MACHINE LEARNING-BASED MULTI-ARRAY SENSOR SYSTEM FOR RICE CLASSIFICATION

SHAHKHIR MOZAMIR; Burhanuddin Aboobaider

doi:10.54554/jacta.2025.07.02.004

Authors

SHAHKHIR MOZAMIR UNIVERSITI TEKNIKAL MALAYSIA MELAKA
Burhanuddin Aboobaider

DOI:

https://doi.org/10.54554/jacta.2025.07.02.004

Keywords:

Multi-Array Sensor, Machine Learning, Rice Classification, Decision Tree Algorithm, Smart Food Quality Monitoring

Abstract

Ensuring consistent rice quality is crucial for maintaining consumer confidence and supporting standards within the rice industry. Traditional grading methods, which depend on manual visual inspection, are often subjective, time-consuming, and heavily reliant on expert judgment. To address these limitations, this study introduces an energy-efficient multi-array sensor system enhanced with machine learning for automated rice classification. The proposed system combines multiple sensors to capture essential physical and olfactory features of rice samples, while reducing power consumption through optimized data collection and processing. A Decision Tree algorithm is employed to analyze the sensor data and accurately classify rice categories. Experimental results reveal that the system achieves an 80% accuracy rate, confirming its potential as a dependable, low-power solution for real-time rice evaluation. Overall, this work demonstrates the effectiveness of integrating sensor fusion and AI-driven analysis to advance intelligent and sustainable food quality monitoring systems.

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