Jitdumrong PreechasukOrawan ChaowalitFuangfar PensiriPorawat Visutsak2025-03-102025-03-102019ACM International Conference Proceeding Series978-145037263-310.1145/3375959.33759822-s2.0-85090187346https://repository.dusit.ac.th//handle/123456789/4876Mushrooms are fungi. The edible mushrooms include nutritional content and health benefits. However, some mushroom species is toxic and contains poisonous substances that could cause illness and lead to death. Mushroom poisoning accounts for approximately 70% of natural poisoning and often causes death. However, there are only 30-50 poisonous species among the thousands of species found on earth, and of these, no more than 10 are fatally poisonous [1]. The main reason of eating poisonous mushrooms is the lack of knowledge and skill to classify the edible and poisonous mushrooms. Besides, the physical characteristics of mushrooms are similar. Therefore, this work focuses on the classification of 45 types of mushrooms. This work aims to reduce the number of illness persons whom are risk of exposure to toxic mushrooms. This work proposes a new model of classifying 45 types of mushrooms including edible and poisonous mushrooms by using a technique of Convolution Neural Networks. The proposed model was tested on both types of mushroom. It was trained to construct the CNN models and used the trained models to classify all types of mushroom. The proposed model gives the results of 0.78, 0.73 and 0.74 of precision, recall and F1 score, respectively. It concluded that the proposed model can classify types of mushroom image with efficiently and effectively. � 2019 ACM.Analysis of mushroom imageClassification of mushroom typeConvolution Neural NetworkMushroom imagePoisonous mushroomConference paperScopus