20.500.12556/DKUM-98499 OPTIFARM: Benchmarking YOLO architectures for location-robust potato quality detection Potato sorting in post-harvest processing relies heavily on manual visual inspection, which is physically demanding, subjective, and insufficiently scalable for modern packing lines. This study investigates the feasibility of a low-cost RGB-based optical inspection system for automated potato quality detection using deep learning-based object detection. A controlled imaging platform was constructed using commodity hardware, and a dataset of 19,805 manually annotated instances across 1361 images was collected from two geographically distinct farm locations in Slovenia. A systematic benchmark of 25 model configurations spanning five YOLO architecture families—YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLO26—was conducted across three practical quality classes (Edible, Feed, Rotten) using a strict cross-location evaluation protocol in which models were trained on one location and tested on a completely unseen second location. All models achieved strong in-distribution performance (F1 ≥ 0.906), but showed considerable variation under cross-location conditions, with external F1 ranging from 0.792 to 0.918. The yolo26_l configuration achieved the best cross-location performance (F1 = 0.918, mAP@0.5:0.95 = 0.816, ΔF1 = 0.029), demonstrating that transferable representations are achievable under a standard supervised training protocol. Per-class analysis identified feed detection as the primary generalization bottleneck. The results confirm that affordable RGB-based sorting systems are technically feasible and highlight cross-location evaluation as an essential protocol for assessing real-world deployment readiness. potato sorting object detection YOLO cross-location generalization food quality and safety intelligent inspection non-destructive testing sortiranje krompirja odkrivanje predmetov posplošitev med lokacijami kakovost in varnost hrane inteligentni pregled nedestruktivno testiranje true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/2304-8158/15/12/2121 Članek v reviji 2026-06-17 10:17:13 2026-06-17 10:17:14 2026-06-18 03:25:29 0000-00-00 00:00:00 2026 0 0 29 str. iss. 12, [article no.] 2121 Vol. 15 June 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2026-05-05 2026-06-10 2026-06-12 681.5:004.9 281687811 10.3390/foods15122121 2304-8158 https://www.mdpi.com/2304-8158/15/12/2121 1 f2ffdba3-6a24-11f1-87c9-00155d580e34 https://dk.um.si/Dokument.php?lang=slv&id=207223 foods-15-02121.pdf foods-15-02121.pdf 1 C59C3D5200DDDBD60BFA4A0A823A682D b62ed0d0268d2f783e563fcd8c733719827cad115c966a36c424c48bf624c21e 44ebf296-6a25-11f1-87c9-00155d580e34 https://dk.um.si/Dokument.php?lang=slv&id=207224 Fakulteta za strojništvo 0 0 0