Header Image Title: USING A DRONE TO DETECT PLANT DISEASE PATHOGENS
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Title: USING A DRONE TO DETECT PLANT DISEASE PATHOGENS

USING A DRONE TO DETECT PLANT DISEASE PATHOGENS
Jakub Karbowski; Jedrzej Minda
10.5593/sgem2022V/3.2/s14.53
10.5593/sgem2022V/3.2
1314-2704
978-619-7603-54-5
English
22
3.2
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Forest Ecosystems
Today, drones are already widely used in agriculture and forestry. Drones can help spray plants, take surface measurements, and estimate the damage caused by adverse weather conditions or wild animals. Drones can also be used in caring for the condition of crops. Traditional methods of detecting threats related to plant disease are time-consuming and ineffective. Thanks to the use of drones equipped with appropriate vision systems, it is possible to carry out quick and precise control of the condition of the plants. The article proposes a system for detecting plant pathogens using a drone. The concept of the proposed solution and the methodology of plant monitoring is described. The use of multiple neural network architectures is presented. Credible model performance metrics are described, thanks to the use of various datasets. In addition, the article presents the results of the developed algorithm on the testing ground. The conducted research confirmed the legitimacy and validity of the use of drones in the monitoring of forest crops and ecosystems.
pathogen detection, drones, vision systems, forestry
[1] 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;
[2] Calabro D., Pasini M. L., Ferro N., Perotto S., A deep learning approach for detection and localization of leaf anomalies, 2022 https://doi.org/10.48550/arXiv.2210.03558;
[3] 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;
[4] 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;
[5] Olariu A., Leaf detection dataset, https://www.kaggle.com/datasets/alexo98/leafdetection;
[6] 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;
[7] Makerere AI Lab, Bean disease dataset, January 2020, https://github.com/AI-LabMakerere/ibean/;
[8] Jocher G., et al., YOLOv5 Classification Models, https://zenodo.org/record/7002879;
[9] 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, https://doi.org/10.48550/arXiv.2207.02696;
[10] Howard A. et al., Searching for MobileNetV3, 2019, https://doi.org/10.48550/arXiv.1905.02244;
[11] Tan M., Le Q. V., EfficientNetV2: Smaller models and faster training, 2021, https://doi.org/10.48550/arXiv.2104.00298;
[12] 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
[13] Wang W. et al., InternImage: Exploring large-scale vision foundation models with deformable convolutions, 2022, https://doi.org/10.48550/arXiv.2211.05778;
[14] 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, https://doi.org/10.48550/arXiv.2012.07177;
[15] Cubuk E. D., Zoph B., Mane D., Vasudevan V., Le Q. V., AutoAugment: Learning augmentation policies from data, 2019, https://doi.org/10.48550/arXiv.1805.09501.
conference
Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 06-08 December, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
455-462
06-08 December, 2022
website
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