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Data-Driven Real-Time Rice Milling Optimisation via YOLO26 Machine Vision and Adaptive Closed-Loop Motor Control
Benjamin Ilo1, Yogang Singh2, Hongwei Zhang1
1Advanced Food Innovation Centre (AFIC), Sheffield Hallam University, Sheffield S9 2AA, UK.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a cloud-based system using YOLOv26 machine vision and Arduino control to reduce rice breakage during milling. The closed-loop system significantly improved milling quality, achieving Grade A-equivalent standards.
Area of Science:
- Agricultural Engineering
- Computer Vision
- Machine Learning
- Process Control
Background:
- Conventional rice milling quality inspection occurs post-process, preventing real-time adjustments and leading to increased grain breakage.
- Lack of immediate feedback in traditional milling processes hinders optimization and results in suboptimal product quality.
Purpose of the Study:
- To develop and validate a cloud-mediated, closed-loop control system for real-time rice milling quality adjustment.
- To quantitatively assess the effectiveness of a YOLOv26 machine vision pipeline coupled with Arduino-based actuators in reducing rice breakage.
Main Methods:
- A cloud-mediated architecture was implemented, integrating a YOLOv26 machine vision pipeline for broken rice detection with Arduino microcontrollers for actuator control.
- Image data was acquired, uploaded to the cloud, and processed for broken rice fraction estimation using YOLOv26 and a hybrid classifier.
- Actuators (motor and vibrator) were controlled via Pulse Width Modulation (PWM) based on real-time quality feedback.
Main Results:
- The YOLOv26 detector achieved high performance with a mean Average Precision of 0.951 on a test set.
- Closed-loop operation significantly reduced the broken rice fraction from 21.04% to 6.19% compared to an open-loop baseline.
- Dynamic analysis revealed a near-linear plant gain, quantifying the relationship between PWM input and breakage percentage.
Conclusions:
- The developed closed-loop system effectively reduces rice breakage, moving laboratory prototype quality from non-compliant to Grade A-equivalent at constant throughput.
- The study provides a quantitative characterization of the actuator's transfer relationship under deep learning feedback, enabling future control system synthesis.
- A roadmap for scaling the technology from laboratory prototype to pilot-scale deployment is outlined.
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