☕ Coffee Brewing Level Prediction Based on Spectroscopy Data Using Deep Learning

This repository contains the complete pipeline for a thesis project that predicts coffee brewing levels using spectroscopy data and deep learning models, later deployed on a Raspberry Pi with a real-time interface using OLED display and a physical button.

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📚 Description

The goal of this project is to classify brewed coffee into categories such as Underdeveloped, Strong, Weak, Bitter, or Ideal based on spectral data obtained using the AS7265x spectroscopy sensor. A deep learning model is trained using Python and TensorFlow, then converted into TensorFlow Lite and deployed on a physical prototype for real-time prediction.

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🧪 Data Collection

• Sensor: AS7265x Triad Spectral Sensor
• Platform: Raspberry Pi
• Categories: Coffee brewing levels
• Format: Raw 18-channel spectral data saved in .csv format

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🧠 Modeling

Main Libraries for Modeling:

• pandas: data manipulation and preprocessing
• seaborn: data visualization and EDA
• tensorflow: deep learning model training and conversion to .tflite
• numpy: numerical operations

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📟 Prototype Deployment

A physical prototype is built using:

• Raspberry Pi
• AS7265x Spectroscopy Sensor
• SSD1306 OLED Display
• Push Button
• TensorFlow Lite Interpreter