We present SmrtFridge, a consumer-grade smart fridge prototype that demonstrates two key capabilities: (a) identify the individual food items that users place in or remove from a fridge, and (b) estimate the residual quantity of food items inside a refrigerated container (opaque or transparent). Their awareness was reflected in how they adapted their shopping behavior as one way to reduce waste at home.

These findings are a result of increased awareness, the constant presence and immediacy of E-COmate served as a reminder, and their understanding of how much they actually waste as a group. In sum, eco-feedback as provided by E-COmate had positive impacts on reducing food waste. Eco-feedback further engaged residences in reducing food waste practices starting at the grocery store (e.g., by buying in smaller portions). Furthermore, while our control group showed an increase of 244% of waste of starches and grains toward the last 2 weeks (i.e., the end of term) compared to the 2 baseline weeks, the intervention group only showed an increase of 4.5% in waste of grains and starches. Participants who had E-COmate installed in their kitchens showed overall a significant decrease in food waste and in particular a decrease of almost 32% in edible or once edible food waste, and a 69% decrease in generated compost waste during the last 2 weeks compared to the first 2 baseline weeks. We show that E-COmate had a positive impact on participants' awareness of and behavior toward their food waste. Retrospective interviews were carried out to gain insights in residences' reflections and motivations. Waste audits were conducted to explore waste changes. To explore its impact, a mix of methods was adopted. The aim of the study was to see whether a system like E-COmate could impact food waste patterns and awareness, and if so, to what extent it engages consumers. Four E-COmate smart bins were introduced, deployed and evaluated for 8 weeks at a student residence in Canada. Our E-COmate system captures and visualizes domestic food waste data for more readily comprehensible and accessible information within a home environment embedded in an existing waste bin. We explored the design challenges and effectiveness of novel eco-feedback techniques at the point of food waste through an in-situ study in a university residence. However, little attention has been paid to the design of encouraging awareness of waste in the home, particularly at the point of food waste. A growing area of concern in sustainable living is food waste, and many new institutional waste receptacles incorporate waste sorting and recycling instructions for waste management. In the end, real-time testing results are also presented which validates the system's performance.Įco-feedback aims at increasing awareness of resource use to encourage conservation. A total of eight test cases are considered with accuracy and training time as the performance metric. The designed module and algorithm have been elaborated in detail and are considerably evaluated for its accuracy using test images on standard fruits and vegetable datasets. To address these issues, this work proposes an automated identification algorithm for computer vision in smart refrigerators using InceptionV3 and MobileNet Convolutional Neural Network (CNN) architectures. But a major issue is the high cost of available smart refrigerators and the lack of accurate design algorithms which can help achieve computer vision in any ordinary refrigerator. Smart refrigerators have been very successful in playing a pivotal role in mitigating this problem of food wastage. According to a survey by the Food and Agriculture Organization of the United Nations (FAO), it has been found that 1.3 billion tons of food is wasted by consumers around the world due to either food spoilage or expiry and a large amount of food is wasted from homes and restaurants itself.

In particular the need for automating the process of real-time food item identification, there is a huge surge of research so as to make smarter refrigerators. Machine Learning has evolved with a variety of algorithms to enable state-of-the-art computer vision applications.

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