Fine-Grained Food Image Recognition: A Study on Optimising Convolutional Neural Networks for Improved Performance

Abstract

Addressing the pressing issue of food waste is vital for environmental sustainability and resource conservation. While computer vision has been widely used in food waste reduction research, existing food image datasets are typically aggregated into broad categories (e.g., fruits, meat, dairy, etc.) rather than the fine-grained singular food items required for this research. The aim of this study is to develop a model capable of identifying individual food items to be integrated into a mobile application that allows users to photograph their food items, identify them, and offer suggestions for recipes. This research bridges the gap in available datasets and contributes to a more fine-grained approach to utilising existing technology for food waste reduction, emphasising both environmental and research significance. This study evaluates various (n = 7) convolutional neural network architectures for multi-class food image classification, emphasising the nuanced impact of parameter tuning to identify the most effective configurations. The experiments were conducted with a custom dataset comprising 41,949 food images categorised into 20 food item classes. Performance evaluation was based on accuracy and loss. DenseNet architecture emerged as the top-performing out of the seven examined, establishing a baseline performance (training accuracy = 0.74, training loss = 1.25, validation accuracy = 0.68, and validation loss = 2.89) on a predetermined set of parameters, including the RMSProp optimiser, ReLU activation function, ‘0.5’ dropout rate, and a 160×160 image size. Subsequent parameter tuning involved a comprehensive exploration, considering six optimisers, four image sizes, two dropout rates, and five activation functions. The results show the superior generalisation capabilities of the optimised DenseNet, showcasing performance improvements over the established baseline across key metrics. Specifically, the optimised model demonstrated a training accuracy of 0.99, a training loss of 0.01, a validation accuracy of 0.79, and a validation loss of 0.92, highlighting its improved performance compared to the baseline configuration. The optimal DenseNet has been integrated into a mobile application called FridgeSnap, designed to recognise food items and suggest possible recipes to users, thus contributing to the broader mission of minimising food waste.

Keywords

• food waste management
• Image processing
• deep learning
• recipe suggestion
• image recognition
• food waste classification