Research Profiles
- ORCID:
https://orcid.org/0000-0002-6780-2425 - Google Scholar: https://scholar.google.com/citations?user=K6Vw3bYAAAAJ&hl=en
- Semantic Scholar: https://www.semanticscholar.org/author/Juan-Sandino/40385298
- ResearchGate: https://www.researchgate.net/profile/Juan_Sandino
- QUT ePrints: https://eprints.qut.edu.au/view/person/Sandino,_Juan.html
- Scopus: https://www.scopus.com/authid/detail.uri?authorId=57202714779
2023
- J. Sandino, B. Bollard, A. Doshi, K. Randall, J. Barthelemy, S. A. Robinson, and F. Gonzalez, “A green fingerprint of Antarctica: Drones, hyperspectral imaging, and machine learning for moss and lichen classification,” Remote sensing, vol. 15, no. 24, p. 5658, Dec. 2023. DOI: 10.3390/rs15245658.
2022
- J. Sandino, “Autonomous decision‑making for UAVs operating under environmental and object detection uncertainty,” Ph.D. dissertation, Queensland University of Technology, Brisbane, Australia, Jun. 2022. DOI: 10.5204/thesis.eprints.232513.
2021
- J. Sandino, F. Maire, P. Caccetta, C. Sanderson, and F. Gonzalez, “Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty,” Remote Sensing, vol. 13, no. 21, p. 4481, Nov. 2021. DOI: 10.3390/rs13214481.
2020
- J. Sandino, F. Vanegas, F. Maire, P. Caccetta, C. Sanderson, and F. Gonzalez, “UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments,” Remote Sensing, vol. 12, no. 20, p. 3386, Oct. 2020. DOI: 10.3390/rs12203386.
- J. Sandino, F. Vanegas, F. Gonzalez, and F. Maire, “Autonomous UAV Navigation for Active Perception of Targets in Uncertain and Cluttered Environments,” in Aerospace Conference, Big Sky, MT, USA: IEEE, Mar. 2020, pp. 1–12. doi: 10.1109/AERO47225.2020.9172808.
2018
- J. Sandino, G. Pegg, F. Gonzalez, and G. Smith, “Aerial mapping of forests affected by pathogens using UAVs, hyperspectral sensors, and artificial intelligence,” Sensors, vol. 18, no. 4, p. 944, 2018. doi: 10.3390/s18040944.
- J. Sandino and F. Gonzalez, “A novel approach for invasive weeds and vegetation surveys using UAS and artificial intelligence,” in International Conference on Methods & Models in Automation & Robotics, IEEE, 2018, pp. 515–520. doi: 10.1109/MMAR.2018.8485874.
- J. Sandino, F. Gonzalez, K. Mengersen, and K. J. Gaston, “UAVs and machine learning revolutionising invasive grass and vegetation surveys in remote arid lands,” Sensors, vol. 18, no. 2, p. 605, 2018. doi: 10.3390/s18020605.
2017
- J. Sandino, D. Amaya Hurtado, and O. L. Ramos, “Prediction of reproductive system affectation in sprague dawley rats by food intake exposed with fenthion, using naïve bayes classifier and genetic algorithms,” Biosciences, Biotechnology Research Asia, vol. 14, no. 4, pp. 1291–1297, 2017. doi: 10.13005/bbra/2572.
- J. Sandino, A. Wooler, and F. Gonzalez, “Towards the automatic detection of pre-existing termite mounds through UAS and hyperspectral imagery,” Sensors, vol. 17, no. 10, p. 2196, 2017. doi: 10.3390/s17102196.
2016
- J. Sandino, D. Amaya Hurtado, and O. L. Ramos Sandoval, “Prediction of endocrine system affectation in fisher 344 rats by food intake exposed with malathion, applying naïve bayes classifier and genetic algorithms,” International Journal of Preventive Medicine, vol. 7, no. 1, p. 111, 2016. doi: 10.4103/2008-7802.190611.
- J. Sandino, D. Amaya Hurtado, and O. L. Ramos Sandoval, “Monitoreo preliminar de incidencia de fisiopatías en cultivos de fresa usando procesamiento digital de imágenes,” Biotecnología en el Sector Agropecuario y Agroindustrial, vol. 14, no. 1, pp. 45–52, 2016. doi: 10.18684/BSAA(14)45-52.
- J. Sandino, O. L. Ramos-Sandoval, and D. Amaya-Hurtado, “Method for estimating leaf coverage in strawberry plants using digital image processing,” Revista Brasileira de Engenharia Agrícola e Ambiental, vol. 20, no. 8, pp. 716–721, 2016. doi: 10.1590/1807-1929/agriambi.v20n8p716-721.
2015
- D. Amaya and J. Sandino, “Método preliminar de detección de patógenos biológicos en cultivos de fresa por medio del procesamiento digital de imágenes,” Revista de Investigación Agraria y Ambiental, vol. 6, no. 1, pp. 111–122, 2015. doi: 10.22490/21456453.1267.
