UAV (unmanned aerial vehicle) sprayer for 1 hectare 12 5
DOI:
https://doi.org/10.51896/xnjrb093Keywords:
Precision Agriculture, Unmanned Aerial Vehicles (UAVs), Agricultural Engineering, Technological Innovation, Technological DevelopmentAbstract
Precision agriculture has emerged as a technological strategy to enhance the efficiency of agricultural systems through the integration of digital tools and automated processes. In this context, unmanned aerial vehicles (UAVs) have gained increasing relevance due to their ability to optimize agrochemical application, reduce operational costs, and minimize environmental impacts. The objective of this research was to conceptually design a zeppelin-type unmanned aerial vehicle for spraying corn crops covering areas of up to one hectare. The study adopted an applied research approach with a descriptive scope and a non-experimental research design, supported by a comprehensive documentary review and the application of the Jenkins methodology for the systematic development of the proposed prototype. Key design variables included payload capacity, flight endurance, spraying coverage, aerodynamic stability, and operational efficiency, while also considering applicable aeronautical regulations. The proposed design demonstrates potential advantages over conventional spraying methods by reducing application time, improving the efficiency of agrochemical use, decreasing operator exposure to hazardous substances, and increasing agricultural productivity. It is concluded that the incorporation of a zeppelin-type UAV constitutes a viable technological alternative to strengthen precision agriculture, promote sustainable farming practices, and support the modernization of agricultural activities, particularly for small-scale producers.
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References
Blanchard, B. S., & Fabrycky, W. J. (2011). Systems Engineering and Analysis (5th ed.). Pearson.
Food and Agriculture Organization of the United Nations. (2022). The State of Food and Agriculture 2022: Leveraging automation in agriculture for transforming agrifood systems. FAO. https://doi.org/10.4060/cb9479en
Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Santos, C. H., & Pekkeriet, E. (2020). Agricultural robotics for field operations. Sensors, 20(9), 2672. https://doi.org/10.3390/s20092672
Gokool, S., Mahomed, M., Kunz, R., Clulow, A., Sibanda, M., Naiken, V., Chetty, K., & Mabhaudhi, T. (2023). Crop monitoring in smallholder farms using unmanned aerial vehicles to facilitate precision agriculture practices: A scoping review and bibliometric analysis. Sustainability, 15(4), 3557. https://doi.org/10.3390/su15043557
Huang, Y., Hoffmann, W. C., Lan, Y., Wu, W., & Fritz, B. K. (2022). Development of unmanned aerial vehicle technologies for agricultural applications. Biosystems Engineering. (Verifica el volumen y páginas antes del envío).
Javaid, M., Khan, I. H., Haleem, A., Vaishya, R., Suman, R., & Vaish, A. (2023). Understanding the potential applications of artificial intelligence in agriculture. Advanced Agrochem, 2(1), 15–30. https://doi.org/10.1016/j.aac.2022.10.001
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
Liu, Z., & Li, J. (2023). Application of unmanned aerial vehicles in precision agriculture. Agriculture, 13(7), 1375. https://doi.org/10.3390/agriculture13071375
Mesías-Ruiz, G. A., Pérez-Ortiz, M., Dorado, J., de Castro, A. I., & Peña, J. M. (2023). Boosting precision crop protection towards Agriculture 5.0 via machine learning and emerging technologies. Frontiers in Plant Science, 14, 1143326. https://doi.org/10.3389/fpls.2023.1143326
Narzari, R., Choudhury, B. U., Singhal, G., & Choudhary, K. K. (2025). A critical review of how UAVs can transform precision agriculture in the realm of agroecology. Discover Soil, 2, 28. https://doi.org/10.1007/s44378-025-00055-2
Qin, W., & Chen, P. (2023). Analysis of the research progress on the deposition and drift of spray droplets by plant protection UAVs. Scientific Reports, 13, 14935. https://doi.org/10.1038/s41598-023-40556-0
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
Torres-Sánchez, J., López-Granados, F., Peña, J. M., & Castro, A. I. (2020). An automatic object-based method for optimal thresholding in UAV imagery applied to precision agriculture. Remote Sensing. (Verifica volumen y DOI antes del envío).
Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712. https://doi.org/10.1007/s11119-012-9274-5
Zualkernan, I., Abuhani, D. A., Hussain, M. H., Khan, J., & ElMohandes, M. (2023). Machine learning for precision agriculture using imagery from unmanned aerial vehicles (UAVs): A survey. Drones, 7(6), 382. https://doi.org/10.3390/drones7060382
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