## 1 Introduction
The industry has a responsibility to determine a fair price by recognizing, identifying, and classifying the defects, errors, damage, and diseases of the fruit that enters the quality selection process. Likewise, the industry can analyze and judge the criteria that it has been using to classify this fruit and thus recognize the different biases between the industry and the harvest by the farmer. In short, when technology allows and detection algorithms are fully validated, fair, objective and unbiased quality ratings can be achieved. This is a challenge that begins with the use of this database. This database aims to solve one of the major problems in the industry. It will evaluate the largest amount of fruit as possible, and not a single sample as representing an entire batch. It is important to highlight that the RGB images in the database were acquired in an industrial context, just as is done, without process variations, see Figure 2a. The detection process in the machine takes between 2 and 5 min, see Figure 2b. This is an acceptable detection time for the industry. Studies and investigations on the detection of the quality of the fruit are extensive and varied, specifically as regards raspberry fruit; to the best of our knowledge, this is the first RGB image database for the industrial applications of red raspberries' automatic quality estimation. Table 1 shows some freely accessible database examples from different countries with the corresponding access to their repositories. Observe that the study objective focuses mainly on identifying and classifying diseases or defects to detect the quality of the fruit. This is because there is an interest in controlling the quality of the fruit since it can be  
Figure 2: Raspberries and equipment. (**a**) RGB image of raspberries in a tray to be evaluated. Note that it is a sample unit of the fruit, which is uniformly distributed throughout the tray, avoiding overlap between the fruit as much as possible. (**b**) Patented device for the acquisition of the images. Observe that the tray is placed at the bottom of the device, at the top is the lighting system, and an external computer to store the images. significantly affected by damage or stains due to pathological injury, pests, or diseases, which ultimately degrade its quality and cause financial losses to the farmers. It is well-known that data plays a critical role in machine learning approaches. The characteristics of these datasets must be clear and precise, such as their annotations or labels, because they directly influence the behavior of a model, which needs a training and testing stage to detect what has been learned, thus allowing applying the model to new data never seen before. Image quantity and resolution play a fundamental role during this process. In the examples in Table 1, the images are of high quality, with a very high resolution, which allows for the additional details that are stored in the pixels. These details are crucial in the analysis of the quality of the fruit, since, for example, a change in color, shade, or contrast, can help identify a disease or a defect; or to apply image techniques such as segmentation [3] or augmentation [4; 5]. Please note that the images are generally captured in fieldwork, which is why it is often difficult to transport the necessary equipment to capture the appropriate images. In general, high-resolution cameras or cell phones are used. Furthermore, synthetic images can be generated from some samples. For example, in [6], a near-infrared (NIR) image generation method was used to yield a set of images re-processed from two public datasets with realistic results. In this work, we present a free new database designed from a real industrial process with the aim of recognizing, identifying, and classifying the red raspberry's quality from their RGB images in a tray, with recently harvested fresh fruit that enters the industry's selection and quality control process, as Figure 1 shows.