10077647AIPPN: Interpretable AI enabled Molecular Identification Pipeline for Plant-parasitic NematodesClosedCollaborative R&DInnovate UKPlant parasitic nematodes (PPNs) are destructive pests of many crops in temperate, sub-tropical, and tropical agricultural systems. Damage caused by their feeding results in a 14% loss in global crop yield. Furthermore, PPNs, can form damaging disease complexes with fungal and bacterial pathogens and act as virus vectors. Decisions on management, such as nematicide application, biopesticide use, choice of resistant/tolerant varieties and rotation length is frequently based on soil testing to determine the density of genera or species present. Following soil sampling, the extracted nematodes are assessed using taxonomic characteristics and dichotomous keys. Molecular tests such as qPCR may also be used in nematode diagnosis but these are mainly used to identity individual species of interest. To determine the array of free living PPNs in a soil sample, trained taxonomists, typically with at least 3-4 years of experience, are needed. Unfortunately, such specialists are in decline and there are few training providers. Cyst nematodes, consisting of the economically important genera _Globodera_ and _Heterodera_ spp., are routinely assessed in crops such as potatoes and sugar beet. The potato cyst nematodes (_Globodera_ _rostochiensis_ and _G. pallida_) occur in 48% of the land used for potato production in England and Wales and are associated with annual losses amounting to £26-50M. Current assessment of potato cyst nematode is mainly based upon the morphological characterisation of cyst shape (female nematodes) by competent analysts. In a recent BBSRC project we developed an early-stage solution to this problem by building a CNN-based PPN detection model to recognise important PPN genera from soil (BBSRC project BB/X01200X/1). Here, we propose to build on the success of this project by expanding interpretable AI models to improve the accuracy and robustness of PPN identification, classification, and quantification. This project will provide a transformative solution for this technical skills gap by developing, training and testing an interpretable AI based automatic image recognition technique that will act as an alternative to the standard PPN identification system. Particularly, this technique is able to: 1) extract morphological PPN features from mixed PPN samples; 2) access PPN features' sensitivity in PPN recognition; 3) achieve accurate PPN classification and quantification. It will build on existing resources in the consortium (BB/X01200X/1), including: 2K PPN images (SparkSoft), a world-leading pest detection model (PestNet; UoS), agronomic and pest management (ADAS), Outputs of this study would represent an important advancement in the rapid screening of nematodes and have many research applications.