Arable - NemaRecognition: An AI-and molecular-driven pipeline for throughput plant parasitic nematode recognition

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NemaRecognition will be a machine learning based automatic image recognition technique capable of real-time detection of PPNs using digital images/videos.Plant clinics carry out a suite of services for growers and their advisers. A key service is the assessment of soil samples for PPN. PPN screening is carried out through time-intensive taxonomic identification, this is reliant on taxonomic expertise and several years of training. Trained nematologists are in short supply, causing concern in the industry as accurate and reliable identification of PPN is a critical factor influencing agronomic decisions.PPN affect various crops and can devastate yields, with losses up to 35% (AHDB, 2017). Growers screen fields prior to planting to identify and quantify PPN to help decide on the crop to be planted/avoided, guide variety choice, and advise control strategies. PPN screening can cost £70 per field per season and represents a substantial cost. More rapid, cost-effective assessment methods would represent a cost saving to growers.Alternatives, such as molecular-based tests, have been developed but have substantial shortcomings in accuracy, breadth of use, and grower-confidence. AI algorithms have been developed for nematode identification; however, the majority only identify one PPN genera (Bogale et al., 2020; Akintayo et al., 2018). NemaRecognition would represent an innovative state-of-the-art solution for PPN assessment by providing recognition for multiple PPN genera, and through further development would become one of the first machine learning-based techniques providing plant health services to UK growers.Image-recognition techniques have been developed for other agricultural pests (e.g., insects). However, significant challenges to producing a transformative PPN recognition system using machine learning techniques remain, including recognition of a range of PPN genera, detection in field samples, recognition through video-capture, validation, benchmarking, and selection of appropriate models.NemaRecognition would have myriad benefits, including reduced grower costs (passed down through plant clinic cost savings), increased accessibility to PPN screening in regions where services are inhibited by a taxonomic skills shortage, and as a training tool to help address the taxonomic skills shortage within the industry. Global challenges have been influential in creating this opportunity: UK net-zero farming, EU Sustainable Use Directive, UK path to sustainable farming.The NemaRecognition project will showcase the feasibility and applicability of this technology toward PPN detection and would also represent proof-of-concept for developing similar innovations for other soil-dwelling organisms, with significant potential in the growing area of soil health services.