FOOD RECOGNITION AND NUTRITION FACTS DETERMINATION WITH DEEP CONVOLUTION NEURAL NETWORK MODELS
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
North Univ Center Baia Mare
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Food recognition plays a crucial role in various domains including healthcare, nutrition, and the food industry. In healthcare, food recognition is valuable for individuals to monitor their daily food intake and manage their diet effectively. It also aids dietitians and nutritionists in creating personalized meal plans for patients based on their nutritional requirements and preferences. The objective of this research was to develop software capable of recognizing and predicting the nutritional information of commonly consumed fruits and vegetables in Turkey. Basic nutrition data for each food item was collected and organized. A dataset comprising 9,000 food images was gathered, encompassing 200 images for each food item. To train the images, deep learning (DL) algorithms such as GoogleNet, ResNet-50, and Inception-v3 were utilized on platforms like Matlab and.NET Core. Additionally, 900 food images were reserved for external validation purposes. The DL algorithms achieved excellent accuracy, with all models surpassing 98.3% accuracy in predicting food categories. Notably, the Inception-v3 algorithm outperformed the others, achieving an accuracy of 99.1% during the testing phase. Consequently, the Inception-v3 algorithm was chosen to develop software for food recognition and nutrition analysis, intended for both computers and smartphones. The software can be relied upon for food recognition and nutrition analysis, making it highly valuable in healthcare, particularly in tracking the dietary intake of patients with chronic conditions like diabetes, heart disease, or obesity. The system can effectively track the types and quantities of foods consumed, providing personalized feedback to both patients and healthcare providers.
Açıklama
Anahtar Kelimeler
Vision System Approach, Color Measurements
Kaynak
Carpathian Journal of Food Science and Technology
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
