2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 20 , Issue 2 , PP: 01-12, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Jabbar Abed Eleiwy 1 , Mustafa Muslih Shwaysh 2 , Ahmed Mubdir Kadhim 3 * , Ahmed Adil Nafea 4 * , Aythem Khairi Kareem 5 , Mustafa Nadhim Owaid 6
Doi: https://doi.org/10.54216/FPA.200201
The classification of mushrooms as either deadly or edible stays a important challenge due to their similar appearances, which can lead to fatal poisonings. The primary difficulty lies in identifying complex patterns in mushroom appearances, such as cap shape, color, and gill structure, which complicate accurate classification. Traditional approaches and even some machine learning (ML) models fail to capture these subtle but important distinctions, leading to misclassifications. To address this issue, this paper proposed a One-Dimensional Convolutional Neural Network (1D-CNN) approach aimed at improving the accurate of mushroom classification. By effectively recognizing complex patterns in the mushroom data set, the proposed approach greatly improves classification accuracy. The model performance evaluated utilizing Precision, Accuracy, Recall, and F1-Score that achieved high scores of 100% across all metrics. These results highlight the strength of deep learning (DL) method, specifically 1D-CNNs, in recognizing with learning complex data patterns. This shows a clear advancement over traditional ML methods and ensemble techniques, establishing the 1D-CNN as a highly reliable tool for mushroom classification that can help reduce mushroom poisoning incidents.
Mushroom , One-Dimensional Convolutional Neural Network , Machine Learning , Classification , Deep learning ,
[1] M. M. Khalid and O. Karan, "Deep Learning for Plant Disease Detection," Int. J. Math., Stat. Comput. Sci., vol. 2, pp. 75–84, 2023, doi: 10.59543/ijmscs.v2i.8343.
[2] K. Yang, M. Zhao, and D. Argyropoulos, "Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system," Smart Agric. Technol., vol. 10, p. 100747, 2025.
[3] W. E. Brandenburg and K. J. Ward, "Mushroom poisoning epidemiology in the United States," Mycologia, vol. 110, no. 4, pp. 637–641, 2018.
[4] Y. Zhang et al., "Polymerase chain reaction-based methods for the rapid identification of Amanita exitialis," Food Chem., vol. 448, p. 139086, 2024.
[5] Y. Wang, J. Du, H. Zhang, and X. Yang, "Mushroom toxicity recognition based on multigrained cascade forest," Sci. Program., vol. 2020, no. 1, p. 8849011, 2020.
[6] N. Chitayae and A. Sunyoto, "Performance comparison of mushroom types classification using K-nearest neighbor method and decision tree method," in 2020 3rd Int. Conf. Inf. Commun. Technol. (ICOIACT), 2020, pp. 308–313.
[7] J. Ortega, A. C. Lagman, L. R. Q. Natividad, E. T. Bantug, M. R. Resureccion, and L. O. Manalo, "Analysis of performance of classification algorithms in mushroom poisonous detection using confusion matrix analysis," Int. J., vol. 9, no. 1.3, 2020.
[8] K. Kousalya, B. Krishnakumar, S. Boomika, N. Dharati, and N. Hemavathy, "Edible mushroom identification using machine learning," in 2022 Int. Conf. Comput. Commun. Informatics (ICCCI), 2022, pp. 1–7.
[9] A. B. Siddique et al., "Studying the effects of feature selection approaches on machine learning techniques for Mushroom classification problem," in 2023 Int. Conf. IT Ind. Technol. (ICIT), 2023, pp. 1–6.
[10] M. S. Morshed, F. Bin Ashraf, M. U. Islam, and M. S. R. Shafi, "Predicting Mushroom Edibility with Effective Classification and Efficient Feature Selection Techniques," in 2023 3rd Int. Conf. Robot. Electr. Signal Process. Tech. (ICREST), 2023, pp. 1–5.
[11] B. Singh, N. Dhanda, and R. Verma, "Applications of Machine Learning Techniques in the Classification of Mushrooms," in 2023 Int. Conf. IoT, Commun. Autom. Technol. (ICICAT), 2023, pp. 1–7.
[12] R. Sahu, S. Pandey, R. Verma, and P. Pandey, "Ensemble Learning based Classification of Edible and Poisonous Agaricus Mushrooms," in 2024 Fourth Int. Conf. Adv. Electr. Comput. Commun. Sustain. Technol. (ICAECT), 2024, pp. 1–7.
[13] W. saif, syaifuddin; Ferdiana, Ridi; Widyawan, "IoT-DH Dataset," Mendeley Data, 2024.
[14] H. Tao et al., "Development of integrative data intelligence models for thermo-economic performances prediction of hybrid organic rankine plants," Energy, p. 130503, 2024.
[15] M. Rath and H. Date, "Quantum data encoding: A comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy," EPJ Quantum Technol., vol. 11, no. 1, p. 72, 2024.
[16] A.-M. Manar, A. K. Kareem, A. A. Nafea, A. M. Shaban, S. A. S. Aliesawi, and M. M. AL-Ani, "An Effective Deep Learning Approach for the Estimation of Proton Energy by Using Artificial Neural Network," in 2024 21st Int. Multi-Conf. Syst. Signals Devices (SSD), 2024, pp. 257–262.
[17] E. U. H. Qazi, A. Almorjan, and T. Zia, "A one-dimensional convolutional neural network (1D-CNN) based deep learning system for network intrusion detection," Appl. Sci., vol. 12, no. 16, p. 7986, 2022.
[18] N. N. Jamil and A. K. Kareem, "Comparative Analysis on Machine Learning and One-Dimensional Convolutional Neural Network to Predict Surface Enhanced Raman Spectroscopy," in 2023 3rd Int. Conf. Comput. Inf. Technol. (ICCIT), 2023, pp. 216–221.
[19] Y. Chen, R. Chang, and J. Guo, "Effects of data augmentation method borderline-SMOTE on emotion recognition of EEG signals based on convolutional neural network," IEEE Access, vol. 9, pp. 47491–47502, 2021.
[20] S. A. Rafa, Z. M. Al-qfail, A. A. Nafea, S. F. Abd-hood, M. M. Al-Ani, and S. A. Alameri, "A Birds Species Detection Utilizing an Effective Hybrid Model," in 2024 21st Int. Multi-Conf. Syst. Signals Devices (SSD), 2024, pp. 705–710.
[21] Z. H. Abdaljabar, O. N. Ucan, and K. M. A. Alheeti, "An intrusion detection system for IoT using KNN and decision-tree based classification," in 2021 Int. Conf. Mod. Trends Inf. Commun. Technol. Ind. (MTICTI), 2021, pp. 1–5.
[22] Y. M. Mohialden, N. M. Hussien, S. A. Salman, A. B. A. Alwahhab, and M. Ali, "Enhancing Agriculture Crop Classification with Deep Learning," Babylonian J. Artif. Intell., vol. 2024, pp. 20–26, 2024.
[23] O. A. Mahdi, J. A. Eleiwy, Y. R. B. Al-Mayouf, and B. Al-Attar, "An efficient node selection algorithm in the context of IoT-based vehicular ad hoc network for emergency service," J. Intell. Syst., vol. 33, no. 1, 2024, doi: 10.1515/jisys-2024-0208.
[24] M. S. Ibrahim, J. A. Eleiwy, H. M. Muhi-Aldeen, Y. Al-Yasiri, and A. A. Nafea, "Human to Chatbot Text Classification Using Multi-Source AI Chatbots and Machine Learning Models," J. Intell. Syst. Internet Things, vol. 16, no. 1, pp. 152–165, 2025, doi: 10.54216/JISIoT.160113.
[25] T. Kavitha, G. Amirthayogam, J. J. Hephzipah, R. Suganthi, and T. Chelladurai, "Healthcare Analysis Based on Diabetes Prediction Using a Cuckoo-Based Deep Convolutional Long-Term Memory Algorithm," Babylonian J. Artif. Intell., vol. 2024, pp. 64–72, 2024.
[26] O. J. Kadhim, A. A. Nafea, S. A. S. Aliesawi, and M. M. Al-Ani, "Ensemble Model for Prostate Cancer Detection Using MRI Images," in 2023 16th Int. Conf. Developments eSystems Eng. (DeSE), 2023, pp. 492–497.
[27] S. Y. Mohammed, M. Aljanabi, and M. M. Mijwil, "Detecting Denial-of-Service (DoS) Attacks with Edge Machine Learning," in Annu. Int. Conf. Theory Appl. Cryptographic Tech., 1988, pp. 119–127.
[28] K. Tutuncu, I. Cinar, R. Kursun, and M. Koklu, "Edible and poisonous mushrooms classification by machine learning algorithms," in 2022 11th Mediterranean Conf. Embedded Comput. (MECO), 2022, pp. 1–4.
