FridgeSnap: A software for recipe suggestion based on food image classification

The reduction of food losses and waste is part of the 2030 Agenda Sustainable Development Goals (target 12.3) and, consequently, is one of the EU’s political priority in the field of circular economy. In this framework, the target 12.3 of the UN’s 2030 Agenda for Sustainable Development stated, by 2030, a not quantified reduction in post-harvest losses and a halving of food waste at the retail and consumers levels; while the EU Commission has recently set the target, by 2030, of reducing by 10% food losses occurred at the stage of processing and manufacturing and by 30% food waste occurred at the stage of retail and consumption. Food losses and waste should primarily be considered an expression of inefficiency of agricultural production processes determining several environmental, social and economic impacts. The quantification of food losses and waste is still an unsolved problem so much so that, in EU; currently available data are only related to the period 2020–2021 and, in many cases, are based on estimates and are not uniform in individual Member States. This makes reliable predictions of results achievable by the European Union particularly difficult. The aim of this paper is to analyze whether the plan of losses and waste reduction established by the EU Commission is able to both ensure the achievement of 2030 Agenda target 12.3 and to make a contribution to improving the sustainability of agri-food systems. In doing so, in addition to Eurostat data on losses and waste, the EU-Commission projections and the latest results of recent studies on environmental, social and economic impact of food losses and waste in EU were used, including indicators on hidden costs of agrifood systems developed by FAO. What observed shows that the EU-Commission reduction plan, if implemented, should result in a reduction of losses and waste inadequate for complying with the recommendation given by the target 12.3. Furthermore, the EU-Commission’s decision not to provide for losses reduction at the agricultural stage should significantly limit environmental benefits, given that much of the negative environmental impacts (GHG, land and water use, etc.) are ascribable to this stage as are, according to FAO estimates, hidden environmental costs. Besides limiting environmental benefits, the EU-Commission reduction plan, focusing only on the food losses and waste reduction in the final stage of supply food chain, could lead to negative social and economic impacts caused by a decline in the demand, which will reduce supply, jobs losses and GDP reduction. The conclusion on this, is that the reduction of food losses and waste is an issue that cannot be addressed separated but needs to be included in a wider program of integrated and complementary measures as a whole clearly aimed at realizing forms of circular economy.

Food waste has been identified as one of the major factors that constitute numerous anthropogenic activities, especially in developing countries. There is a growing problem with food waste that affects every part of the waste management system, from collection to disposal; finding long-term solutions necessitates involving all participants in the food supply chain, from farmers and manufacturers to distributors and consumers. In addition to food waste management, maintaining food sustainability and security globally is crucial so that every individual, household, and nation can always get food. “End hunger, achieve food security and enhanced nutrition, and promote sustainable agriculture” are among the main challenges of global sustainable development (SDG) goal 2. Therefore, sustainable food waste management technology is needed. Recent attention has been focused on global food loss and waste. One-third of food produced for human use is wasted every year. Source reduction (i.e., limiting food losses and waste) and contemporary treatment technologies appear to be the most promising strategy for converting food waste into safe, nutritious, value-added feed products and achieving sustainability. Food waste is also employed in industrial processes for the production of biofuels or biopolymers. Biofuels mitigate the detrimental effects of fossil fuels. Identifying crop-producing zones, bioenergy cultivars, and management practices will enhance the natural environment and sustainable biochemical process. Traditional food waste reduction strategies are ineffective in lowering GHG emissions and food waste treatment. The main contribution of this study is an inventory of the theoretical and practical methods of prevention and minimization of food waste and losses. It identifies the trade-offs for food safety, sustainability, and security. Moreover, it investigates the impact of COVID-19 on food waste behavior.

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Abstract: This work aims to find new techniques for food type recognition, diabetes disease prediction and food recommendation have been proposed for managing the diabetic disease and for recommending food with required calorie for the persons detected with diabetes. Therefore, this Project work focuses on effective methods for identifying the food type and estimating its calorie values with reference to the calorie table. Moreover, it focuses on prediction of diabetic disease. Based on the predicted level of diabetes, food recommendations have been made for the required calorie values. By combining image classification with calorie prediction, the approach aims to empower users in making informed dietary choices and managing their caloric intake more effectively. The proposed method demonstrates promising outcomes in both food item classification accuracy and calorie estimation precision. Its potential application extends to supporting healthier eating habits and aiding dietary monitoring for individuals, dietitians, and the food industry. This technological approach could serve as a valuable tool in promoting nutritional awareness and combating diet-related health issues.

In 2015, the United Nations defined the Sustainable Development Goals (SDG), which include a target (12.3) on food waste. The target requires “by 2030, to halve per capita global food waste at the retail and consumer levels and to reduce food losses along production and supply chains, including post-harvest losses”. The target has increased awareness about the food waste problem and boosted research in food waste quantification. Nevertheless, there is a lack of studies that adopt a systematic approach to account for food waste providing disaggregated values per food supply chain stage and per food groups. Such an approach could support policy makers in prioritizing interventions for food waste reduction. To fill this gap, this paper presents a high-level top-down approach to food waste accounting in the European Union. The study aims to support the understanding of the mass flows associated with food production, consumption, and waste, addressing different food groups along the food supply chain. The method for accountin is the mass flow analysis. According to the results, cereals, fruit, and vegetables as the food groups are responsible for the highest amount of food waste, and the consumption stage to be responsible for the largest share of food waste for most food groups. This work highlights the need for further primary research on food waste generation in the EU. Ultimately, this would allow to robustly estimate the food waste generated at EU level, and establishing a more accurate baseline to track the progress towards SDG target 12.3.

Over 10% of the world’s population is undernourished, yet 1/3 of all food produced each year is lost or wasted. Such a level of inefficiency in the global food system has a significant economic, social, and environmental impact which has elicited calls for urgent global action. This paper responds to this call by developing an interdisciplinary framework focusing on legal, regulatory, accounting, and reporting frameworks to improve the prevention or reduction of food loss and waste (FLW). Mobilising a literature review, this paper advances a three-pronged suggestion for tackling FLW in UK seafood companies: the development of technological solutions in the form of sensors; the enactment of a comprehensive legal and regulatory reporting template for seafood companies; and finally, the development of accounting standards that mandate reporting beyond the current Food and Waste Accounting and Reporting Standard by the Water Resources Institute (WRI), which is modelled on voluntary compliance.

Background, aim, and objectivesThe purpose of the review is to assess the cultural practices and its influence on food waste in South Africa. Furthermore, it explores the reasons for food waste by looking at different cultural practice of various ethnic groups in South Africa. The growing concern of the challenges of waste on human health and the environment has pressured the world to come up with drastic measures on how to manage waste to reduce both environmental and public health impacts. One of the concerns is that food waste has become one of the main contributors to increased greenhouse gas emissions. However, South Africa does not have stringent measures to control or reduce food waste in its communities.MethodsAn extensive online search was conducted to assess the influence of culture on food waste in South Africa and explore how other countries’ cultural practices contribute to food waste. Information was retrieved from online reports, journals, and books.Results and conclusionsThe studies showed a diversity of cultures and practices in South Africa compared to other countries. For example, in affluent countries where food is available in large quantities, cultural practices have a major influence on food waste. Moreover, some studies highlighted the fact that even though most developing countries are overwhelmed by poor people, many of whom live below the poverty line, food waste is also a challenge. In South Africa, food waste generated during social activities that are related to cultural practices remains a serious challenge. This is because during the cultural and social events, food is prepared in large quantities, which ends up not being wholly consumed and resulting in an increase in food waste that gets disposed of at the landfill sites. The government of South Africa must institute awareness raising measures to inform communities to avoid or reduce the generation of food waste. This would reduce the emission of greenhouse gases and environmental impact, and to protect human health.

Household food waste presents a growing challenge to both economic security and environmental sustainability, particularly in rural agricultural regions. This study evaluated the economic and environmental implications of food waste at household level in Gauradaha Municipality, Jhapa district, Nepal. Based on a structured survey of 51 households using a self-administered questionnaire, the research examined factors influencing food waste generation and its associated costs. Data were analyzed using MS Excel and RStudio. One-way ANOVA, Pearson correlation, and proportions were analyzed in R to examine the effects of gender, education, and family size on waste generation, whereas MS Excel was used to assess the quantity of economic and environmental impacts. Economic valuation revealed that household food waste results in a loss of approximately NRS 35.21 (0.24 USD) per household per week, equivalent to less than 1% of annual household income. Environmentally, each household's food wastage contributes an estimated 0.92 kg of CO₂ emissions and consumes nearly 477.70 liters of water. Among food categories, cereals were identified as having the highest economic and environmental impact. Furthermore, overcooking emerged as the leading cause of waste, with refrigeration being the most commonly used storage method, yet no measures of value addition, processing and sustainable storage methods were practiced effectively, which reflects changing lifestyles, urbanization, and time constraints. These findings underscore the urgent need for targeted strategies that address food waste behaviour, promote efficient storage practices, and reduce the broader sustainability burdens at the household level in rural Nepal.

Image recognition is one of the most important fields of image processing and computer vision. Food image classification is an unique branch of image recognition problem. In modern days people are more conscious about their health. A system that can classify food from image is necessary for a dietary assessment system. Classification of food images is very challenging since the dataset of food images is highly non-linear. In this paper we proposed a method that can classify food categories with images. We used convolutional neural network to classify food images. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying., object detection and other computer vision problems. We classified a food dataset consisting different food categories with 16643 images. We obtained an accuracy of 92.86% in our experiment.

Classification of food images is now very popular in recent years with the development of vision technology. It is quite challenging work to classify food images as images are consist of different shape, size and color. We proposed a new food recognition system by using local features in this paper. We selected UEC-100 food dataset. It consist 100 types of food images which are popular food in Japan. We considered GIST and SURF as local features. We applied pre-processing task on the input image to improve image quality. Classification is performed by Support Vector Machine (SVM). GIST and SURF features are extracted separately. Classification shows poor result while features are used separately. Later when both features are combined together and creates hybrid feature, it results in higher accuracy. We classified images with different classifier. However, SVM as a classifier shows comparable and promising result.

Food waste in private households of industrialised countries is becoming an increasing concern, as its impact on greenhouse gas emissions is comparable to that of the aviation industry. One particular unnecessary contribution to food waste is a person forgetting which food items they have in their household, which often leads to food items expiring before they are used. This paper introduces FoodFighters a mobile serious game aimed at encouraging young adult players to interact with their food items thereby making them more memorable. FoodFighters works by taking pictures of food items, that then can be customized into so called Food Fighters, which are then used to battle other players/friends fighters. In two preliminary lab studies, we validated that Food Fighters actually improved retention of food items and found that players found the game enjoyable based on a GEQ-questionnaire.

The problem of food image classification has become a prominent topic that attracts many researchers due to its multiple benefits and applications in various aspects of life, from health to marketing. Image classification applications rely heavily on recent advancements in computer vision-based object recognition. In this paper, several deep transfer learning methods were investigated for food image classification. Furthermore, we applied a data augmentation approach to expand the Food-101 dataset. The impact of applying data augmentation and transfer learning was evaluated using five different deep learning models including Mobilenet, EfficientNetB1, and ResNet. It was noted that the EfficientNetB1 classifier achieved the best results with a score of 96.13%. In addition, we found that our data augmentation process was able to improve model performance.

Short communication: A food-systems approach to assessing dairy product waste