Identification Food Nutrition and Weight Prediction using Image Processing
DOI:
https://doi.org/10.30871/jaee.v9i1.9492Keywords:
Detection, Intensity, Obesity, YOLOAbstract
Obesity is a global issue with rising prevalence each year, driven partly by excess nutrient and calorie intake. Identifying nutrient content in food is vital to prevent obesity. This research employs image processing, specifically the YOLO (You Only Look Once) algorithm, to classify and identify fruits and vegetables quickly and accurately. YOLO is advantageous for its speed and ability to classify multiple objects simultaneously. The goal is to develop a system that recognizes, classifies, and predicts the weight of fruits and vegetables, providing nutritional and calorie information. Tests showed that the system accurately detects produce under various lighting conditions—achieving 100% accuracy with additional ring light (600–650 lux) and 99.2% without extra lighting. Beyond object detection, the system predicts weight with an average error of 5.6% when illuminated. This technology has the potential to aid users in monitoring nutritional intake by providing reliable identification and calorie data, contributing to obesity prevention efforts.
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