Ichsan Hasanudin, Kahlil Muchtar, Muhammad Subianto
Manual coffee bean sorting in Indonesia is labor-intensive, subjective, and limits access to premium international markets. Edge Artificial Intelligence (AI) offers a transformative solution for real-time quality assessment at the source. This work develops an Edge AI system for binary classification (normal vs. defective) of Arabica coffee beans using a novel multi-dataset approach. Unlike prior works focusing primarily on cloud-based accuracy metrics, this research uniquely integrates: (1) rigorous comparative analysis of EfficientNetV2-Small and FocalNet-Tiny deployed on NVIDIA Jetson Orin Nano under FP32, FP16, and INT8 quantization, (2) comprehensive sustainability assessment using Green Software Foundation’s Software Carbon Intensity (SCI) specification (2024) providing transparent carbon accounting measured with CodeCarbon library, and (3) multi-dataset integration combining two independently captured repositories totaling 5,958 harmonized images. EfficientNetV2-Small FP16 achieves optimal performance with 98.83% accuracy, 24.73 images/sec throughput, 40.43 ms latency, and lowest SCI of 0.103 gCO2e per 1,000 images. This work establishes new benchmarks for sustainable, deployment-ready edge-AI in agricultural quality control, positioning Indonesian coffee for “Green-AI sorted” premium market differentiation. ©2025 IEEE.
Department of Informatics, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia
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