Research of Automated Classification System of Pleurotus eryngii Based on Deep Learning

• Main Authors: HUANG, WEN-HSIN, 黃文信
• Other Authors: JOU, RONG-YUAN
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/ywbpy6

碩士 === 國立虎尾科技大學 === 機械設計工程系碩士班 === 107 === In recent years, mushroom production has become one of the important industries in Taiwan, but most of the operations still highly rely on manpower. Taking the classification of Pleurotus eryngii for an example, it is mostly classified by judging its appearance. This study developed an automated classification system of Pleurotus eryngii based on machine vision and convolutional neural network (CNN). In this study, we designed the system based on raspberry pi, which used to collect Pleurotus eryngii image samples. Some of the samples were used for training and validation of CNN. Other samples were used to test the generalization ability of neural network model. The automated classification system of Pleurotus eryngii developed in this study has an accuracy of 93.18% in the final test. The system also has outstanding performance in multiple model evaluation indicators. Obviously, the system has a perfect generalization ability on the neural network model, and it can effectively and accurately classify Pleurotus eryngii.