Identification of Edible and Non-Edible Mushroom Through Convolution Neural Network
- DOI
- 10.2991/ahis.k.210913.039How to use a DOI?
- Keywords
- Bigdata, CNN, classification, DCNN, Mushroom
- Abstract
Mushroom is one among the most popular consumed food in India. In India people are cultivating mushroom as viable income source for their livelihood. Now-a-days deep learning is being applied to process big data and vision related applications. Recent smart devices can be utilized for automated edibility diagnosis of mushroom using deep convolution neural network (CNN) it has revealed a remarkable performance capability in all its sphere of research activities. DCNN works on static dataset. The models on which it applies will pose as well determine its requirement for training. This paper presents a classification tool for edibility detection of mushroom through deep CNN. Better performance is obtained by tuning the hyper-parameters and through adjustments in pooling combinations in order to obtain real time inference suitably. DCNN has been trained with a data set of segmentation as train and test sets. Performance is analyzed on sNet, Lenet, AlxNet, cNET network architectures. DCNN results are comparatively better in its performance.
- Copyright
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - G Devika AU - Asha Gowda Karegowda PY - 2021 DA - 2021/09/13 TI - Identification of Edible and Non-Edible Mushroom Through Convolution Neural Network BT - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021) PB - Atlantis Press SP - 312 EP - 321 SN - 2589-4900 UR - https://doi.org/10.2991/ahis.k.210913.039 DO - 10.2991/ahis.k.210913.039 ID - Devika2021 ER -