Detecting Avocado Freshness In Real-Time: A Yolo-Based Deep Learning Approach
DOI:
https://doi.org/10.52436/1.jutif.2025.6.3.4626Keywords:
Avo Freshify, Butter Avocado, Detection, Freshness, YOLOv8Abstract
The increasing consumption of avocados in Indonesia highlights the need for an effective method to ensure fruit freshness. The main problem lies in the absence of an objective and standardized system for assessing avocado freshness, which may lead to consumer dissatisfaction and food waste. This study aims to address the challenge of identifying avocado freshness to ensure suitability for consumption. Conducted from May 23 to June 5, 2024, the research used butter avocado samples sourced from supermarkets. The method employed is the You Only Look Once version 8 (YOLOv8) deep learning algorithm, known for its real-time object detection capabilities. YOLOv8 offers enhanced performance compared to earlier versions through anchor-free detection, improved speed, and accuracy, making it suitable for fast and reliable freshness detection tasks. Avocados were classified based on estimated spoilage time under room and refrigerator temperatures, ranging from "up to 5 days at room temperature and 14 days in refrigeration" to "not fit for consumption." The model was validated using 120 images categorized into six freshness levels. Evaluation results demonstrated high performance, with 98% accuracy, an F1-Score of 0.978, mAP50 of 0.994, and mAP50-95 of 0.972 after 50 training epochs, confirming the model’s robustness. Real-time tests yielded confidence levels of 96% and 94%, further validating its effectiveness in detecting avocado freshness. To facilitate daily use, a mobile application named Avo Freshify was developed. The app accurately identifies the freshness of avocados and provides valuable information for consumers and sellers. This research contributes to the advancement of artificial intelligence and object detection in food quality control and agricultural technology.
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