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OPTIFARM: Benchmarking YOLO Architectures for Location-Robust Potato Quality Detection.

Tadej Peršak¹, Marko Simonič¹, Jernej Hernavs¹

¹Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia.

Foods (Basel, Switzerland)

|June 26, 2026

Summary

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Automated potato sorting using low-cost RGB cameras and deep learning is feasible. The YOLO26 model achieved the best performance in detecting potato quality across different farm locations, proving system readiness.

Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Manual potato sorting is labor-intensive, subjective, and limits scalability.
Automated quality detection is crucial for efficient post-harvest processing.

Purpose of the Study:

To assess the feasibility of a low-cost RGB optical system for automated potato quality detection.
To benchmark deep learning object detection models for potato sorting.

Main Methods:

Constructed a controlled imaging platform with commodity hardware.
Collected and annotated a dataset of 19,805 potato instances across 1361 images.
Benchmarked 25 YOLO model configurations (YOLOv8-YOLO26) using cross-location evaluation.

Main Results:

Keywords:

YOLO cross-location generalization food quality and safety intelligent inspection non-destructive testing object detection potato sorting

Related Experiment Videos

All models showed high in-distribution performance (F1 ≥ 0.906).
Cross-location performance varied (F1: 0.792-0.918), with YOLO26 achieving the best results (F1=0.918).
Feed detection emerged as a generalization bottleneck.

Conclusions:

Affordable RGB-based potato sorting systems are technically viable.
Cross-location evaluation is essential for assessing real-world deployment readiness.
Transferable representations are achievable with standard supervised training.