Design and implementation of fish freshness detection algorithm using deep learning

Abstract

Organoleptic assessment of fresh fish includes specifications for the quality of the eyes, gills, mucus, odor, texture and flesh (color and appearance). However, not everyone has knowledge about it. This research uses the tiny yolov2 to facilitate the determination of fish freshness levels (good quality, medium quality, poor quality) correctly and fast. There are a few stages in this research, included organoleptic test accompanied by taking fish eye image dataset every hour, processing organoleptic test data labeling, training, and validation. There are three types of fish used, consists of Rastrelliger, Euthynnus affinis, and Chanos chanos. Detection of fish freshness level for three species was successfully carried out with the result of average precision is 72.9%, average recall is 57.5%, and accuracy is 57.5%. The factors that affect the prediction results in this study is the collection of datasets before the training process is carried out consisting of fish samples obtained from traditional markets, which are considered inadequate so that it affects the organoleptic test process itself, the organoleptic test that was carried out as a reference for image sorting was considered inaccurate because it used less than 30 untrained panelists and dataset imbalance.