Smart sensors and communication using IoT in supermarkets

Shelf monitor system

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This thesis tries to find a solution for the problem of managing and monitoring the banana shelf in a supermarket using IoT.
The research focuses on using a wireless sensor that detects some features of the banana shelf while being non-intrusive.
The three main features that are examined of the shelf are the quality and quantity of the bananas and the quality of the shelf.
First a research was conducted to find the best sensor to use for these measurements.
The chosen sensor is a color image sensor, the platform for the IoT device is a Raspberry Pi.
Using the python programming language in combination with the openCV library image processing was used to detect the features.

The image is first smoothed using a Gaussian filter, afterwards the foreground is segmented.
The different segmentation methods are researched and adaptive thresholding is used.
To determine the quantity of the bananas and quality of the shelf the stickers on the bananas are detected.
This detection is implemented using different filtering methods ranging from spectral filtering to color thresholding.
With the segmented foreground the quality of the bananas is assessed using a color histogram.
This information is then sent to a communication module that is connected to a IoT dashboard for user interpretation.

With the proposed design the status of the shelf including the percentage of the shelf filled, the quality of the bananas on the shelf and the neatness of the shelf are available for a supermarket manager to better organize his supermarket.
This sensor makes it possible to better organize the banana shelf and act preemptive instead of reactive.