Coffee Beans Moisture Detection with Fusioned Triple Deep Convolutional Neural Network
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Authors: Ivan Arsyaditya Prananda, Mohamad Arsya Kaukabi, and Muhammad Naufal Ariiq
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Affiliation: Bangkit Academy
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E-mail: arsyakaukabi@gmail.com
Fig 1 Triple deep convolutional neural network model for moisture level detection
The dataset for this work is manually collected by our team.
The dataset used for this work is images of green coffee beans with different moisture. There is 5 different classification:
There are total 416 images splitted into train_data, validation_data, and test_data. You can find the dataset here: Google Drive Prepared Dataset
Dataset Size : 411MB
Transfer learning and model fusion is used in this work. There are 3 fusioned model :
- InceptionV3
- VGG16
- DenseNet121
For an immediate simulation without the hassle of going over the previous instructions, refer to this link:
PRE-TRAINED WEIGHTS FILESIZE: (344 MB)
- Open the
TDCNN_1.ipynb
file inCo-ffee_MoistureDetection/Model Trainer/
- Import all the required libraries.
- Build the model with transfer learning of
InceptionV3
,VGG16
, andDenseNet121
. - Fuse the previous three transfer learning model into one model and make sure when all this three is fused, they have the same input layer.
- Download the dataset from the link and load it into
ImageDataGenerator
with.flow_from_directory
- Start the training.
- After all T-DCNN models are built, you may now run the
testing.py
from the mainCo-ffee_MoistureDetection/
folder. - Follow through the given instructions and make sure to use the test sample from the provided
/test/
folder
Fig 2 Accuracy and loss graph after 25 epochs.
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Thanks to Bangkit Academy. Without its support, this work would not have become possible.