Potato-Free: Eliminating Model Training for Disease Detection and Localization in Potato Tubers

Potato production has become a major global food crop, generating high profitability while reducing the pressure on the world’s food supply chain. Therefore, potato tuber disease inspection for quality control and food security is of utmost importance. With the emergence of Artificial Intelligence (AI), Deep Convolutional Neural Networks (DCNNs) have become the most widely employed approach for this task. However, DCNNs mainly require large annotated datasets and often generalize poorly to unseen conditions or potato tuber diseases. In this paper, Potato-Free is introduced as a training-free framework for potato disease detection and localization. In detail, the framework extracts potato tuber masks, estimates the average color of healthy tubers, and generates colored potato masks. These masks are compared with the potato tuber images to produce MSE maps, from which optimal thresholds for potato tuber disease detection and localization are determined. Experimental results show that the proposed framework achieves a macro F1-Score of 63.20% in a cross-dataset evaluation on completely unseen potato tuber diseases, providing comparable performance to State-of-the-Art DCNN classification models (macro F1-Score of 63.40%) while also outperforming a reconstruction-based baseline (macro F1-Score of 42.92%), without requiring training. An extensibility study also illustrates that Potato-Free can separate different disease types with a macro F1-Score close to the best-performing DCNN model (5.02% difference in macro F1-Score) despite being a threshold-based framework that does not require training.