91Pro

Abstract

This pump-priming project will develop, validate, and test the feasibility of using AI-based image-recognition systems for the detection of single nematode species, or cyst counting, has been recently evidenced, showcasing the feasibility of AI-based technology for nematode and cyst detection. The project proposes to develop and validate a novel process to identify PPN species through an approach that combines machine-learning and AI-based image-analysis techniques to identify PPN genus followed by a targeted molecular assay to determine the species present.

Description

NemaRecognition will be a machine learning based automatic image recognition technique capable of real-time detection of Plant Parasitic Nematodes (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.