NemDetect: Early detection of quarantine nematodes in potatoes using remote sensing
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Abstract
Plant‐parasitic nematodes cause significant economic losses in crop production, with root‐knot nematodes (RKN) and potato cyst nematodes (PCN) being the top two in terms of agronomic impact. Early detection is crucial as nematode damage can take years to become visible and lead to full field infestations if unmanaged. Accurate detection and monitoring methods are needed for effective management. Remote sensing is a promising approach, but it needs to reliably distinguish between biotic stress from nematodes and abiotic stress such as drought. This project focuses on four nematode species and includes drought stress in experiments to ensure accuracy. The project is divided into four parts: literature review, identifying implementation opportunities, pilot case studies, and knowledge transfer. The literature review analyses the current state‐of‐the‐art in remote sensing for plant health assessment and provides guidelines for future research. Identifying implementation opportunities assesses the potential of remote sensing for nematode detection and monitoring. Pilot case studies test the effectiveness of remote sensing in detecting nematode infestation and distinguishing between biotic and abiotic stress. Knowledge transfer disseminates the findings to stakeholders. The pilot case study used hyperspectral imaging to identify infested tubers, showing promise for early detection and management. The project's guidelines for future research and implementation are also published in various manuscripts and a monography. Remote sensing is a promising approach to improve crop yields, reduce pesticide use, and promote sustainable agriculture by detecting and monitoring nematode infestations in plants.