The use of X-ray Computed Tomography revolutionises soil pathogen detection

Potato cyst nematodes (PCN), namely Globodera pallida, pose a significant threat to potato production worldwide. Accurately quantifying nematode population densities is crucial for effective management strategies. However, traditional detection methods are time-consuming and often imprecise. X-ray computed tomography (CT) offers high-resolution, non-destructive and three-dimensional (3D) imaging frequently used to visualise internal structures of objects, materials or biological tissues, allowing for detailed analysis of their composition, defects, and morphology. Here, we demonstrate the effectiveness of X-ray CT in detecting and quantifying PCN cysts in different soil types. The ability to achieve 3D imaging and volume quantification of PCN cysts from soils allows accurate enumeration of their egg content. To explore future potential, we propose that when integrated with AI-led quantification of X-ray CT-generated cyst counts, this method could evolve into a next-generation technology to accurately and rapidly quantify these damaging pests in agricultural soils. Combined with the potential to co-analyse other organisms within the same sample, we propose X-ray CT as a fundamental tool for comprehensive soil health assessments and sustainable pest management in agriculture. Author SummaryPotato cyst nematodes are a major threat causing significant crop losses worldwide. Traditionally, detecting this pathogen in soil is challenging, often requiring time-consuming and imprecise. Our study explores a new approach using X-ray computed tomography (CT), a powerful imaging tool that creates detailed 3D images in situ. We observed that X-ray CT can effectively detect and measure the cysts nematodes, even within different soil types. This detailed analysis allows us to determine the cyst structures and estimate the eggs contained. In the future, combining X-ray CT with artificial intelligence could make detecting and quantifying these nematodes even faster and more accurate. This method could also be expanded to study other soil organisms, making it a valuable tool for monitoring soil health and improving agricultural pest management.