# A Real-Time Nut-Type Classifier Application Using Transfer Learning
## 1 Introduction  
Artificial intelligence (AI) has helped to construct smart environments and changed daily habits through recent widespread usage. In [1], it is stated that we are at the beginning of a technological revolution based on the paradigms of Industry 4.0 and Society 5.0. Thus, it is important to exploit the recent innovations in digital technologies led by AI. Civilization should opt into this progress by utilizing smart solutions in different aspects of daily life as early as possible to reap their benefits in the long run [2]. Recently, the current potential of AI has been noticed by many countries and industrial companies. As a result of this awareness, products are being created in many different fields, including health, economy, agriculture, defense industry, and sports, using a set of tools consisting of AI, machine learning (ML), and image processing [3]. These studies are conducted to facilitate human life, increase its quality, and accelerate business processes. There are also AI applications that are utilized in the daily routines of society, where an application can easily be used by an ordinary end-user. Shopping is one of the most important parts of the daily routine, and the shopping process has significantly changed in recent years. The shopping that used to be undertaken in smaller and traditional stores in the past has now been replaced by market chains and shopping centers. This transition has led to a new era of smart shopping. Kiosk hardware, internet of things (IoT) devices, near-field communication (NFC) technology, etc., are contributing to the transition to these new shopping habits. AI is also contributing to this new way of shopping by participating in the development of consumer-oriented smart shopping applications. For example, thanks to the walk out technology used in Amazon's revolutionary Amazon Go markets [4], shopping can be undertaken without queuing and human interaction. In suchapplications, the data of the AI solution are processed by one of various methods, such as image processing, data mining, IoT, NFC, radio frequency identification (RFID), etc. Various image-processing technologies, such as barcode and character recognition, have already been used for packaged products in market chains and shopping centers. As a result, vendors have started to set up smart payment terminals to facilitate the payment process of the customers [5]. It can be said that the recent advances in IoT technologies make it possible to conveniently sell packaged products. On the other hand, unpackaged products, such as nuts, fruits, vegetables, etc., are still widely consumed in low- and middle-income countries due to their lower prices, availability, and affordability [6; 7]. Therefore, there is a need for appropriate applications to digitize the purchase of unpackaged products. However, it is difficult to recognize such products using advanced AI techniques due to environment-specific challenges and the lack of barcode, RFID, and NFC components. There are several attempts in the literature to alleviate the purchase of unpackaged products. Ref. [8] conducts a study of the classification of 15 different fruit and vegetable classes for usage in supermarkets. The authors focus on the ambient lighting conditions of the environment and the color variations in different fruits and vegetables. They propose to use a deep convolutional neural network (DCNN) to reduce the classification errors and find that the experiments with the trained models give a prediction accuracy of up to 99%. Ref. [9] proposes to use deep learning (DL) to classify six different fruits in a supply chain and merchandising context. In their study, the authors propose to utilize fusion-based feature extraction from fruit images and common DL architectures for model training. Consequently, the authors reported that they achieved a success ratio of 97% to recognize different fruits. Ref.