- The paper details a novel machine learning application that classifies coffee roast levels using CNN techniques to reduce subjective human error.
- It employs a MobileNet-based model with image augmentation and pre-processing, achieving approximately 82% accuracy across four roast levels.
- The study offers practical value for consistent coffee quality control and demonstrates the broader applicability of AI in visual assessments.
Overview of "Coffee Roast Intelligence"
The paper "Coffee Roast Intelligence" introduces a novel application utilizing machine learning to classify the degree of roasting in coffee beans. The paper addresses the challenge of maintaining consistent coffee quality by automating the roast level classification process, typically subject to human error due to environmental factors like lighting and fatigue. This application, developed as an Android platform, leverages image processing combined with Convolutional Neural Networks (CNN) to predict roast levels based on visual data, thereby providing an objective alternative to subjective human assessment.
Methodology and Implementation
The core methodology involves using a CNN model, specifically a variation of the MobileNet model, to analyze images of coffee beans. This model was selected for its efficiency and reduced computational requirements, which are ideal for mobile applications. The application processes images by applying Gaussian blur for noise reduction, converts the images to the HSV color space, and uses Boolean masks to focus on relevant portions of the image. Subsequently, image data are augmented through transformations such as rotation and zoom to improve model robustness.
A comprehensive dataset of 4,800 images, categorized into four roast levels, was used to train the model. The dataset expansion through augmentation ensures a wide representation of possible input conditions. The machine learning model training was executed using a Python environment with Keras and TensorFlow libraries, employing techniques such as k-fold cross-validation to enhance model reliability.
Results and Performance
The experimental evaluation demonstrated an accuracy of approximately 82% across different roast classes. The model differentiates among green (unroasted), light, medium, and dark roasted beans, with precision ranging between 0.7 and 0.9 for individual categories. The results are depicted using confusion matrices and performance metrics such as accuracy and recall, confirming the model's potential utility in real-world scenarios.
Practical and Theoretical Implications
Practically, the "Coffee Roast Intelligence" application offers significant potential benefits for coffee industry professionals, including baristas and coffee shop owners, by facilitating consistent quality control in coffee roasting processes. Theoretically, this work underscores the applicability of CNNs beyond conventional domains, demonstrating their utility in settings requiring visual classification in constrained environments such as mobile platforms.
Future Directions
The research identified limitations, particularly regarding the dataset's uniformity concerning coffee bean origin. Future work could improve prediction accuracy by including a more diverse dataset accounting for varietal differences. Moreover, enhancements might involve improved noise handling and the integration of additional sensory data beyond visual inputs, potentially incorporating aroma or temperature sensors to refine roast level predictions further.
In summary, the "Coffee Roast Intelligence" application represents a significant step towards automating quality assurance in the coffee industry using AI, presenting a scalable model that combines image processing with advanced machine learning techniques. This approach not only improves consistency but also offers a blueprint for similar applications in other domains where subjective quality assessment prevails.