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Convolutional neural network based mobile application for poisonous mushroom detection


Shazlin Nizam Amirul Zen Computer System, Persiaran Flora, Cyber 12, 63000 Cyberjaya, Selangor, Malaysia Norlina Mohd Sabri Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA Cawangan Terengganu, Kampus Kuala Terengganu, Malaysia Gloria Jennis Tan Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA Cawangan Terengganu, Kampus Kuala Terengganu, Malaysia Nurul Ainina Redwan Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA Cawangan Terengganu, Kampus Kuala Terengganu, Malaysia Zhiping Zhang College of Computer and Mathematics, Xinyu University, Jiangxi, P. R. China
Abstract
Mushroom identification and classification are critical areas of research due to the significant health risks posed by poisonous varieties. Poisonous mushrooms present a considerable threat to public safety as they can be easily mistaken for edible varieties, potentially leading to severe poisoning or even death. The lack of accessible and reliable resources for accurately distinguishing between edible and poisonous mushroom species could result in a growing number of fatalities and health complications within the population. Furthermore, the process of mushroom classification itself is inherently time-consuming, demanding a substantial investment of resources and a comprehensive understanding of mycology. To address these issues, this study aims to develop a mushroom detection prototype specifically for identifying poisonous mushrooms using a mobile application. The application leverages a Convolutional Neural Network (CNN) algorithm to accurately classify mushrooms based on user-submitted images. CNN is one of the deep learning algorithms that is well known for its good performance in image recognition and classification. There are 3 main phases of the research methodology, which cover the data collection and preprocessing, model design and implementation, and performance evaluation. In this study, the developed model achieved a good accuracy of 89%, indicating acceptable performance in distinguishing between edible and poisonous mushrooms. This good accuracy underscores the model's reliability and effectiveness in real-world applications, making it a valuable tool for ensuring public safety.
Keyword: CNN, Mobile Application, Poisonous Mushroom, Detection
DOI: 10.24191/esteem.v21iSeptember.7137.g4960
References:
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Mzoughi, N. Riaz, M. Mujahid, M. Faheem, M. Tausif, and A. R. Khan, "Mushroom Species Classification in Natural Habitats Using Convolutional Neural Networks (CNN)," IEEE Access, vol. 12, pp. 176818–176832, Nov. 2024. Available: https//doi.org/ 10.1109/ACCESS.2024.3502543 [14]Y. Demirel and G. Demirel, "Deep Learning Based Approach for Classification of Mushrooms," Journal of Science Part A: Engineering and Innovation, vol. 10, no. 4, pp. 487–498, 2023. Available: https//doi.org/10.54287/gujsa.1355751 [15]P. M. Jacob et al., "An Intelligent System for Cultivation and Classification of Mushrooms Using Machine Vision," in Proc. Int. Conf. Inf. Syst. Eng. Sci. (CISES), Tamil Nadu, India, 2023. [Online]. Available: https://doi.org/10.1109/cises58720.2023.10183464 [16]C. Chaschatzis, C. Karaiskou, S. K. Goudos, K. E. Psannis, and P. Sarigiannidis, "Detection of Macrolepiota Procera Mushrooms Using Machine Learning," in Proc. IEEE World Symp. Commun. Eng. 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Available: https://universe.roboflow.com/huurhuntergel/mushroom-nzafz [24]L. Wu and Y. Chen, "Mushroom Recognition and Classification Based on Convolutional Neural Network," in Proc. 5th IEEE Adv. Inf. Manage., Commun., Electron. and Automat. Control Conf. (IMCEC), Chongqing, China, Dec. 2022. [Online]. Available: https://doi.org/10.1109/imcec55388.2022.10019866

[1] J. S. Ortiz-Letechipia et al., "Classification and Selection of the Main Features for the Identification of Toxicity in Agaricus and Lepiota with Machine Learning Algorithms," PeerJ, vol. 12, p. e16501, 2024. [Online]. Available: https://doi.org/10.7717/peerj.16501

[2] R. Gürfidan and Z. Akçay, "Real-Time Deep Learning-Based Mobile Application for Detecting Edible Fungi: MushAPP," Int. J. Intell. Syst. Appl. (IJISA), vol. 16, no. 5, pp. 1–9, 2024

[3] M. S. Ahmed et al., "Comparative Analysis of Interpretable Mushroom Classification using Several Machine Learning Models," in Proc. 25th Int. Conf. Comput. Commun. and Inform. Technol. (ICCIT), Dhaka, Bangladesh, Dec. 2022. [Online]. Available: https://doi.org/10.1109/iccit57492.2022.10055555

[4] D. S. L. Manikanteswari and C. S. Kalyani, "Classification of Edible and Poisonous Mushrooms Using Machine Learning Algorithms," Int. J. Multidiscip. Res. (IJFMR), vol. 6, no. 2, pp. 1–8, 2024

[5] M. Abinaya and R. Sathish Kumar, "Edible and Poisonous Mushrooms Classification Using Multi-Layer Perceptron Algorithm," Int. J. Res. Publ. Rev. (IJRPR), vol. 5, no. 8, pp. 1351–1356, 2024.

[6] H. Ujir, I. Hipiny, M. H. Bolhassan, K. N. F. Ku Azir, and S. A. Ali, "Automating Mushroom Culture Classification: A Machine Learning Approach," Int. J. Adv. Comput. Sci. Appl. (IJACSA), vol. 15, no. 4, pp. 519–522, 2024

[7] V. R. Kaneti et al., "Supervised Learning for Edible Mushroom Identification: Promising Results and Implications for Food Safety," Int. J. Intell. Syst. Appl. Eng. (IJISAE), vol. 12, no. 8s, pp. 290–298, 2024

[8] W. H. Sevilla, R. M. Hernandez, M. A. D. Ligayo, M. T. Costa, and A. Q. Quismundo, "Machine Vision Recognition System of Edible and Poisonous Mushrooms Using a Small Training Set-Based Deep Transfer Learning," in Proc. Int. Conf. Decision Aid Sci. and Appl. (DASA), Chiangrai, Thailand, Mar. 2022. [Online]. Available: https://doi.org/10.1109/dasa54658.2022.9765046

[9] J. A. Eleiwy, M. M. Shwaysh, A. M. Kadhim, and A. A. Nafea, "Enhancing Mushroom Detection Using One-Dimensional Convolutional Neural Networks," Fusion Practice and Applications, vol. 20, no. 2, pp. 1–12, 2025

[10] J. P. Singh, D. Ghosh, J. Singh, A. Bhattacharjee, and M. K. Gourisaria, "Optimized DenseNet Architectures for Precise Classification of Edible and Poisonous Mushrooms," International Journal of Computational Intelligence Systems, vol. 18, no. 1, 2025. Available: https//doi.org/10.1007/s44196-025-00871-y

[11] J. Y. Lim, Y. Y. Wee, and K. K. Wee, "Machine Learning and Image Processing-Based System for Identifying Mushrooms Species in Malaysia," Applied Sciences, vol. 14, no. 15, 2024. Available: https//doi.org/10.3390/app14156794

[12] S. Subramani, F. A. Imran, A. Ttm, S. M. Karthik, and J. Yaswanth, "Deep Learning Based Detection of Toxic Mushrooms in Karnataka," Procedia Computer Science, vol. 235, pp. 91–101, 2024. Available: https//doi.org/10.1016/j.procs.2024.04.012

[13] R. N. Bashir, O. Mzoughi, N. Riaz, M. Mujahid, M. Faheem, M. Tausif, and A. R. Khan, "Mushroom Species Classification in Natural Habitats Using Convolutional Neural Networks (CNN)," IEEE Access, vol. 12, pp. 176818–176832, Nov. 2024. Available: https//doi.org/ 10.1109/ACCESS.2024.3502543

[14] Y. Demirel and G. Demirel, "Deep Learning Based Approach for Classification of Mushrooms," Journal of Science Part A: Engineering and Innovation, vol. 10, no. 4, pp. 487–498, 2023. Available: https//doi.org/10.54287/gujsa.1355751

[15] P. M. Jacob et al., "An Intelligent System for Cultivation and Classification of Mushrooms Using Machine Vision," in Proc. Int. Conf. Inf. Syst. Eng. Sci. (CISES), Tamil Nadu, India, 2023. [Online]. Available: https://doi.org/10.1109/cises58720.2023.10183464

[16] C. Chaschatzis, C. Karaiskou, S. K. Goudos, K. E. Psannis, and P. Sarigiannidis, "Detection of Macrolepiota Procera Mushrooms Using Machine Learning," in Proc. IEEE World Symp. Commun. Eng. (WSCE), Thessaloniki, Greece, Nov. 2022. [Online]. Available: https://doi.org/10.1109/wsce56210.2022.9916046

[17] H. Zhao, F. Ge, P. Yu, and H. Li, "Identification of Wild Mushroom Based on Ensemble Learning," in Proc. Int. Conf. Big Data Artif. Intell. (BDAI), Qingdao, China, 2021. [Online]. Available: https://doi.org/10.1109/bdai52447.2021.9515225

[18] D. Perdios, M. Vonlanthen, F. Martinez, M. Arditi, and J. Thiran, "CNN-Based Ultrasound Image Reconstruction for Ultrafast Displacement Tracking," IEEE Trans. Med. Imag., vol. 40, no. 3, pp. 1078–1089, Mar. 2021. [Online]. Available: https://doi.org/10.1109/tmi.2020.3046700

[19] ] N. Kiss and L. Czúni, "Mushroom Image Classification with CNNs: A Case-Study of Different Learning Strategies," in Proc. Int. Symp. Image and Signal Process. and Anal. (ISPA), Zagreb, Croatia, 2021. [Online]. Available: https://doi.org/10.1109/ispa52656.2021.9552053

[20] N. Zahan, M. Z. Hasan, A. Malek, and S. S. Reya, "A Deep Learning-Based Approach for Edible, Inedible and Poisonous Mushroom Classification," in Proc. Int. Conf. Inf. Commun. Technol. Sustainable Dev. (ICICT4SD), Dhaka, Bangladesh, 2021. [Online]. Available: https://doi.org/10.1109/icict4sd50815.2021.9396845

[21] C. Tardi, "The 80-20 Rule (aka Pareto Principle): What It Is, How It Works," Investopedia, Dec. 19, 2023. [Online]. Available: https://www.investopedia.com/terms/1/80-20-rule.asp

[22] GunKurnia, "Choosing the Optimal Data Split for Machine Learning: 80/20 vs 70/30," Medium, Jun. 14, 2024. [Online]. Available: https://medium.com/@gunkurnia/choosing-the-optimal-data-split-for-machine-learning-80-20-vs-70-30-14ed37f3c686 [21] C. Tardi, "The 80-20 Rule (aka Pareto Principle): What It Is, How It Works," Investopedia, Dec. 19, 2023. [Online]. Available: https://www.investopedia.com/terms/1/80-20-rule.asp

[23] Huurhuntergel, "download (36).jpeg," Roboflow Universe. [Online]. Available: https://universe.roboflow.com/huurhuntergel/mushroom-nzafz

[24] L. Wu and Y. Chen, "Mushroom Recognition and Classification Based on Convolutional Neural Network," in Proc. 5th IEEE Adv. Inf. Manage., Commun., Electron. and Automat. Control Conf. (IMCEC), Chongqing, China, Dec. 2022. [Online]. Available: https://doi.org/10.1109/imcec55388.2022.10019866