## 1 Introduction  
The most recent advances in image processing include image processing, image processing  
## 1 Introduction  
Fish is one of the most important food sources for human health. It has high levels of protein and nutrients such as omega-3 fatty acids and vitamins, which are very important for human health [1, 2]. However, fish is prone to spoilage due to its structural characteristics [3]. During the spoilage process, the color of the fish's eyeballs changes, deterioration is seen on the body surface and abdomen, muscle tone weakens, and, as a result, the nutritional value deteriorates and quality decreases [4]. The consumption of stale or spoiled fish can result not only in the loss of nutritional value and flavor but also in serious food poisoning. There are some commonly known methods to assess the freshness of fish. Among these are methods such as examining the brightness of the skin, examining the clarity of the eyes, and checking the meat by hand. However, these methods are not very successful due to factors such as variations in visual inspection, experience level differences, and ambient light. Although new technological solutions (such as biosensors, nano-test kits) are available, fishermen and consumers do not use these services due to their limited capacity and cost [5]. The fact that fish is both economically viable and has high nutritional value has led researchers to focus on the determination of fish freshness. When the existing studies were examined, it was seen that they emphasized freshness detection and the analysis of critical parts of the fish, such as the eyes, gills, and skin surface [5, 6, 7, 8, 9]. In these studies, it was observed that convolutional neural network (CNN) techniques were mostly utilized over visual data. In addition, there are also studies in the literature on freshness detection using whole fish images [10]. There are also studies that detect freshness according to information from different sensors, such as electronic noses [11, 12, 13]. Many studies have confirmed that combining multiple features, instead of using a single feature, increases the validity in classification studies [14, 15]. In this study, a fusion model combining 940 nm single-wavelength laser reflectance data and image features is proposed to classify fish freshness. The general structure of the study is shown in Figure 1. In the study, 130 mackerel fish were kept under controlled conditions, and 3-day images and laser data were collected for both surfaces of the fish. Deep learning models such as ResNet, MobileNet, EfficientNet, and ConvNeXt were used for feature extraction from the preprocessed images. To address the limitations of the dataset size, a transfer learning approach was adopted, in which pretrained weights from ImageNet were utilized. This allows models to effectively extract discriminative features even from relatively small datasets. A fusion approach combining image and laser data was developed to predict the freshness of the fish. These data were analyzed using support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) algorithms, and the results were comparatively evaluated using five-fold cross-validation. In summary, the main contributions of this work include the following:  
* The effects of laser data on fish freshness classification are demonstrated through detailed analyses. In addition, experimental studies demonstrate that the fusion of image and laser data performs successfully compared to single approaches. * A comprehensive dataset including 3-day mackerel images and laser data collected systematically in 24 h periods under controlled conditions has been created for the use of researchers working in related fields. * Feature extraction from fish images was performed using four deep learning architectures with different complexity levels, such as MobileNet, ResNet, EfficientNet, and ConvNeXt.