Research publications

Below is a wide selection of the research that has been undertaken in this field in recent years:

Sheffield, K.J.; Clements, D.; Clune, D.J.; Constantine, A.; Dugdale, T.M. Detection of Aquatic Alligator Weed (Alternanthera philoxeroides) from Aerial Imagery Using Random Forest Classification. Remote Sens. 2022, 14, 2674. https://doi.org/10.3390/rs14112674

Blanco-Sacristán, J.; Guirado, E.; Molina-Pardo, J.L.; Cabello, J.; Giménez-Luque, E.; Alcaraz-Segura, D. Remote Sensing-Based Monitoring of Postfire Recovery of Persistent Shrubs: The Case of Juniperus communis in Sierra Nevada (Spain). Fire 2023, 6, 4. https://doi.org/10.3390/fire6010004

Sulaiman, N., Che’Ya, N. N., Mohd Roslim, M. H., Juraimi, A. S., Mohd Noor, N., & Fazlil Ilahi, W. F. (2022). The application of Hyperspectral Remote Sensing Imagery (HRSI) for weed detection analysis in rice fields: a review. Applied Sciences, 12(5), 2570.

Amarasingam, N., Salgadoe, A. S. A., Powell, K., Gonzalez, L. F., & Natarajan, S. (2022). A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops. Remote Sensing Applications: Society and Environment, 26, 100712.

Coleman, G. R., Bender, A., Hu, K., Sharpe, S. M., Schumann, A. W., Wang, Z., ... & Walsh, M. J. Weed detection to weed recognition: reviewing 50 years of research to identify constraints and opportunities for large-scale cropping systems. Weed Technology, 1-50.

Aktas, Y. O., Ozdemir, U., Dereli, Y., Tarhan, A. F., Cetin, A., Vuruskan, A., Yuksek, B., Cengiz, H., Basdemir, S., Ucar, M., Genctav, M., Yukselen, A., Ozkol, I., Kaya, M. O., & Inalhan, G. (2016). Rapid Prototyping of a Fixed-Wing VTOL UAV for Design Testing. Journal of Intelligent and Robotic Systems: Theory and Applications, 84(1–4), 639–664. https://doi.org/10.1007/s10846-015-0328-6

Carlson, S. (2014). A hybrid tricopter/flying-wing VTOL UAV. 52nd Aerospace Sciences Meeting, January, 1–11. https://doi.org/10.2514/6.2014-0016

Chandar, E. A. (2021). Structural Investigation of Agricultural UAV. 7(2), 709–727.

De Rango, F., Potrino, G., Tropea, M., Santamaria, A. F., & Palmieri, N. (2019). Simulation, Modeling and Technologies for Drones Coordination Techniques in Precision Agriculture. In Advances in Intelligent Systems and Computing (Vol. 873). Springer International Publishing. https://doi.org/10.1007/978-3-030-01470-4_5

Gago, J., Douthe, C., Coopman, R. E., Gallego, P. P., Ribas-Carbo, M., Flexas, J., Escalona, J., & Medrano, H. (2015). UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management, 153, 9–19. https://doi.org/10.1016/j.agwat.2015.01.020

García, L., Parra, L., Jimenez, J. M., Lloret, J., Mauri, P. V., & Lorenz, P. (2020). DronAway: A proposal on the use of remote sensing drones as mobile gateway for wsn in precision agriculture. Applied Sciences (Switzerland), 10(19). https://doi.org/10.3390/APP10196668

Geiger, RS; Cope, D; Ip, J;  Lotosh, M; Shah, A; Weng, J; Tang, R. (2021) “Garbage in, garbage out” revisited: What do machine learning application papers report about human-labeled training data?. Quantitative Science Studies 2021; 2 (3): 795–827. doi: https://doi.org/10.1162/qss_a_00144

Huang, B; Reichman, D; Collins, L. M; Bradbury, K; and Malof, J.M. (2019)  “Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations.” arXiv, Feb. 25, 2019. doi: 10.48550/arXiv.1805.12219.

Jordan, J. (2019) “Building machine learning products: a problem well-defined is a problem half-solved.,”  Sep. 22,  https://www.jeremyjordan.me/ml-requirements/ (accessed May 24, 2023).

“Organizing machine learning projects: project management guidelines.,” Jordan, J Sep. 02, 2018. https://www.jeremyjordan.me/ml-projects-guide/ (accessed May 24, 2023).

Khanal, S., Fulton, J., & Shearer, S. (2017). An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture, 139, 22–32. https://doi.org/10.1016/j.compag.2017.05.001

Lin, Y. C., Cheng, Y. T., Zhou, T., Ravi, R., Hasheminasab, S. M., Flatt, J. E., Troy, C., & Habib, A. (2019). Evaluation of UAV LiDAR for mapping coastal environments. Remote Sensing, 11(24), 1–32. https://doi.org/10.3390/rs11242893

Maxwell, A.E.; Warner, T.A.; Guillén, L.A. (2021). Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 2: Recommendations and Best Practices. Remote Sens. 2021, 13, 2591. https://doi.org/10.3390/rs13132591

Olson, D., & Anderson, J. (2021). Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal, June 2020, 1–22. https://doi.org/10.1002/agj2.20595

Ozdemir, U., Aktas, Y. O., Vuruskan, A., Dereli, Y., Tarhan, A. F., Demirbag, K., Erdem, A., Kalaycioglu, G. D., Ozkol, I., & Inalhan, G. (2014). Design of a commercial hybrid VTOL UAV system. Journal of Intelligent and Robotic Systems: Theory and Applications, 74(1–2), 371–393. https://doi.org/10.1007/s10846-013-9900-0

Pircher, M., Geipel, J., Kusnierek, K., & Korsaeth, A. (2017). Development of a hybrid UAV sensor platform suitable for farm-scale applications in precision agriculture. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(2W6), 297–302. https://doi.org/10.5194/isprs-archives-XLII-2-W6-297-2017

Ribeiro-Gomes, K., Hernández-López, D., Ortega, J. F., Ballesteros, R., Poblete, T., & Moreno, M. A. (2017). Uncooled thermal camera calibration and optimization of the photogrammetry process for UAV applications in agriculture. Sensors (Switzerland), 17(10), 9–11. https://doi.org/10.3390/s17102173

Roberts, M., Driggs, D., Thorpe, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 3, 199–217 (2021). https://doi.org/10.1038/s42256-021-00307-0

Vuruskan, A., Yuksek, B., Ozdemir, U., Yukselen, A., & Inalhan, G. (2014). Dynamic modeling of a fixed-wing VTOL UAV. 2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014 - Conference Proceedings, 483–491. https://doi.org/10.1109/ICUAS.2014.6842289

Wallace, L., Lucieer, A., Watson, C., & Turner, D. (2012). Development of a UAV-LiDAR system with application to forest inventory. Remote Sensing, 4(6), 1519–1543. https://doi.org/10.3390/rs4061519

Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., Zhang, R., Feng, H., Zhao, X., Li, Z., Li, H., & Yang, H. (2017). Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Frontiers in Plant Science, 8(June). https://doi.org/10.3389/fpls.2017.01111

Yao, H., Qin, R., & Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications - A review. Remote Sensing, 11(12), 1–22. https://doi.org/10.3390/rs11121443

Zhou, Z., Majeed, Y., Diverres Naranjo, G., & Gambacorta, E. M. T. (2021). Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Computers and Electronics in Agriculture, 182 (November 2020), 106019. https://doi.org/10.1016/j.compag.2021.106019