| In the potato industry, PVY has resulted in significant economic harm to farmers and has, at times, disrupted seed supplies to commercial growers, especially in varieties with good marketing attributes but high disease susceptibility such as Russet Norkotah. Commercial potato growers rely entirely on seed producers and certification systems to get disease-free seed as they have no recourse to mitigate seed-borne PVY after the seed is planted. However, seed growers and certification agencies are currently unable to control PVY infection in the industry’s seed pipeline and this has a significant impact on commercial markets and regional economies. In this study, it is shown that PVY-infected potato plants in an agricultural production field produce different spectral signatures than neighboring non-infected plants. Using machine learning or machine vision analysis such as support vector machine classifiers can differentiate spectral signature of PVY-infected and non-infected plants at an accuracy of 89.8 percent. This was achieved in a field showing significant crop canopy variability as identified by remote sensing and cluster analysis. |