## 2 Literature Review  
### Fruits and Vegetables Classifications  
In the proposed prototype, the main objective is to classify and recognize different types of fruits and vegetables, which can be achieved using image classification or object detection models. Prior research has been conducted by various researchers who have utilized different deep learning models to perform classification, recognition, and detection tasks for different types of fruits and vegetables. Let us begin by examining the earlier works in which researchers conducted the classification or identification of diverse types of fruits and vegetables. There are numerous studies that have employed a range of techniques such as Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs. In reference [20], the author conducted a study on classification and recognition of fruits. He utilized a convolutional neural network for the classification of fruits. In his study he used two datasets composed of 3 and 20 classes, respectively. He trained CNN using three different optimization algorithms such as Adam, SGD, and RMSprop. In another paper [21], the author employed a DNN based on CNN for vegetable category recognition. He utilized a dataset composed of 8 classes and 160 images. He performed 3 million iterations for training CNN which seem computationally expensive. In the research in reference [22], caffe and chainer DNN frameworks were utilized for vegetable recognition. The dataset used in this study consisted of 15 classes and 150 images. It transpires that the limited size of the dataset used in studies [21; 22] may lead to concerns about the ability of the model to generalize well to new datasets. In this work [23], the author presented the idea of transfer learning. He utilized various convolutional neural network (CNN) architectures on a dataset composed of 15 classes and 21,000 images for image classification. The typical CNN and four state-of-the-art architectures, including VGG16, MobileNet, InceptionV3, and ResNet, were trained and their performances were compared. Results showed that training a CNN from scratch on a small dataset resulted in lower performance compared to training a pre-trained model. The findings of this study suggest that, when working with small datasets, transfer learning is a technique that can be applied to enhance the performance of the algorithm. In this study [24], the author addresses the challenge of fruit classification in the horticulture industry by leveraging advancements in AI and deep learning. The author employs a dataset consisting of 26,149 images encompassing 40 different types of fruits for experimentation. To enhance the classification performance, the author introduces a customized head comprising five different layers into the MobileNetV2 architecture. By replacing the classification layer of MobileNetV2, a modified version called TL-MobileNetV2 is obtained. Transfer learning is employed to leverage the knowledge gained from pre-trained models. By retaining the pre-trained weights, TL-MobileNetV2 achieves an impressive accuracy of 99%, surpassing the performance of MobileNetV2 by 3%. Moreover, TL-MobileNetV2 exhibits a remarkably low equal error rate of just 1%. Precision, recall, and F1-score measures demonstrate excellent performance, with TL-MobileNetV2 achieving 99% for each metric. In this study [25], the authors propose an autonomous system using a CNN-based approach to classify date fruit, addressing the absence of such a system and the limitations of manual expertise. They create a dataset with eight distinct classes of date fruit for training and employ preprocessing techniques like image augmentation, decayed learning rate, model checkpointing, and hybrid weight adjustment to enhance the model's performance. The proposed model, based on MobileNetV2 architecture, achieves an impressive 99% accuracy rate.