ct 18: e1

Research Article

IoT Based Detection of Molded Bread and Expiry Prediction using Machine Learning Techniques

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  • @ARTICLE{10.4108/eai.27-4-2022.173972,
        author={Muhammad Shoaib Akhtar and Tao Feng},
        title={IoT Based Detection of Molded Bread and Expiry Prediction using Machine Learning Techniques},
        journal={EAI Endorsed Transactions on Creative Technologies: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={CT},
        year={2022},
        month={4},
        keywords={IoT, ML, SVM},
        doi={10.4108/eai.27-4-2022.173972}
    }
    
  • Muhammad Shoaib Akhtar
    Tao Feng
    Year: 2022
    IoT Based Detection of Molded Bread and Expiry Prediction using Machine Learning Techniques
    CT
    EAI
    DOI: 10.4108/eai.27-4-2022.173972
Muhammad Shoaib Akhtar1, Tao Feng1,*
  • 1: School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
*Contact email: fengt@lut.cn

Abstract

Expiration of a bread is a very popular issue in food logistics. Due to various conditions fungal bread can cause food poisoning for consumers. As a result, nausea, diarrhea and different medical issues appear in people. For this purpose, an intelligent system required for the detection of present condition of bread is required which will help the stores and consumers. In this study, we have developed a prototype made up of Arduino Nano as a microcontroller, MQ series sensors for CO and CO2 detection in shopper bags of bread in order to collect data. This data is further processed in different machine learning algorithms for the detection of current condition of bread in these stores. The data collected from these sensors was imbalanced. Data collected from sensors is then balanced by using SMOTE and TOMEC Links (data balancing techniques). Furthermore, data preprocessing and feature engineering has been applied on IoT Based dataset to improve its efficiency. We have applied linear learning models for the prediction of current condition of bread. Within linear models, Gaussian Naïve Bayes has scored highest accuracy of 81.54%.