1), 390-396.
[12] Chen, H., Lan, Y., Fritz, B. K., Hoffmann, W. C., & Liu, S. (2021). Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). International Journal of Agricultural and Biological Engineering, 14(1), 38-49. https://dx.doi.org/10.25165/j.ijabe.20211401.5714
[13] Chen, R., Meng, Q., & Yu, J. J. (2023). Optimal government incentives to improve the new technology adoption: Subsidizing infrastructure investment or usage?. Omega, 114(1), 102740.https://doi.org/10.1016/j.omega.2022.102740
[14] Crusiol, L. G. T., Sun, L., Sun, Z., Chen, R., Wu, Y., Ma, J., & Song, C. (2022). In-season monitoring of maize leaf water content using ground-based and UAV-based hyperspectral data. Sustainability, 14(15), 9039. https://doi.org/10.3390/su14159039
[15] Dai, K., Shen, S., & Cheng, C. (2022). Evaluation and analysis of the projected population of China. Scientific Reports, 12(1), 3644. https://doi.org/10.1038/s41598-022-07646-x
[16] Faiçal, B. S., Freitas, H., Gomes, P. H., Mano, L. Y., Pessin, G., de Carvalho, A. C., Krishnamachari, B., & Ueyama, J. (2017). An adaptive approach for UAV-based pesticide spraying in dynamic environments. Computers and Electronics in Agriculture, 138(1), 210-223. https://doi.org/10.1016/j.compag.2017.04.011
[17] Farhan, S. M., Yin, J., Chen, Z., & Memon, M. S. (2024). A comprehensive review of LiDAR applications in crop management for precision agriculture. Sensors (Basel, Switzerland), 24(16), 5409. https://doi.org/10.3390/s24165409
[18] Frauendorf, J. L., & Almeida de Souza, É. (2022). The different architectures used in 1G, 2G, 3G, 4G, and 5G networks. In The Architectural and Technological Revolution of 5G (pp. 83-107). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-10650-7_7
[19] Fue, K. G., Porter, W. M., Barnes, E. M., & Rains, G. C. (2020). An extensive review of mobile agricultural robotics for field operations: focus on cotton harvesting. AgriEngineering, 2(1), 150-174. https://doi.org/10.3390/agriengineering2010010
[20] Haseeb, K., Ud Din, I., Almogren, A., & Islam, N. (2020). An energy efficient and secure IoT-based WSN framework: An application to smart agriculture. Sensors, 20(7), 2081. https://doi.org/10.3390/s20072081
[21] He, G., Li, C., Song, M., Shu, Y., Lu, C., & Luo, Y. (2023). A hierarchical federated learning incentive mechanism in UAV-assisted edge computing environment. Ad Hoc Networks, 149(1), 103249. https://doi.org/10.1016/j.adhoc.2023.103249
[22] Hiraguri, T., Shimizu, H., Kimura, T., Matsuda, T., Maruta, K., Takemura, Y., ... & Takanashi, T. (2023). Autonomous drone-based pollination system using AI classifier to replace bees for greenhouse tomato cultivation. IEEE Access.10.1109/ACCESS.2023.3312151
[23] Hongbo, C., Hansen, E. H., & Růǐčka, J. (1985). Evaluation of critical parameters for measurement of pH by flow injection analysis determination of pH in soil extracts. Analytica chimica acta, 169, 209-220. https://doi.org/10.1016/S0003-2670(00)86223-6
[24] Hossain, A., Krupnik, T. J., Timsina, J., Mahboob, M. G., Chaki, A. K., Farooq, M., Bhatt, R., Fahad, S., & Hasanuzzaman, M. (2020). Agricultural land degradation: processes and problems undermining future food security. In Environment, climate, plant and vegetation growth (pp. 17-61). Cham: Springer International Publishing.
[25] Huang, Y., Hoffman, W. C., Lan, Y., Fritz, B. K., & Thomson, S. J. (2015). Development of a lowvolume sprayer for an unmanned helicopter. Journal of Agricultural Science, 7(1), 148. http://dx.doi.org/10.5539/jas.v7n1p148
[26] Hunt Jr, E. R., & Daughtry, C. S. (2018). What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?. International Journal of Remote Sensing, 39(15-16), 5345-5376. https://doi.org/10.1080/01431161.2017.1410300
[27] Jain, S., Choudhari, P., & Srivastava, A. (2021). The fundamentals of Internet of Things: architectures, enabling technologies, and applications. In Healthcare Paradigms in the Internet of Things Ecosystem (pp. 1-20). Academic Press. https://doi.org/10.1016/B978-0-12-819664-9.00001-6
[28] Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15-30. https://doi.org/10.1016/j.aac.2022.10.001
[29] Jenssen, R. O. R., Eckerstorfer, M., & Jacobsen, S. (2019). Drone-mounted ultrawideband radar for retrieval of snowpack properties. IEEE Transactions on Instrumentation and Measurement, 69(1), 221-230. 10.1109/TIM.2019.2893043
[30] Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1-12. https://doi.org/10.1016/j.aiia.2019.05.004
[31] Jiang, Z., & Xu, C. (2023). Policy incentives, government subsidies, and technological innovation in new energy vehicle enterprises: Evidence from China. Energy Policy, 177, 113527. https://doi.org/10.1016/j.enpol.2023.113527
[32] Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198(1), 105-114. https://doi.org/10.1016/j.rse.2017.06.007
[33] Kerkech, M., Hafiane, A., & Canals, R. (2020). Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach. Computers and Electronics in Agriculture, 174(1), 105446. https://doi.org/10.1016/j.compag.2020.105446
[34] Khadatkar, A., Mathur, S. M., Dubey, K., & Magar, A. P. (2021). Automatic Ejection of Plug-type Seedlings using Embedded System for use in Automatic Vegetable Transplanter. Journal of Sensors, 2021(1), 305-312.
[35] Khuzaimah, Z., Nawi, N. M., Adam, S. N., Kalantar, B., Emeka, O. J., & Ueda, N. (2022). Application and potential of drone technology in oil palm plantation: Potential and limitations. Journal of Sensors, 2022(1), 5385505. https://doi.org/10.1155/2022/5385505
[36] Laghari, A. A., Jumani, A. K., Laghari, R. A., & Nawaz, H. (2023). Unmanned aerial vehicles: A review. Cognitive Robotics, 3, 8-22. https://doi.org/10.1016/j.cogr.2022.12.004
[37] Lan, Y., Thomson, S. J., Huang, Y., Hoffmann, W. C., & Zhang, H. (2010). Current status and future directions of precision aerial application for site-specific crop management in the USA. Computers and electronics in agriculture, 74(1), 34-38. https://doi.org/10.1016/j.compag.2010.07.001
[38] Liu, J., Zhu, Y., Tao, X., Chen, X., & Li, X. (2022). Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery. Frontiers in Plant Science, 13, 1032170. https://doi.org/10.3389/fpls.2022.1032170
[39] Lu, B., Dao, P. D., Liu, J., He, Y., & Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing, 12(16), 2659. https://doi.org/10.3390/rs12162659
[40] Mahroof, K., Omar, A., Rana, N. P., Sivarajah, U., & Weerakkody, V. (2021). Drone as a Service (DaaS) in promoting cleaner agricultural production and Circular Economy for ethical Sustainable Supply Chain development. Journal of Cleaner Production, 287,125522. https://doi.org/10.1016/j.jclepro.2020.125522
[41] Maimaitijiang, M., Sagan, V., Erkbol, H., Adrian, J., Newcomb, M., LeBauer, D., LeBauer, D., Pauli, D., Shakoor, N., & Mockler, T. C. (2020). UAV-based sorghum growth monitoring: A comparative analysis of lidar and photogrammetry. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 3, 489-496. https://doi.org/10.5194/isprsannals-V-3-2020-489-2020
[42] Makam, S., Komatineni, B. K., Meena, S. S., & Meena, U. (2024). Unmanned aerial vehicles (UAVs): an adoptable technology for precise and smart farming. Discover Internet of Things, 4(1), 12. https://doi.org/10.1007/s43926-024-00066-5
[43] Mao, F., Khamis, K., Clark, J., Krause, S., Buytaert, W., Ochoa-Tocachi, B. F., & Hannah, D. M. (2020). Moving beyond the technology: a socio-technical roadmap for low-cost water sensor network applications. Environmental Science & Technology, 54(15), 9145-9158. https://doi.org/10.1021/acs.est.9b07125
[44] Marzuki, O. F., Teo, E. Y. L., & Rafie, A. S. M. (2021). The mechanism of drone seeding technology: a review. Malays. For, 84, 349-358.
[45] Matese, A., Toscano, P., Di Gennaro, S. F., Genesio, L., Vaccari, F. P., Primicerio, J., Belli, C., Zaldei, A., Bianconi, R., & Gioli, B. (2015). Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote sensing, 7(3), 2971-2990. https://doi.org/10.3390/rs70302971
[46] Mattivi, P., Pappalardo, S. E., Nikolić, N., Mandolesi, L., Persichetti, A., De Marchi, M., & Masin, R. (2021). Can commercial low-cost drones and open-source GIS technologies be suitable for semi-automatic weed mapping for smart farming? A case study in NE Italy. Remote sensing, 13(10), 1869. https://doi.org/10.3390/rs13101869
[47] Ming, R., Jiang, R., Luo, H., Lai, T., Guo, E., & Zhou, Z. (2023). Comparative analysis of different uav swarm control methods on unmanned farms. Agronomy, 13(10), 2499. https://doi.org/10.3390/agronomy13102499
[48] Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114(4), 358-371. https://doi.org/10.1016/j.biosystemseng.2012.08.009
[49] Narayana, T. L., Venkatesh, C., Kiran, A., Kumar, A., Khan, S. B., Almusharraf, A., & Quasim, M. T. (2024). Advances in real time smart monitoring of environmental parameters using IoT and sensors. Heliyon, 10(7), 28195. https://doi.org/10.1016/j.heliyon.2024.e28195
[50] Nazarov, D., Nazarov, A., & Kulikova, E. (2023). Drones in agriculture: Analysis of different countries. In BIO Web of Conferences (Vol. 67, p. 02029). EDP Sciences. https://doi.org/10.1051/bioconf/20236702029
[51] Nex, F., Armenakis, C., Cramer, M., Cucci, D. A., Gerke, M., Honkavaara, E., Kukko, A., Persello, C., Skaloud, J., & Skaloud, J. (2022). UAV in the advent of the twenties: Where we stand and what is next. ISPRS Journal of Photogrammetry and Remote Sensing, 184, 215-242. https://doi.org/10.1016/j.isprsjprs.2021.12.006
[52] Oliveira, R. C. D., & Silva, R. D. D. S. E. (2023). Artificial intelligence in agriculture: benefits, challenges, and trends. Applied Sciences, 13(13), 7405. https://doi.org/10.3390/app13137405
[53] Pádua, L., Adão, T., Sousa, A., Peres, E., & Sousa, J. J. (2020). Individual grapevine analysis in a multi-temporal context using UAV-based multi-sensor imagery. Remote Sensing, 12(1), 139. https://doi.org/10.3390/rs12010139
[54] Pansy, D. L., & Murali, M. (2023). UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN. Environmental Monitoring and Assessment, 195(9), 1120.
[55] Prakash, C., Singh, L. P., Gupta, A., & Lohan, S. K. (2023). Advancements in smart farming: A comprehensive review of IoT, wireless communication, sensors, and hardware for agricultural automation. Sensors and Actuators A: Physical, 362(1), 114605. https://doi.org/10.1016/j.sna.2023.114605
[56] Puppala, H., Peddinti, P. R., Tamvada, J. P., Ahuja, J., & Kim, B. (2023). Barriers to the adoption of new technologies in rural areas: The case of unmanned aerial vehicles for precision agriculture in India. Technology in Society, 74, 102335. https://doi.org/10.1016/j.techsoc.2023.102335
[57] Shamshiri, R., Weltzien, C., Hameed, I. A., J Yule, I., E Grift, T., Balasundram, S. K., Pitonakova, L., Ahmad, D., & Chowdhary, G. (2018). Research and development in agricultural robotics: A Perspective of Digital Farming., 11(4), 1-14. http://dx.doi.org/10.25165/j.ijabe.20181104.4278
[58] Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148. https://doi.org/10.1016/j.comnet.2020.107148
[59] Ray, D. K., West, P. C., Clark, M., Gerber, J. S., Prishchepov, A. V., & Chatterjee, S. (2019). Climate change has likely already affected global food production. PloS one, 14(5), e0217148. https://doi.org/10.1371/journal.pone.0217148
[60] Rinaldi, M., Primatesta, S., & Guglieri, G. (2023). A comparative study for control of quadrotor UAVs. Applied Sciences, 13(6), 3464. https://doi.org/10.3390/app13063464
[61] Schmidt, R., Schadow, J., Eißfeldt, H., & Pecena, Y. (2022). Insights on remote pilot competences and training needs of civil drone pilots. Transportation research procedia, 66, 1-7. https://doi.org/10.1016/j.trpro.2022.12.001
[62] Seong, M., Jo, O., & Shin, K. (2024). Age of information minimization in UAV-assisted data harvesting networks by multi-agent deep reinforcement curriculum learning. Expert Systems with Applications, 255(1), 124379. https://doi.org/10.1016/j.eswa.2024.124379
[63] Singh, N., Gupta, D., Joshi, M., Yadav, K., Nayak, S., Kumar, M., Nayak, K., Gulaiya, S., & Rajpoot, A. S. (2024). Application of Drones Technology in Agriculture: A Modern Approach. Journal of Scientific Research and Reports, 30(7), 142-152. https://doi.org/10.9734/jsrr/2024/v30i72131
[64] Song, Y., Bi, J., & Wang, X. (2024). Design and implementation of intelligent monitoring system for agricultural environment in IoT. Internet of Things, 25, 101029. https://doi.org/10.1016/j.iot.2023.101029
[65] Souvanhnakhoomman, S. (2024). Review on application of drone in spraying pesticides and fertilizers. arXiv preprint arXiv:2402.00020. https://doi.org/10.48550/arXiv.2402.00020
[66] Sundmaeker, H., Verdouw, C., Wolfert, S., & Freire, L. P. (2022). Internet of food and farm 2020. In Digitising the industry internet of things connecting the physical, digital and virtualworlds (pp. 129-151). River Publishers.
[67] Toscano, F., Fiorentino, C., Capece, N., Erra, U., Travascia, D., Scopa, A., Drosos, M., & D’Antonio, P. (2024). Unmanned Aerial Vehicle for Precision Agriculture: A Review. IEEE Access., 12(1), 69188-69205. https://doi.org/10.1109/ACCESS.2024.3401018
[68] Townsend, A., Jiya, I. N., Martinson, C., Bessarabov, D., & Gouws, R. (2020). A comprehensive review of energy sources for unmanned aerial vehicles, their shortfalls and opportunities for improvements. Heliyon, 6(11), e05285. https://doi.org/10.1016/j.heliyon.2020.e05285
[69] Wei, X., Xie, B., Wan, C., Song, R., Zhong, W., Xin, S., & Song, K. (2024). Enhancing soil health and plant growth through microbial fertilizers: Mechanisms, benefits, and sustainable agricultural practices. Agronomy, 14(3), 609. https://doi.org/10.3390/agronomy14030609
[70] Yin, X., Jin, R., & Lin, D. (2024, June). Efficient airto-air drone detection with composite multi-dimensional attention. In 2024 IEEE 18th International Conference on Control & Automation (ICCA) (pp. 725-730). IEEE. https://doi.org/10.1109/ICCA62789.2024.10591905
[71] Zhou, Y., Lao, C., Yang, Y., Zhang, Z., Chen, H., Chen, Y., Chen, J., Ning, J., Yang, N., & Yang, N. (2021). Diagnosis of winter-wheat water stress based on UAVborne multispectral image texture and vegetation indices. Agricultural Water Management, 256, 107076. https:// doi.org/10.1016/j.agwat.2021.107076