Revolutionizing Mushroom Identification: Improving efficiency with ultrasound-assisted frozen sample analysis and deep learning techniques

Abstract

The aim of this research is to explore the potential applications of deep learning algorithms in categorizing ultrasound-assisted frozen mushrooms based on their quality attributes. To achieve this, an object detection model was trained on a dataset of images grouped by control (mushrooms frozen by immersion method without ultrasonication), pulse (mushrooms frozen by immersion method in combination with pulsed mode ultrasonication), continues (mushrooms frozen by immersion method in combination with continuous mode ultrasonication), and raw data (mushrooms before freezing. The accuracy of the developed model was 100 % for continues and control groups, while the model achieved accuracies of 92 % and 95 % for raw and pulse ones, respectively. Specifically, the model achieved a total accuracy rate of 96 % for identifying mushrooms. The model's excellent performance in accurately classifying images shows its ability to recognize subtle differences in image quality and clarity. The findings could provide valuable insights into using deep learning algorithms for analyzing mushroom quality data and developing intelligent mushroom processing systems.