Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer
Author:
Funder
Higher Education Commision, Pakistan
Publisher
Springer Science and Business Media LLC
Subject
General Agricultural and Biological Sciences
Link
https://link.springer.com/content/pdf/10.1007/s11119-021-09808-9.pdf
Reference44 articles.
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2. Alexandridis, T. K., Tamouridou, A. A., Pantazi, X. E., Lagopodi, A. L., Kashefi, J., Ovakoglou, G., Polychronos, V., & Moshou, D. (2017). Novelty detection classifiers in weed mapping: Silybum marianum detection on UAV multispectral images. Sensors (Switzerland), 17, 9. https://doi.org/10.3390/s17092007
3. Ammar, A., Koubaa, A., Ahmed, M., & Saad, A. (2019). Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3. ArXiv, 1–28. http://arxiv.org/abs/1910.07234
4. Bandini, F., Jakobsen, J., Olesen, D., Reyna-Gutierrez, J. A., & Bauer-Gottwein, P. (2017). Measuring water level in rivers and lakes from lightweight Unmanned Aerial Vehicles. Journal of Hydrology, 548, 237–250. https://doi.org/10.1016/j.jhydrol.2017.02.038
5. Castaldi, F., Pelosi, F., Pascucci, S., & Casa, R. (2017). Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agriculture, 18(1), 76–94. https://doi.org/10.1007/s11119-016-9468-3
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