Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical
Validación multisitio de sistemas duales de drones para la agricultura de precisión en anacardo tropical
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Precision agriculture in tropical woody crops faces a critical gap: the lack of validated operational frameworks
at commercial scale with multisite reproducibility. This study bridges that gap by validating a dual-drone system
across 201.27 ha of cashew in Vichada, Colombia. Over 254 missions, the system demonstrated a 236 % improvement
in operational efficiency compared to the baseline, achieving an average of 6.68 ha h-¹ and peaks of 11.35 ha h- ¹. The
protocol exhibited high reproducibility (CV < 24 % in hourly efficiency), validating its robustness for commercial
operations. The economic analysis confirms that the Agriculture as a Service (AaaS) model is profitable, with a
25.6 % cost reduction and a 63.4 % return on investment (ROI). This work therefore establishes the first reproducible
and economically viable operational framework for largescale precision agriculture in this type of crops, providing a
validated roadmap for technological adoption.
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- Acharya, B., O’Quinn, T. N., Everman, W. J. y Mehl, H. L. (2019). Effectiveness of fungicides and their application
- timing for the management of sorghum foliar anthracnose in the Mid-Atlantic United States. Plant Disease,
- 103(11), 2804–2811. https://doi.org/10.1094/PDIS-10-18-1867-RE
- Ahn, M. I. y Yun, S. C. (2009). Epidemiological investigations to optimize the management of pepper anthracnose. Plant
- Pathology Journal, 25(3), 213–219. https://doi.org/10.5423/PPJ.2009.25.3.213
- Alharasees, O., Adali, O. H. y Kale, U. (2023). Human factors in the age of autonomous UAVs: Impact of artificial
- intelligence on operator performance and safety. En 2023 International Conference on Unmanned Aircraft Systems
- (ICUAS) (pp. 344–351). https://doi.org/10.1109/ICUAS57906.2023.10156037
- Alemán-Montes, B., Henríquez-Henríquez, C., Largaespada-Zapata, K. y Ramírez-Rodríguez, T. (2022). Evaluación
- de flecha seca en palma aceitera (Elaeis guineensis Jacq.) mediante imágenes multiespectrales obtenidas con
- VANT. Agronomía Mesoamericana, 33(2), 47557. https://doi.org/10.15517/am.v33i2.47557
- Alvarez-Vanhard, E., Corpetti, T. y Houet, T. (2021). UAV y satellite synergies for optical remote sensing applications:
- A literature review. Science of Remote Sensing, 4, 100019. https://doi.org/10.1016/j.srs.2021.100019
- Arcadia, É. A., Marceleño, S. M. L. y Flores, F. (2024). Agricultura de precisión en la producción de caña de azúcar:
- Diagnóstico para revisar las relaciones entre prácticas agrícolas tradicionales y la adopción de tecnologías. En
- E. I. Mariscal, M. E. Becerra, R. Gómez y L. C. Barrón (coords.), Desafíos en el contexto empresarial: sostenibilidad,
- innovación y competitividad (pp. 35–47). Universidad Autónoma de Nayarit. https://doi.org/10.52501/cc.250.02
- Banik, T. y Vn, N. (2024). Farming in the digital age: Unleashing the power of farming as a service (FaaS). International
- Journal of Agriculture Extension and Social Development, 7(5), 99–102. https://doi.org/10.33545/26180723.2024.
- v7.i5b.601
- Barbedo, J. G. A. (2019). A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and
- assessing plant stresses. Drones, 3(2), 40. https://doi.org/10.3390/DRONES3020040
- Barocco, R. L., Clohessy, J. W., O’Brien, G. K., Dufault, N. S., Anco, D. J. y Small, I. M. (2024). Sensor-based quantification
- of peanut disease defoliation using an unmanned aircraft system and multispectral imagery. Plant Disease,
- 108(2), 416–425. https://doi.org/10.1094/PDIS-05-23-0847-RE
- Canicattì, M. y Vallone, M. (2024). Drones in vegetable crops: A systematic literature review. Smart Agricultural
- Technology, 7, 100396. https://doi.org/10.1016/j.atech.2024.100396
- Conner, R. L., McAndrew, D. W., Kiehn, F. A., Chapman, S. R. y Froese, N. T. (2004). Effect of foliar fungicide application
- timing on the control of bean anthracnose in the navy bean ‘Navigator’. Canadian Journal of Plant Pathology,
- 26(3), 299–303. https://doi.org/10.1080/07060660409507147
- Costa, R., Almeida, C. y Laurindo, F. (2022). Precision farming-as-a-service: Fundamental concepts, trends and
- challenges. A new business model into agricultural segment. En 19th International Conference on Information
- Systems and Technology Management. https://doi.org/10.5748/19contecsi/pse/agb/7067
- DaMatta, F. M., Avila, R. T., Cardoso, A. A., Martins, S. C. V. y Ramalho, J. C. (2018). Physiological and agronomic
- performance of the coffee crop in the context of climate change and global warming: A review. Journal of
- Agricultural and Food Chemistry, 66(21), 5264-5274. https://doi.org/10.1021/acs.jafc.7b0453
- Kwao, P. L., Owusu, G. M., Okyere, J., Agbenya, J. K., Laryea, I. L. N. y Armah, S. K. (2024). Agricultural drones in
- Africa: Exploring adoption, applications, and barriers. International Journal for Multidisciplinary Research, 6(6).
- https://doi.org/10.36948/ijfmr.2024.v06i06.28326
- Mena, E., Galeana, G., Estrada, M., Alonso, E. P. y Flores, D. A. (2025). Tecnologías innovadoras en la agricultura de
- precisión. Ciencia Latina Revista Científica Multidisciplinar, 9(2), 5660–5666. https://doi.org/10.37811/cl_rcm.
- v9i2.17319
- Mhaned, A., Salma, M., Haji, M. E. y Benhra, J. (2025). Smart agriculture based on artificial intelligence and drones:
- A systematic review. En M. Syafrudin, N. Fitriyani y M. Anshari (eds.), Artificial Intelligence and Data Science
- for Sustainability: Applications and Methods (pp. 213-266). IGI Global Scientific Publishing. https://doi.
- org/10.4018/979-8-3693-6829-9.ch008
- MicaSense Inc. (2025). MicaSense RedEdge-P camera technical specifications. https://support.micasense.com/hc/en-us/
- articles/4410824602903-RedEdge-P-Integration-Guide
- MicaSense Support Team. (2022). Best practices: Collecting data with MicaSense sensors. https://support.micasense.com/
- hc/en-us/articles/224893167
- Monteiro, F., Romeiras, M., Bernabé, J., Catarino, S., Batista, D. y Sebastiana, M. (2022). Disease-causing agents in cashew:
- A review in a tropical cash crop context. Agronomy, 12(10), 2553. https://doi.org/10.3390/agronomy12102553
- Nagel, J. (2012). Principales barreras para la adopción de las TIC en la agricultura y en las áreas rurales. Cepal. https://www.
- cepal.org/es/publicaciones/4011-principales-barreras-la-adopcion-tic-la-agricultura-areas-rurales
- Nicolau, A., Tăbîrcă, A. I., Tănase, L. C. y Radu, V. (2025). Integrating drone-based decision support systems in precision
- farming: An econometric simulation of management efficiency and cost-benefit analysis. Romanian Agricultural
- Research. https://doi.org/10.59665/rar4285
- Njoroge, S., Mugi-Ngenga, E., Limo, B. y Fakoya, O. E. (2025). Precision agriculture in Africa: Challenges and
- opportunities. Growing Africa, 4(1), 2–5. https://doi.org/10.55693/ga41.mbuf4046
- Pham, Y., Reardon-Smith, K., Mushtaq, S. y Cockfield, G. (2019). The impact of climate change and variability on coffee
- production: A systematic review. Climatic Change, 16, 609-630. https://doi.org/10.1007/s10584-019-02538-y
- Prasad, K., Venkatesa, P. N. B., Rohini, A., Kalpana, M., Parameswari, E. y Kowsalya, S. (2025). Exploring the impact
- of drone technology on agricultural practices: A bibliometric review. Plant Science Today, 12(sp1). https://doi.
- org/10.14719/pst.10165
- Rishikesavan, S., Kannan, P., Pazhanivelan, S., Kumaraperumal, R., Sritharan, N., Muthumanickam, D., Firnass, M. M.
- R. A., Baskaran, V. y Teja, V. S. (2024). Prospects and challenges of drone technology in sustainable agriculture.
- Plant Science Today, 11(sp4). https://doi.org/10.14719/pst.5761
- Rodríguez-López, E. S., Cárdenas-Soriano, E., Hernández-Delgado, S., Gutiérrez-Díez, A. y Mayek-Pérez, N. (2013).
- Análisis de la infección de Colletotrichum gloeosporioides (Penz.) Penz. y Sacc. de frutos de aguacatero. Revista
- Brasileira de Fruticultura, 35(3), 747–756. https://doi.org/10.1590/S0100-29452013000300029
- Sagan, V., Maimaitijiang, M., Sidike, P., Maimaitiyiming, M., Erkbol, H., Hartling, S., Peterson, K. T., Peterson, J., Burken,
- J. y Fritschi, F. (2019). UAV/satellite multiscale data fusion for crop monitoring and early stress detection. En
- ISPRS Geospatial Week 2019 (Vol. XLII-2/W13, pp. 715–722). International Society of Photogrammetry and
- Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-2-W13-715-2019
- Salas-Macías, C. A., Sánchez-Mora, F., Montes Escobar, K., de la Hoz-M, J., Limongi-Andrade, R., Mora-Yela, R. V. y
- Garcés-Fiallos, F. R. (2024). Resilience of cacao-based agroforestry systems to climate change. En L. García,
- N. Maddela, F. Zambrano y C. Aguilar (eds.), Sustainable Cacao Cultivation in Latin America (pp. 115–134).
- Routledge. https://doi.org/10.4324/9781003381761-8
- Satish, S., Shirwal, S., Abishek, A. G., Maheshwari y Murali, M. (2025). Application of drones in precision agriculture:
- A review on benefits and challenges. Journal of Experimental Agriculture International, 47(7), 516–531. https://
- doi.org/10.9734/jeai/2025/v47i73591
- Singh, E., Pratap, A. y Kumar, A. (2024). Smart agriculture drone for crop spraying using image-processing and machine
- learning techniques: experimental validation. IoT, 5(2), 348–367. https://doi.org/10.3390/iot5020013
- Sreeram, M. y Nof, S. Y. (2021). Human-in-the-loop: Role in cyber-physical agricultural systems. International Journal of
- Computers Communications & Control, 16(2). https://doi.org/10.15837/IJCCC.2021.2.4166
- Telefónica Tech. (2025, septiembre). Drones, AI and IoT in precision agriculture: Innovation across the phenological cycle.
- https://telefonicatech.com/en/blog/drones-ai-and-iot-in-precision-agriculture-innovation-across-thephenological-cycle
- Vanitha, N. y Selvaa, S. K. R. (2023). Analysis of drone applications in precision agriculture. En G. Karthick (ed.),
- Contemporary Developments in Agricultural Cyber-Physical Systems (pp. 240-253). IGI Global Scientific Publishing.
- https://doi.org/10.4018/978-1-6684-7879-0.ch013
- Wingtra AG. (2025). WingtraOne GEN II technical specifications. https://wingtra.com/mapping-drone-wingtraone/
- technical-specifications/
- Xu, K., Gong, Y., Fang, S., Wang, K., Lin, Z. y Wang, F. (2021). Radiometric calibration of UAV-based multispectral
- imagery: A critical review for quantitative analysis. Remote Sensing, 11(11), 1291. https://doi.org/10.3390/
- rs11111291
- Yılmaz, A. A. (2024). Enhancing UAV crew performance and safety: A technology and innovation management
- perspective. Sosyal Mucit Academic Review, 5(2), 130–153. https://doi.org/10.54733/smar.1512893
- Zhao, J. (2024). Drone technology for precision agriculture: Advancements and optimization strategies. Highlights in
- Science Engineering and Technology, 111, 185–191. https://doi.org/10.54097/h70j2c34
- Zhou, Q., Zhang, S., Xue, X., Cai, C. y Wang, B. (2023). Performance evaluation of UAVs in wheat disease control.
- Agronomy, 13(8), 2131. https://doi.org/10.3390/agronomy13082131