IntelliStore: IoT And AI‐based intelligent storage monitoring for perishable food

Abstract

Among all the food produced annually, the loss rate for fruits and vegetables is the highest. This is due to the inability to detect critical ambient environmental parameters in cold storage. The self‐life of food plays an important characteristic in minimizing loss. We present IntelliStore in this work, an intelligent Machine Learning (ML) and Internet of Things (IoT) powered storage monitoring system that enables real‐time monitoring of temperature, humidity & concentration, and pest detection using a Passive Infrared (PIR) sensor and microphone. We collected a dataset of 10 different fruit and vegetables from the Food Quality and Analysis lab. We have experimented with SVM, Decision Tree, AdaBoost, and Gradient Boosting machine learning algorithms and have achieved the highest accuracy of 88% with SVM. Moreover, the proposed system has additional functionality to update the dataset with actual observations in the future and retraining models that would allow improvement of ML performance over time.