Comparison of the Effectiveness of Artificial Intelligence-Based Plant Recognition Applications
Keywords:
machine vision, artificial intelligence, plant recognition, mobile applicationAbstract
With the development of technology, more and more artificial intelligence-based plant recognition software is being developed, which can make it easier to determine the plants living in our environment. The aim of our work was to compare the effectiveness of different plant recognition software. We chose Kísérleti lake and its immediate surroundings in the area of Stage I (Hídvégi lake) in Kis-Balaton in the year 2024. In connection with machine vision, the task of the device was to detect the plant, separate it from the plants representing the background, and based on its individual characteristics, make a decision about exactly what kind of plant it is. During the research, we compared six different plant recognition applications by analyzing 15 images of plant species typical of the area. The plants appeared as a simple tool in the experiment, with which the comparison of applications could be realized. The main aspect of their selection was that the input data set should contain simple plants with almost the same color as the background, as well as plants with unique, colorful inflorescences. We checked the accuracy and compared the results with the values indicated by the developers of the software. Based on the results, we determined which application is recommended for the accurate recognition of plants in this region.
References
Badeka, E., Kalabokas, T., Tziridis, K., Nicolaou, A., Vrochidou, E., Mavridou, E., Papakostas, G.A., Pachidis, T. 2019. Grapes Visual Segmentation for Harvesting Robots Using Local Texture Descriptors. International Conference on Computer Vision Systems. Springer: Thessaloniki, Greece, 98–109. https://doi.org/10.1007/978-3-030-34995-0_9
Chassignol, M., Khoroshavin, A., Klimova, A., Bilyatdinova, A. 2018. Artificial Intelligence trends ineducation: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233
Dietterich, T.G. 1997. Machine-Learning Research. AI Magazine, 18 (4), 97. https://doi.org/10.1609/AIMAG.V18I4.1324
Doris, L., Potter, K. (2024): The Role of Deep Learning in Computer Vision. Machine Learning.
Han, B.-G., Lee, J. T., Lim, K.-T., Choi, D.-H. 2020. License Plate Image Generation using Generative Adversarial Networks for End-To-End License Plate Character Recognition from a Small Set of Real Images. Applied Sciences 10, 2780. https://doi.org/10.3390/app10082780
Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., Pasupuleti, V. R. 2020. A critical review oncomputer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033. https://doi.org/10.1016/j.jafr.2020.100033
Kozma-Bognár, K., Szeglet, P., Berke, J., Kozma-Bognár, V. 2021. Térinformatikai adatbázis fejlesztésének lehetőségei a Kis-Balaton mintaterületén. XXVII. Multimédia az oktatásban online nemzetközi konferencia kiadvány. 181–185.
Kozma-Bognár, V., Magyary, V., Berke, J. 2016. Ultranagy felbontású légifelvételek multitemporális elemzése. Debreceni Egyetem Térinformatikai Konferencia és Szakkiállítás. 7, 271–277. https://doi.org/10.13140/RG.2.1.3711.7044
Lehoczky, M., Siki, Z. 2020. Fotogrammetriai feldolgozószoftverek. Geodézia és Kartográfia. 72 (2), 23–27. http://doi.org./10.30921/GK.72.2020.2.4
Li K., Hopkins A. K., Bau D., Viégas F., Pfister H., Wattenberg M. 2022. Emergent world representations: exploring a sequence model trained on asynthetic task. https://doi.org/10.48550/arXiv.2210.13382
Tian, H., Wang, T., Liu, Y., Qiao, X., Li, Y. 2020. Computer vision technology in agricultural automation—A review. Information Processing in Agriculture 7, 1–19. https://doi.org/10.1016/j.inpa.2019.09.006
Minsky, M. 1961. Steps Toward Artificial Intelligence. Proceedings of the IRE, 49 (1), 8–30. https://doi.org/10.1109/JRPROC.1961.287775
Prasad G.A., Kumar, A. V. S., Sharma, P., Irawati, I. D., Chandrashekar D. V., Musirin, I. B., Abdullah, H. M .A., Rao, L. M. 2023. Artificial Intelligence in Computer Science: An Overview of Current Trends and Future Directions. In S. Rajest, B. Singh, A. Obaid, R. Regin, & K. Chinnusamy (Eds.), Advances in Artificial and Human Intelligence in the Modern Era, IGI Global, 43–60. https://doi.org/10.4018/979-8-3693-1301-5.ch002
Sabzi, S., Abbaspour-Gilandeh, Y., Javadikia, H. 2017. Machine vision system for the automatic segmentation of plants under different lighting conditions. Biosystems Engineering 161, 157–173. https://doi.org/10.1016/j.biosystemseng.2017.06.021
Zhang, H., Cisse, M., Dauphin, Y. N., Lopez-Paz, D. 2018. mixup: Beyond Empirical Risk Minimization. International Conference on Learning Representations, Vancouver, BC, Canada, 1–13. https://doi.org/10.48550/arXiv.1710.09412
Zhang, W., Liu, D., Wang, C., Liu, R., Wang, D., Yu, L., Wen, S. 2022. An Improved Python-Based Image Processing Algorithm for Flotation Foam Analysis. Minerals 12, 1126. https://doi.org/10.3390/min12091126
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Kristóf Kozma-Bognár, József Berke, Angéla Anda, Veronika Kozma-Bognár
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Cikkre a Creative Commons 4.0 standard licenc alábbi típusa vonatkozik: CC-BY-NC-ND-4.0. Ennek értelmében a mű szabadon másolható, terjeszthető, bemutatható és előadható, azonban nem használható fel kereskedelmi célokra (NC), továbbá nem módosítható és nem készíthető belőle átdolgozás, származékos mű (ND). A licenc alapján a szerző vagy a jogosult által meghatározott módon fel kell tüntetni a szerző nevét és a szerzői mű címét (BY).