Coffee-Brewing-Level-Prediction/README.md

☕ 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, Ideal, or Bitter, 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