# Automatic Fruits Freshness Classification Using CNN and Transfer Learning
## 2 Related Work  
This section briefly discusses recent methods that aim to classify various fruits using machine learning and image processing-based methods. In [5], a deep learning-based method to classify fruits and vegetables is developed, which is primarily based on the YOLOv4 model. This method initially recognizes the object type in an image and then classifies the object either as fresh or rotten. This model also improves the backbone of the YOLOv4 version using the Mish activation function, which results in rapid detection of objects. In [6], researchers analyze and proposed a novel design of computer vision-based method using deep learning with the Convolutional Neural Network (CNN) model to detect several fruit freshness level. The specially designed CNN model is later evaluated and extensively tested with public datasets of fruits fresh and rotten for classification. This is a nice effort and nicely handles the fruits' freshness level instantly. In [7], published work focuses on classifying rotten and good apples. For the task of apple classification, initially texture features of apples are extracted. For instance, discrete wavelet feature, histogram of oriented gradients, and law's texture energy along with the gray level co-occurrence. Later, various classifiers are applied, for instance the SVM, the k-NN, and Linear Discriminant. Researchers' conclude that the SVM classifier yields 98.9% accuracy, which is better than few of the compared classifiers. In [8], eight deep learning models namely AlexNet, Google Net, ResNet18, ResNet50, ResNet101, VGG16, VGG19, and NasNetMobile are fine-tuned to assess the quality of fruits and vegetables. The performance of deep learning models is based on the training and validation accuracy. The model's outcome shows that the VGG19 model reached the highest validation accuracy over the original samples and the ResNet18 model achieved the highest validation accuracy based on the augmented data samples. In [9], the authors investigate the maturity status of Papaya fruit by using machine learning. To classify the fruits, the LBP, the HOG, Gray Level co-occurrence Matrix (GLCM), SVM, K-Nearest neighbor (KNN), and Naive Bayes methods are applied and compared. Seven pre-trained models are fine-tuned on the given dataset of Papaya to evaluate the performance of the robust system. The K-Nearest neighbors (KNN) with the HOG features results high accuracy with much less training rate. In [9], authors apply deep learning model on the banana different dataset. In this work, bananas' freshness was analyzed by transfer learning and established the relationship between freshness and storage dates. Banana feature extraction were extracted by Google Net. The reported classification accuracy of this model is 98.92%, which is at par with normal human detection. In [10], authors use k-means clustering along with colors, textures, and shape features to classify the apple freshness by investigating its disease. This work also uses multiclass SVM during classification stage. In [11], a system for classifying fruits and vegetables in supermarkets is implemented, which combines backdrop removal with a split-and-merge strategy to find fruits and vegetables in pictures. This model also employs color, shape, and texture as key identifying characteristics. The feature space was condensed using the PCA. Several kernel functions, such as the MWM-SVM, the MWV-SVM, and the DAG-SVM were used during the algorithm development. The maximum accuracy of 88.2%, was attained by MWV-SVMs utilizing Gaussian radial basis kernels. In [12], authors published a method for identifying fruit flaws in retail. Cameras are positioned on the borders of a conveyor to capture orange data samples. They applied color as a feature in the RGB images and also produced color histograms. The Fisher-LDA is employed to decrease features size and to reduce noise. Next, the orange problematic zones are found using the SVM. The trial results showed that their proposed technique had a 96.7% recall rate.