Decoding AI: Experimental Analysis of Artificial Intelligence based Wine Quality Prediction Logic using Convoluted Deep Classification Strategy

Wine quality prediction is essential for optimizing production and ensuring consumer satisfaction. This study introduces a novel Convoluted Deep Classification (CDC) approach for accurate wine quality assessment. Leveraging convolutional neural networks (CNNs), our method effectively extracts hierarchical features from diverse wine data. The process begins with dataset preprocessing, encompassing chemical properties and sensory attributes. Subsequently, a hierarchical CNN architecture is tailored to capture intricate features such as acidity levels and volatile compounds. Techniques like data augmentation and transfer learning are employed to bolster model generalization. Evaluation on a benchmark dataset demonstrates outstanding performance, achieving a remarkable accuracy of 97%. This surpasses traditional methods and baseline CNN architectures, affirming the efficacy of our approach. Additionally, interpretability analyses unveil the key features influencing wine quality predictions, providing invaluable insights for producers and connoisseurs alike. Our CDC framework offers a robust and interpretable solution for wine quality prediction, empowering stakeholders with actionable insights for production optimization and quality assurance in the wine industry.