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