Original scientific paper
https://doi.org/10.32985/ijeces.12.3.4
Expiry Prediction and Reducing Food Wastage using IoT and ML
Kartik Nair
orcid.org/0000-0002-2455-8557
; Dwarkadas J. Sanghvi College of Engineering, Student of Electronics and Telecommunication, Department of Electronics and Telecommunication Mumbai, India
Bhavya Sekhani
; Dwarkadas J. Sanghvi College of Engineering, Student of Electronics and Telecommunication, Department of Electronics and Telecommunication Mumbai, India
Krina Shah
; Dwarkadas J. Sanghvi College of Engineering, Student of Electronics and Telecommunication, Department of Electronics and Telecommunication Mumbai, India
Sunil Karamchandani
orcid.org/0000-0001-6607-1440
; Dwarkadas J. Sanghvi College of Engineering, Faculty of Electronics and Telecommunication, Department of Electronics and Telecommunication Mumbai, India
Abstract
This paper details development of a low-cost, small-size, and portable electronic nose (E-nose) for the prediction of
the expiry date of food products. The Sensor array is composed of commercially available metal oxide semiconductors sensors like
MQ2 sensor, temperature sensor, and humidity sensor, which were interfaced with the help of ESP8266 and Arduino Uno for data
acquisition, storage, and analysis of the dataset consisting of the odor from the fruit at different ripening stages. The developed
system is used to analyze gas sensor values from various fruits like bananas and tomatoes. Responding signals of the e-nose were
extracted and analyzed. Based on the obtained data we applied a few machine learning algorithms to predict if a banana is stale
or not. Logistic regression, Decision Tree Classifier, Support Vector Classifier (SVC) & K-Nearest Neighbours (KNN) classifiers were the
binary classification algorithms used to determine whether the fruit became stale or not. We achieved an accuracy of 97.05%. These
results prove that e-nose has the potential of assessing fruits and vegetable freshness and predict their expiry date, thus reducing
food wastage.
Keywords
electronic nose (E-nose); MQ2 sensor; food expiry prediction; food wastage; machine learning
Hrčak ID:
261650
URI
Publication date:
27.8.2021.
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