ArticleSensors (Basel, Switzerland)2026
Data-Driven Real-Time Rice Milling Optimisation via YOLO26 Machine Vision and Adaptive Closed-Loop Motor Control.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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