Banana Ripeness Classification Based on Deep Learning using Convolutional Neural Network
| Main Authors: | Saragih, Raymond Erz, Emanuel, Andi Wahju Rahardjo |
|---|---|
| Format: | BookSection PeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
Institut Sains dan Teknologi Terpadu Surabaya (ISTTS)
, 2021
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| Subjects: | |
| Online Access: |
http://e-journal.uajy.ac.id/26440/1/2.%20Banana%20Ripeness%20Classification%20Based%20on%20Deep%20Learning%20using%20Convolutional%20Neural%20Network.pdf http://e-journal.uajy.ac.id/26440/ |
Daftar Isi:
- ruit ripeness is an important thing in agriculture because it determines the fruit's quality. Determining the ripeness of the fruit that was done manually poses several weaknesses, such as takes a relatively long time, requires a lot of labor, and can cause inconsistencies. The agricultural sector is one of the important sectors of the economy in Indonesia. However, sometimes the process of determining fruit ripeness is still done by using the manual method. The development of computer vision and machine learning technologies can be used to classify fruit ripeness automatically. This study applies the Convolutional Neural Network to classify the ripeness of the banana. The banana's ripeness is divided into four classes: unripe/green, yellowish-green, mid-ripen, and overripe. Two pre-trained models are used, which are MobileNet V2 and NASNetMobile. The experiment was conducted using Google Colab and several libraries such as OpenCV, Tensorflow, and scikit-learn. The result shows that MobileNet V2 achieves higher accuracy and faster execution time than the NASNetMobile. The highest accuracy achieved is 96.18