###### Abstract
Subjective methods for assessing meat quality typically rely on sensory evaluation, which includes visual and eating experiences. Subjective assessment methods have the disadvantage of being heavily reliant on evaluators' specific experience, having poor repeatability, and being difficult to quantify [12]. In contrast, objective evaluation methods are laboratory tests that assess the physical and chemical properties of meat as well as the microorganisms present [13]. Although this method yields accurate results, the meat is ruined or damaged, and the detection process is time-consuming. As a result, modern meat processing firms are struggling to meet the demand for automated processing. As a result, the significance of systematic meat quality assessment cannot be overstated. Traditional evaluation methods were based on sensory estimation and a chemical technique that included microbial population [14]. However, when it comes to evaluating meat quality characteristics, these methods are time-consuming, destructive, inconsistent, and unstable. The lack of quick, trustworthy, and non-destructive systems for assessing meat features has hampered the quality control section of the meat industry [11]. More efficient methods are thus required in mechanized procedures to evaluate the properties, control the process, and categorize the products. Such methods could assist the slaughter industry in obtaining information about meat quality immediately after comfort sorting and prior to further use [5]. Furthermore, these methods would assist the meat industry in determining and selecting appropriate raw material processes. An imaging method has recently been used for meat quality estimation and fast recognition of quality problems on the processing line. For effective prediction of meat quality, artificial intelligence technology is employed. It also provides indispensable clinical assistance for online meat grading and evaluation. In food science, AI technology that combines sensors, processors (computers), and other components allow for non-destructive evaluation of products. Moreover the original shape and nature of the sample is maintained. Recent studies suggest that artificial intelligence has opened a new door in image recognition and classification tasks. Detection of glaucoma classification, skin cancer, brain tumors, COVID-19 [15], Alzheimer disease [16], etc., are the notable application areas. Artificial neural networks (ANNs), convolutional neural networks (CNNs), fully connected networks, vision transformers, etc., are widely adopted models. Among the various deep models, CNNs are the most widely used. This work sets out to examine the effectiveness of shallow CNNs in detecting the quality and freshness of meat. This work makes the following notable contributions:  
1. Evaluating the quality of meat using a suggested ConvNet-18 model. 2. Investigating the freshness of meat using the proposed ConvNet-24 model. 3. An ensemble framework based on the averaging ensemble technique is utilized for binary and multi-class classification tasks. The rest of the paper is structured as follows: A brief review of meat quality detection using CNNs is discussed in Section 2. In Section 3, the methodology is explained. Findings and discussions are summarized in Section 4, accompanied by conclusions in Section 5.