Development of IoT Automated Color-based Sorting Machine for Robusta Coffee Cherries (Coffea canephora)

Articles in Press

Document Type : Research Article-en

Authors

• M. K. Alano
• V. Ogaya
• E. Arboleda

Department of Computer, Electronics, and Electrical Engineering, Cavite State University, Cavite, Philippines

https://doi.org/10.22067/jam.2025.93067.1374

Abstract

This research focused on creating an IoT-enabled color-sorting machine for Robusta coffee cherries, utilizing image processing as an effective alternative to manual sorting. The system tackles a significant issue with the strip-picking harvesting method, which gathers cherries at different ripeness levels, negatively affecting coffee quality. The machine sorts cherries by ripeness—red for ripe, green for unripe, and black for overripe—using a detection model trained through image processing and implemented on a Raspberry Pi 4 Model B. The performance was assessed based on sorting speed and classification accuracy. The detection model successfully identified 277 out of 300 cherries, resulting in an overall classification accuracy of 92.33% and a mean precision of 92.55%. In practical tests with 100 cherries over 10 trials, the machine achieved an average sorting accuracy of 86.83% and a mean sorting time of 21 minutes and 33 seconds. When compared to a previously developed coffee bean sorter, the new device showed improved accuracy and faster processing speed.

Keywords

• Convolutional Neural Network
• Image processing
• Strip picking

Main Subjects

• Agricultural systems engineering (greenhouse, fish farming, mushroom production)

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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