IoT Based Grocery Management System

In this project we have targeted multiple solution for different sectors like industries, grocery shopping and avoiding food wastage. Our project provides the low-budget management system for the user which will help him/her to manage his/her grocery smartly and our system provides services globally

2025-06-28 16:33:31 - Adil Khan

Project Title

IoT Based Grocery Management System

Project Area of Specialization Internet of ThingsProject Summary

In this project we have targeted multiple solution for different sectors like industries, grocery shopping and avoiding food wastage. Our project provides the low-budget management system for the user which will help him/her to manage his/her grocery smartly and our system provides services globally. Our project will also help the beverage industries to cut down their huge expenses, which will result in making multiple times the profit they use to make previously. Fixing our system and implementing our idea will surely help this industry to gain maximum profit per annum. According to UN Food and Agriculture Organization (FAO), developing countries waste 40 percent food items, 1.3 billion tons of food is wasted annually all over the world. Almost a billion people are going hungry, out of which one-third are children. While we waste 1/3 of the food we produce. The amount of food waste produced globally each year is more than enough to feed all of the hungry people in the world. By using our grocery management system, the user will be updated about the item which are in vegetable box and in refrigerator’s shelf, also he will know about the left over items in his refrigerator which he might have place days ago and is not in a mood of using it again. And he/she can further play his/her part in preventing food wastage by giving that left over food to some poor or needy person before it gets rot. We have designed a smart cabinet in which we will place different items as per our need and their weight will be accessible to us on our web application. We will be getting weight of item in real time data.

Project Objectives Project Implementation Method

We have divided our project in two different task. First task was to make a smart cabinet, for that we have used load cells to measure the weight of the items and have interfaced them with NodeMCU. This microcontroller has built-in Wi-Fi chip with the help of which we have transferred this data to the Google Firebase Real time database. We made the separate web application using HTML, CSS and JavaScript, and linked this application with the database. Now the user will enter his ID and password in our web app and he/she can see the data of his/her smart cabinet, globally. In addition, if any item weight in below the default weight then an automatic online order will be placed to the specific store for home delivery.

Our second task was to make a smart refrigerator and for that we have used 3 cameras. All these cameras are interfaced with Raspberry Pi. The image processing is being performed on the image from the camera at egg shelf. The other two camera capture the image of  shelf of refrigerator and of the vegetable box. The data from the image processing and the pictures from all the cameras can be accessed through the same web application which we have used to access smart cabinet.

For prototype, we have also made a dummy website like HumMart, TazaMart etc and when our food was finished, an automatic online order was placed at this website. 

Benefits of the Project Technical Details of Final Deliverable

In our project, for Smart Cabinet, we use load cells for weight measurement of the goods placed in the kitchen. These goods can be rice, vegetables, flour, spices, pulses etc. These load cells are interfaced with NodeMCU, which will sends the data to the Google Firebase. Previous smart refrigerators used sensors to measure the food and thus contained many wires.  We have placed three cameras in our Smart Refrigerator and thus the number of wires is greatly reduced. One of them is Raspberry Pi camera and the other two are USB cameras. One camera is placed on the egg shelf, one is placed in the vegetable box, and one is placed on the wall of refrigerator. All these cameras are connected to the Raspberry Pi, which is also placed inside the refrigerator. The Raspberry Pi is placed inside the casing so that it isn’t affected by the humidity. The camera placed on the egg shelf takes the picture and sends that picture to the Raspberry Pi which performs image processing on that picture using custom-made algorithms and the number of eggs is deduced from that picture. The cameras placed inside the vegetable box and on the refrigerator’s door take the pictures and send it to Raspberry Pi so that user will know what vegetables are placed inside the vegetable box and what contents are placed in the refrigerator shelves while looking at the pictures. The camera which is placed on the egg shelf is an IR camera and can take pictures even when the door is closed. All the data from the Raspberry Pi is also sent to the Real-time database of Google Firebase. This real-time database updates the data of NodeMCU and Raspberry Pi in real-time and thus users don’t face any delays. We have also made a dedicated web application. Through this application, users can get all the data from the Firebase. Using Node.js and API of the grocery delivery website, an online order is placed.

The load cells are attached with acrylic sheets. These load cells will be placed in the kitchen and different food items can be placed on top of it. Load cells are connected with HX711 module. Through this module, all the data is transferred to NodeMCU. Basically the working of HX711 module is to convert analogue data into digital data. This NodeMCU is interfaced with load cells. It calibrates the load cells and sends all the data to the Google Firebase at regular intervals. And then it is displayed on web application which user can access using his username and password.

We have used Raspberry Pi Model 3 B+ for our project. All the pictures are sent to Raspberry Pi. It performs image processing on the picture of egg shelf and deduce the number of eggs. All the pictures and the deduced data are then sent to the real-time database of Google Firebase. The real-time database of Firebase receives all the data from the NodeMCU and Raspberry Pi. This database is then linked with a web application and user can access his/her smart cabinet or smart refrigerator using his/her username and password.

Final Deliverable of the Project HW/SW integrated systemType of Industry IT , Food , Others Technologies Internet of Things (IoT)Sustainable Development Goals Industry, Innovation and Infrastructure, Responsible Consumption and ProductionRequired Resources
Item Name Type No. of Units Per Unit Cost (in Rs) Total (in Rs)
Total in (Rs) 22200
Load cells Equipment56003000
NodeMCU Equipment36001800
PiCamera Equipment130003000
usb camera Equipment232006400
raspberry pi 3 B Equipment165006500
acrylic sheet Equipment115001500

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