Expiry Prediction and Reducing Food Wastage using IoT and ML

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

SOCIAL DISTANCE ENCOURAGER AND MOTIVATION SYSTEM

This paper details the development of a wearable device that would encourage and motivate people to maintain social distancing, thus also ensuring the safety and health of individuals. React Native, Redux and, Async storage has been used for the front-end development whereas NodeJS, ExpressJS and, MySQL have been used for the back-end. The distance is monitored with a smartphone’s in-built geo-location sensor, warning the user to maintain a distance of six feet. To obtain higher accuracy, ultrasonic sensors are used in conjunction with the mobile application. The band is tethered with the mobile to alert the user once the six-feet norm is violated