Supervised Learning for Edible Mushroom Identification: Promising Results and Implications for Food Safety
Keywords:
Edible Mushroom Identification, Machine Learning, Decision Tree Classifier, AccuracyAbstract
This Proposal focuses on the identification of edible mushrooms using supervised learning techniques. Mushroom identification plays a crucial role in ensuring food safety and preventing the consumption of toxic or poisonous varieties. By leveraging the power of supervised learning algorithms, we aim to develop an automated system capable of accurately classifying mushrooms as edible or non-edible. The proposed methodology involves extracting relevant features from mushroom samples and training a supervised learning model on a labelled dataset. Through rigorous experimentation and evaluation, it aims to achieve high classification accuracy, contributing to the field of mushroom identification and promoting safe consumption practices. Accurate identification of edible mushrooms is crucial for ensuring food safety and preventing potential health risks. This project attempts to create an automated system that can categorize mushrooms as edible or non-edible based on their properties by utilizing the capability of supervised learning algorithms. A supervised learning model is trained using a labelled dataset after relevant features from mushroom samples have been extracted. The objective is to obtain high classification accuracy and make a contribution to the field of mushroom identification through extensive experimentation and evaluation. The findings of this research have the potential to enhance mushroom identification processes, promote safe consumption practices, and reduce the risk of mushroom-related health issues.
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Li Sijia, Tan Lan, Zhuang Yu, Yu Xiuliang 2020 – ‘Comparison of the prediction effect between the Logistic Regressive model and SVM model’
FitrianaHarahap, Ahir Yugo Nugroho Harahap, EvriEkadiansyah et al. 2018 ‘Implementation of Naïve Bayes Classification Method for Predicting Purchase’
VijayaKumar.K, Lavanya.B, Nirmala.I, Sofia Caroline.S et al. 2019 – ‘Random Forest Algorithm for the Prediction of Diabetes’
MehrbakhshNilashi, Othman bin Ibrahim, Hossein Ahmadi, Leila Shahmoradi et al. 2019. ‘An analytical method for disease prediction using machine learning techniques, Computers & Chemical Engineering’.
Amanpreet Singh, Narina Thakur, Aakanksha Sharma 2020 – ‘A review of Supervised machine learning algorithms’. https://medium.com/@synced/how-random-forest-algorithm-works inmachine-learning-3c0fe15b6674.
Nagulwar, M., D. More, and L. Mandhare, Nutritional properties and value addition of mushroom: a review. The Pharma Innovation Journal, 2020. 9(10): p. 395-398.
Boa, E.R., Wild edible fungi: a global overview of their use and importance to people. 2004.
Kousalya, K., et al. Edible Mushroom Identification Using Machine Learning. in 2022 International Conference on Computer Communication and Informatics (ICCCI). 2022. IEEE.
Ismail, S., A.R. Zainal, and A. Mustapha. Behavioral features for mushroom classification. in 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). 2018. IEEE.
Mahesh, B., Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 2020. 9: p. 381-386.
Ottoman, M.A., N.A. Alawad, and K. Nahar, Classification of mushroom fungi using machine learning techniques. International Journal of Advanced Trends in Computer Science and Engineering, 2019. 8(5): p. 2378-2385.
Hamonangan, R., M.B. Saputro, and C.B.S.D.K. Atmaja, Accuracy of classification of poisonous or edible mushrooms using naïve bayes and k-nearest neighbors. Journal of Soft Computing Exploration, 2021. 2(1): p. 53- 60.
Halili, F. and F. Kamberi, Performance analysis of classification Algorithms: A case
Study of Naïve Bayes and J48 in Big Data. Applied Mathematics and Computation. 2(2): p. 50-57.
Neha Sharma, P. William, Kushagra Kulshreshtha, Gunjan Sharma, Bhadrappa Haralayya, Yogesh Chauhan, Anurag Shrivastava, “Human Resource Management Model with ICT Architecture: Solution of Management & Understanding of Psychology of Human Resources and Corporate Social Responsibility”, JRTDD, vol. 6, no. 9s(2), pp. 219–230, Aug. 2023.
William, P., Shrivastava, A., Chauhan, P.S., Raja, M., Ojha, S.B., Kumar, K. (2023). Natural Language Processing Implementation for Sentiment Analysis on Tweets. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore. https://doi.org/10.1007/978-981-19-7982-8_26
K. Maheswari, P. William, Gunjan Sharma, Firas Tayseer Mohammad Ayasrah, Ahmad Y. A. Bani Ahmad, Gowtham Ramkumar, Anurag Shrivastava, “Enterprise Human Resource Management Model by Artificial Intelligence to Get Befitted in Psychology of Consumers Towards Digital Technology”, JRTDD, vol. 6, no. 10s(2), pp. 209–220, Sep. 2023.
Kumar, A., More, C., Shinde, N. K., Muralidhar, N. V., Shrivastava, A., Reddy, C. V. K., & William, P. (2023). Distributed Electromagnetic Radiation Based Sree Lakshmi, P., Deepak, A., Muthuvel, S.K., Amarnatha Sarma, C Design and Analysis of Stepped Impedance Feed Elliptical PatchAntenna Smart Innovation, Systems and Technologies, 2023, 334, pp. 63
Gupta, A., Mazumdar, B.D., Mishra, M., ...Srivastava, S., Deepak, A., Role of cloud computing in management and education, Materials Today: Renewable Energy Assessment Using Novel Ensembling Approach. Journal of Nano-and Electronic Physics, 15(4).
William, P., Shrivastava, A., Shunmuga Karpagam, N., Mohanaprakash, T.A., Tongkachok, K., Kumar, K. (2023). Crime Analysis Using Computer Vision Approach with Machine Learning. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore. https://doi.org/10.1007/978-981-19-7982-8_25
Al-Mejibli, I.S. and D.H. Abd, Mushroom Diagnosis Assistance System Based on Machine Learning by Using Mobile Devices. Journal of Al-Qadisiyah for computer science and mathematics, 2017. 9(2): p. Page 103-113.
Alkronz, E.S., et alPrediction of whether mushroom is edible or poisonous using back-propagation neural network. 2019.
Maurya, P. and N.P. Singh. Mushroom classification using feature-based machine learning approach. in Proceedings of 3rd International Conference on Computer Vision and Image Processing. 2020. Springer.
Choomuang, N., et al. Mushroom Classification by Physical Characteristics by Technique of k-Nearest Neighbor in 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). 2020. IEEE.
Verma, S. and M. Dutta, Mushroom classification using ANN and ANFIS algorithm. IOSR Journal of Engineering (IOSRJEN), 2018. 8(01): p. 94-100
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