ENHANCED CLASSIFICATION OF COFFEE BEAN SPECIES THROUGH COMPUTATIONAL MODELING AND IMAGE ANALYSIS

Maintaining quality standards and streamlining the value chain in the coffee business depend heavily on the accurate classification of coffee beans. Previously, this procedure was carried out by hand, which frequently resulted in errors that affected the beans overall quality and market value. In recent times however, there have been chances to improve and automate coffee bean sorting accuracy owing to developments in deep learning, especially in the area of image classification. Fraud detection is very important to ensure that the consumers receive quality products. This work investigates the use of deep learning models to categorize coffee beans using image data. By using machine learning and computer vision technologies, we are able to analyze the data given to identify the irregularities found which may result in fraud or issues in the quality of the coffee beans thereby proving that using image classification algorithms can majorly reduce the errors formed with respect to manual sorting, which ultimately leads to improved control of quality and economic outcomes.