Deployment-Optimized Deep Learning Model for Coconut Shell Quality Identification in Charcoal Production - Andi Anzanul Zikra; Rahmat Yanuary; Komala Sari (Ikatan Ahli Informatika Indonesia (IAII), 2026)
Coconut shells are a vital raw material for charcoal briquette production, and ensuring their quality is crucial for producing high-quality charcoal products. This study develops an optimized deep learning model for detecting, classifying, and evaluating coconut shell quality using Mask R-CNN, with a specific focus on bridging the gap between theoretical model development and practical industrial implementation. Unlike previous studies that only evaluate model performance in Integrated Development Environments (IDEs), this research conducts comprehensive evaluation across both IDE and industrial web platforms. The Mask R-CNN model is modified by replacing the default ResNet-101 backbone with optimized variants including ResNet50-FPN and MobileNet-FPN to address performance degradation during deployment. A dataset comprising 1,611 coconut shell images with 8,922 annotated objects across four quality classes was created for training. Experimental results demonstrate that MobileNet-FPN achieves optimal balance between accuracy and computational efficiency, with 96% mAP@0.50 on training and 84% mAP@0.50 on validation. Class-wise analysis reveals