Research Article | Open Access

Volume 2026 |Article ID 100127 | https://doi.org/10.1016/j.plaphe.2025.100127

Detecting nematodes in potato plants an explainable machine learning approach for detection of potato cyst nematode infections using hyperspectral imaging

Janez Lapajne,¹ Nik Susi�c,¹ Andrej Von�cina,¹ Barbara Geri�c Stare,¹ Nicole Viaene,^2,3 Jonathan Van Beek,⁴ David Nuyttens,⁴ Sa�sa Sirca,¹ and Uro�s Zibrat ¹

¹Plant Protection Department, Agricultural Institute of Slovenia, Hacquetova ulica 17, Ljubljana, 1000, Ljubljana, Slovenia
²Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries, and Food, Burgemeester Van Gansberghelaan 96, Merelbeke, 9820, Belgium
³Department of Biology, Ghent University, Ledeganckstraat 35, 9000, Ghent, Belgium
⁴Technology and Food Science Unit, Flanders Research Institute for Agriculture, Fisheries, and Food, Burgemeester Van Gansberghelaan 115, Bus 1, Merelbeke, 9820, Belgium

Abstract

Potato cyst nematodes pose a major threat to potato cultivation, with infestations often going undetected for years. Early and accurate detection is crucial for effective management, necessitating reliable, large-scale monitoring methods. Hyperspectral imaging shows great promise for non-invasive nematode detection, yet distinguishing between biotic (e.g., nematodes) and abiotic (e.g., drought) stressors remains a challenge. This study investigated the stress responses of potato plants to potato cyst nematodes Globodera rostochiensis and G. pallida, and water deficiency. We generated datasets to isolate and evaluate single and combined stressor effects on plant physiology and morphology. Various machine learning models and spectral processing techniques were applied to assess classification performance. Exploratory methods identified key spectral wavelengths, while statistical analyses evaluated the significance of physiological and morphological traits. Results showed that water deficiency was the dominant classification factor (F1 = 0.95). The distinction between infected and non-infected plants reached F1 = 0.70 in well-watered conditions and 0.80 in water-deficient plants. Distinguishing nematode species and inoculation levels yielded moderate accuracy (F1 = 0.65–0.80), improving to 0.80 when combining biotic and abiotic stress. However, classifying multiple stress categories simultaneously reduced performance (F1 = 0.58). These findings highlight the challenges of stressor separation and the potential of hyperspectral imaging for nematode detection. Further research is needed to refine classification models and validate findings under field conditions, facilitating the integration of hyperspectral imaging into precision agriculture.