Automated Meat Quality Detection Using DenseNet-121 Transfer Learning

Abstract

Accurate, rapid evaluation of meat quality is essential in modern food processing and retail chains, directly impacting consumer health, regulatory standards, and economic stability. Conventional inspection methods based on manual observation or basic chemical assays—frequently suffer from subjectivity, variability, and inefficiency, motivating the exploration of advanced, automated alternatives. This research introduces a comprehensive deep learning framework employing DenseNet-121, a state-of-the-art convolutional neural network, to classify meat samples by freshness using digital images. By leveraging transfer learning on well-curated datasets and implementing robust preprocessing and augmentation strategies, the system efficiently extracts and interprets relevant color, texture, and morphological features indicative of spoilage and quality. Extensive experiments reveal that the proposed model achieves superior classification performance compared to traditional vision techniques, with substantial improvements in precision, recall, and robustness to input variations. The solution demonstrates high practical value for industrial automation, regulatory monitoring, and consumer-facing applications, paving the way for scalable, non-destructive, and objective meat quality assessment. Challenges related to dataset diversity, domain adaptation, and integration with complementary sensor modalities are acknowledged, highlighting fruitful directions for future investigation.