Data-Driven Real-Time Rice Milling Optimisation via YOLO26 Machine Vision and Adaptive Closed-Loop Motor Control

<jats:p>Rice-milling quality is conventionally inspected post-process, leaving operators unable to correct breakage as it occurs. We present and quantitatively validate a cloud-mediated closed-loop architecture that couples a YOLO26 machine-vision pipeline to Arduino-based actuator control on a laboratory rice mill. The image-acquisition node uploads frames to a cloud repository; an inference and analysis node retrieves them, runs YOLO26 detection with a hybrid post-process classifier to estimate the broken-rice fraction, and issues a command to an Arduino microcontroller that drives PWM-modulated motor and vibrator actuators. The detector achieved a mean Average Precision of 0.951 (peak precision 0.99, peak recall 0.98) on a held-out test set of 100 images. In a matched comparison against an open-loop baseline (n=196,000 kernels, broken fraction 21.04%), closed-loop operation (n=114,000 kernels) reduced the broken fraction to 6.19%, an absolute improvement of 14.85 percentage points (two-proportion z-test: z=112.8, p&lt;0.001, 95% CI for the absolute reduction: 14.62–15.08 pp). Dynamic analysis identified a near-linear plant gain of 1.0–1.5% breakage per 1% PWM, providing the empirical basis for future formal PID and Model Predictive Control synthesis. The principal empirical contribution is a quantitative characterisation of the PWM-to-breakage transfer relationship of a rice-milling actuator under deep-learning-derived quality feedback, together with a matched open-loop/closed-loop demonstration that this feedback loop moves the laboratory prototype from non-compliant to Grade A-equivalent quality at constant throughput. The lab-scale prototype is not yet industrial; a roadmap to pilot-scale deployment is outlined.</jats:p>