This repository contains the code for a machine learning model that performs implied computer vision tasks using a Convolutional Neural Network (CNN). The machine learning model was developed to classify fruits into rotten or fresh categories using computer vision and a convolutional neural network (CNN). The model was built using the TensorFlow Keras library. In this case, the CNN model was trained on a dataset of images of fruits, such as: apples, avocados, bananas, grapes, guavas, oranges. The dataset included images of both rotten and fresh fruits. The CNN was able to learn to distinguish between the two types of fruit based on the features it extracted from the images.The output from our model is prediction result "Fresh" or "Rotten" and the accuracy.

The model is trained on a custom dataset consisting of labeled images. The dataset can be obtained from https://www.kaggle.com/datasets/sriramr/fruits-fresh-and-rotten-for-classification and https://www.kaggle.com/datasets/moltean/fruits.

The CNN model architecture used for this project is as follows:

pre_trained_model = VGG16(input_shape=(150,150,3), include_top=False) for layer in pre_trained_model.layers: layer.trainable = False x = layers.Dense(1024, activation='relu')(x) x = layers.Dropout(0.2)(x) x = layers.Dense(1, activation='sigmoid')(x)

To run the code in this repository, you will need the following dependencies:

Found 4004 images belonging to 2 classes. 4004/4004 [==============================] - 41s 10ms/step - loss: 0.1405 - accuracy: 0.9451 accuracy test: 0.9450549483299255 loss test: 0.14052222669124603

And for the result test you can check this https://github.com/Bugiene/Bugiene-app/tree/master/machine-learning/result-test

We would like to acknowledge the following contributors for their valuable contributions to this project:

For any questions or inquiries, please contact the contributor mentioned above. Thank you.