Machine Vision Recognition System of Edible and Poisonous Mushrooms Using a Small Training Set-Based Deep Transfer Learning

被引：3

作者：

Sevilla, William H. ^{[1
]}

Hernandez, Rowell M. ^{[2
]}

Ligayo, Michael Angelo D. ^{[3
]}

Costa, Michael T. ^{[4
]}

Quismundo, Allan Q. ^{[5
]}

机构：

[1] Batangas State Univ, Comp Engn Program, Batangas City, Philippines

[2] Batangas State Univ, Coll Informat & Comp Sci, Batangas City, Philippines

[3] Quezon City Univ, Dept Elect Engn, Quezon City, Philippines

[4] Cavite State Univ, Dept Comp & Elect Engn, Cavite, Philippines

[5] Eulogio Amang Rodriguez Inst Sci & Technol, Coll Engn, Manila, Philippines

来源：

2022 INTERNATIONAL CONFERENCE ON DECISION AID SCIENCES AND APPLICATIONS (DASA) | 2022年

关键词：

poisonous mushroom; edible mushrooms; deep learning; transfer learning; yolov3;

D O I：

10.1109/DASA54658.2022.9765046

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A way of classifying if a mushroom is edible or not is presented in this study. As mushrooms are slowly becoming popular, classifying these mushrooms would be crucial as some of the toxic mushrooms that could be found in the mushrooms can kill a person or give them a bad case of stomachache and other effects. Using the YOLOv3 model a model is created that can classify these mushrooms. The model that was chosen has gotten an mAP score of 96.68% and can detect most of the inputs that are used to test the model. The model is also able to achieve a 90% accuracy as it was able to correctly detect 18 photos out of 20 when tested. This model could be used to ensure that there would not be any toxic mushrooms in houses or parks that a child could just pick up and swallow without being noticed by any adults.

引用

页码：1701 / 1705

页数：5

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