An ANN ‐Driven Fermenter System for Bacterial Health Assessment During Cocoa Bean Fermentation

ABSTRACT Cocoa bean fermentation is a critical process that determines the final quality and flavor profile of chocolate. This study presents an Artificial Neural Network (ANN)–based smart fermenter designed to monitor and optimize bacterial health during fermentation. The system integrates multi‐sensor data acquisition (pH, temperature, humidity) to maintain ideal fermentation conditions (30°C–45°C, pH 4–4.5, 75% RH), essential for microbial activity and bean quality. A Backpropagation Neural Network (BPNN) processes sensor data to predict bacterial health status and classify fermentation efficiency. Experimental results demonstrate the model's robust performance, achieving 68.85% accuracy, 95.29% precision, 70.43% recall, 42.8% specificity, and an 80.99% F 1‐score. By enabling real‐time monitoring and control, this ANN‐driven system provides a data‐driven approach to standardizing cocoa fermentation, enhancing process reliability, and ultimately improving chocolate product quality.