Peer-reviewed articles 17,970 +


Title: ARTIFICIALLY AIDED FUNGI RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS
ARTIFICIALLY AIDED FUNGI RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS

PlumX Article Metrics by Elsevier
Kamil Gajewski; Witold Prusak; Jaroslaw Fafara; Aleksander Skrzypiec; Tymoteusz Turlej
10.5593/sgem2022V/3.2/s14.33
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
This article presents the concept of using neural networks in the recognition of fungi for use in a mobile forest ecosystem inspection robot. There are many dependencies regarding the occurrence of fungi in the vicinity of specific tree species. The presence of some fungi may be the result of a developing tree disease. The possibility of quick recognition of the fungus species using an autonomous mobile robot will allow for faster detection and prevention of the disease in entire ecosystems. An attempt was made to use neural networks to improve the efficiency of recognizing a specific species of fungus. This paper presents a comparison between our network and the AlexNet method network (created by Alex Krizhevsky) [1] for fungal recognition. This system was designed so that created by our students' science club NewTech AGH mobile inspection robot "RUMCAJS" could map the fungal population over time. Based on the comparison of the neural networks used, the possibility of correct use of the proposed solution for the detection of fungi was shown, as well as a more effective method in this application was indicated. The proposed method can be successfully implemented for the inspection of ecosystems using autonomous robots.
[1] Krizhevsky.A, Sutskever.I, Hinton.G.E, ImageNet Classification with Deep Convolutional Neural Networks, Proceedings of the 25th International Conference on Neural Information Processing Systems (Lake Tahoe, NV, Dec. 2012), 1097–1105
[2] Olano, J. M., Martinez-Rodrigo, R., Altelarrea, J. M., Agreda, T., Fernandez-Toiran, M., Garcia-Cervigon, A. I., Rodriguez-Puerta, F., & Agueda, B. (2020). Primary productivity and climate control mushroom yields in Mediterranean pine forests. Agricultural and Forest Meteorology, 288-289, 108015.
[3] Collado, E., Camarero, J. J., Martinez de Aragon, J., Peman, J., Bonet, J. A., & deMiguel, S. (2018). Linking fungal dynamics, tree growth and forest management in a mediterranean pine ecosystem. Forest Ecology and Management, 422, 223–232.
[4] Koo T, Kim MH, Jue M-S (2021) Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network. PLoS ONE 16(8): e0256290. https://doi.org/10.1371/journal.pone.0256290
[5] Jagadeesh D. Pujari, Yakkundimath R., Abdulmunaf S.B. (2015). Image Processing Based Detection of Fungal Diseases in Plants, Procedia Computer Science, Vol. 46, p. 1802-1808
[6] R F Rahmat et al., (2018), IOP Conf. Ser.: Mater. Sci. Eng.420 012097
[7] Cordero, R.J., E.R. Mattoon, and A. Casadevall. (2020) Fungi are colder than their surroundings. BioRxiv.
This publication was created as part of the implementation of the AGH Rector's Grants No. 71/GRANT/2022 and the Rector's Grant IDUB/2022 – 3870 and funding from the Dean of the Faculty of Mechanical Engineering and Robotics at WIMIR AGH
conference
https://doi.org/10.5593/sgem2022V/3.2/s14.33
Download PDF
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.
291-298
06-08 December, 2022
website
8797
mushrooms, image recognition, conventional neural networks