## 1 Introduction  
Current supermarket self-checkouts depend upon barcode scanning or selection from a Look Up Table (LUT) for billing. Packaged products at supermarkets can easily support barcodes, however fruit and vegetables, i.e., fresh produce items, must currently be selected from a LUT either by the assisted checkout personnel or by the customer at a self-checkout. This selection from a LUT involves significant human factors and requires good knowledge of different fruit and vegetable varieties. Fruit and vegetables are among the most sold produce items and have a significant contribution in the revenue of supermarkets and, hence, the economy of a country. For example, Australian supermarkets are a AUD 101 billion industry according to the IBISWorld Senior Industry Analyst [1; 2]. This industry is also an employer of approximately 360,000 personnel across the nation. Given the size of the industry, intentional or unintentional incorrect scanning of fruit and vegetables can cause significant losses that can aggregate across the sector. Hence, the introduction of an image-based technique, as proposed in this paper, that eliminate the requirement for a LUT, can significantly improve revenues. The proposed technique also has significant environmental benefits by reducing the use of light-weight plastic packaging and shrink warps, which are currently used to locate barcodes. This plastic waste is an exponentially growing problem all over the world. For instance, approximately 3.5 million tonnes of plastic waste is produced in Australia annually and 0.6 million tonnes was produced via packaging in 2016-2017 [3]. Most of this plastic is not recycled, and as well as going into landfills, a significant percentage of this waste makes its way to sea. Recently, it has been estimated that there will be approximately 12 million kg of plastic waste in international oceans by 2050 [4]. The Environmental Protection Authority (EPA) of Australia recently reported that approximately 75% of low weight plastic is produced by plastic bags and packaging in supermarkets. Considering these factors, there is a strong justification to support the concept of a barcode-less supermarket self-checkout. Fruit and vegetable classification is a complex problem and involves significant challenges. At a higher level of abstraction these challenges can be categorised as: (a) Classification of different fruit and vegetables and (b) classification of different varieties of a fruit or vegetable. The challenges for vision-based classification result from the highly variant physical features of fruit and vegetables i.e., level of ripeness, texture, colour, and shape. However, classification of fruit and vegetables at supermarket self-checkouts presents additional challenges such as variable ambient lighting conditions, human elements in the scanning process, and scanning of multiple fruit and vegetables at the same time. Much research has been published to discuss the design and implementation of automated supermarket self-checkouts [5; 6; 7; 8]. However, a complete discussion on the classification of multiple fruit and vegetables in a supermarket environment is required to analyse the effectiveness of the concept. Moreover, the existing techniques have analysed the classification of fruit and vegetables by using vision-based information only. However, the weight of a fruit or vegetable is also available with the help of a built-in weight sensor at the supermarket checkout counter. This weight information has not previously been considered for classification purposes. Therefore, we propose a novel approach to incorporate the weight information of a fruit or vegetable for classification. A comparison of recent state-of-the-art features and Machine Learning (ML) techniques for fruit and vegetables, can be seen in Table 1 for our proposed approach. An implication can be observed in that much of the existing state-of-the-art work has been performed for small numbers of fruit and vegetable classes with small data sets, which can cause overfitting.