<![CDATA[Indonesia produces large volumes of fruits and vegetables yet faces high food loss and waste (FLW), with studies estimating 23-48 million tons of food waste annually. Observations in Grobogan Regency show traders discarding around 30-40% of daily stock, often produce that is edible but visually imperfect (grade B/C). This study designs and evaluates E-Buyur Market, a digital marketplace embedding an on-device hybrid AI pipeline to automatically signal fruit-vegetable quality. The system combines a YOLO-TFLite commodity detector with a multitask MobileNetV3-Small TFLite grader to predict type, edibility, and a quality score derived from freshness probability. The model was trained on approximately 115,000 images compiled from publicly available Kaggle fruit-vegetable repositories, processed using data augmentation and post-training quantization for TensorFlow Lite deployment; a separate small set of real seller-captured photos was used to qualitatively test the deployed model under real usage conditions. On a held-out test set, the model achieves a type-classification accuracy of 0.91, freshness ROC-AUC of 0.94, PR-AUC of 0.92, and inference latency of tens of milliseconds on mid-range phones. Based on the system's design and signaling-theory rationale, on-device hybrid AI is expected to improve sell-through, reduce shrinkage, and shorten time-to-list for MSME sellers; empirical validation of these business-level outcomes through a live field trial remains future work.]]>
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