USING A DRONE TO DETECT PLANT DISEASE PATHOGENS

Rejeb A., Abdollahi A., Rejeb K, Treiblmaier H., Drones in agriculture: A review and bibliometric analysis, Computers and Electronics in Agriculture, vol. 198, p 107017, 2022; DOI: 10.1016/j.compag.2022.107017

Calabro D., Pasini M. L., Ferro N., Perotto S., A deep learning approach for detection and localization of leaf anomalies, 2022 DOI: 10.48550/arxiv.2210.03558

Thakur P. S., Khanna P., Sheorey T., Ojha A., Trends in vision-based machine learning techniques for plant disease identification: A systematic review, Expert Systems with Applications, vol. 208, p. 118117, 2022; DOI: 10.1016/j.eswa.2022.118117

Singh V., Sharma N., Singh S., A review of imaging techniques for plant disease detection, Artificial Intelligence in Agriculture, vol. 4, pp. 229�242, 2020; DOI: 10.1016/j.aiia.2020.10.002

Olariu A., Leaf detection dataset, https://www.kaggle.com/datasets/alexo98/leafdetection;

Arun P. J., Geetharanani G., Data for: Identification of plant leaf diseases using a 9- layer deep convolutional neural network. Mendeley Data, V1, DOI: 10.17632/tywbtsjrjv.1

Makerere AI Lab, Bean disease dataset, January 2020, https://github.com/AI-LabMakerere/ibean/;

Jocher G., et al., YOLOv5 Classification Models, https://zenodo.org/record/7002879;

Wang C.-Y., Bochkovskiy A., Liao H.-Y. M., Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, 2022, DOI: 10.48550/arxiv.2207.02696

Howard A. et al., Searching for MobileNetV3, 2019, DOI: 10.48550/arxiv.1905.02244 https://doi.org/10.1109/iccv.2019.00140

Tan M., Le Q. V., EfficientNetV2: Smaller models and faster training, 2021, DOI: 10.48550/arxiv.2104.00298

Harakannanavar S. S., Rudagi J. M., Puranikmath V. I., Siddiqua A., Pramodhini R, Plant leaf disease detection using computer vision and machine learning algorithms, Global Transitions Proceedings, Volume 3, Issue 1, June 2022, Pages 305-310 DOI: 10.1016/j.gltp.2022.03.016

Wang W. et al., InternImage: Exploring large-scale vision foundation models with deformable convolutions, 2022, DOI: 10.48550/arxiv.2211.05778

Ghiasi G., Cui Y., Srinivas A., Qian R., Lin T.-Y., Cubuk E. D., Le Q. V., Zoph B., Simple copy-paste is a strong data augmentation method for instance segmentation, 2020, DOI: 10.48550/arxiv.2012.07177 https://doi.org/10.1109/cvpr46437.2021.00294