Food State Recognition Using Deep Learning

Abstract

Automated food detection and recognition methods have been studied to enhance an end-user life. However, most of the existing research focused on food ingredient type recognition with little work has been done for food ingredient state recognition. Successful recognition of food ingredient state plays a significant role in handling the food ingredient by an intelligent system. In this work, we propose a new novel cascaded multihead approach based on deep learning for simultaneously recognizing the state and type of food ingredients. We trained and evaluated the proposed approach on a benchmark dataset of food ingredients images with 9 different food states and 18 food types. We compared the proposed approach with a non-cascaded deep learning approach. The cascaded deep learning approach shows improvement in food ingredient state recognition with an accuracy of 87% compared to 81% using a non-cascaded deep learning method. Our proposed method broadly applies to various tasks where food ingredient state recognition is important, such as feeding elderly and disabled person, and for automation of food recognition and preparation.

Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation

“…On a benchmark dataset of food ingredient photos with nine varying food states and eighteen different food types, they trained and assessed the suggested methodology alongside contrasting the suggested method with a deep learning method that was not cascaded. The cascaded methodology was able to perform food ingredient state recognition with an accuracy of 87%, compared to 81% when using the non-cascaded DL approach [61].…”

Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification

“…In the literature, an automated image-based nutritional assessment technique was proposed in which the technique had the following key stages: food image identification, recognition of food products, weight or quantity valuation, and lastly, nutritional and caloric value assessment [10]. In recent years, developments in Machine Learning (ML), image processing, and specifically Convolutional Neural Networks (CNN), and Deep-Learning (DL) techniques have heavily benefited the image classification and detection processes, comprising the issue of food image identification [11]. Researchers have developed diverse phases of food detection systems, despite which it remains challenging to find a satisfactory and efficient solution for food identification and classification with high accuracy.…”