Arable - NemaRecognition: An AI-and molecular-driven pipeline for throughput plant parasitic nematode recognition
Lead Research Organisation:
University of Sheffield
Department Name: Computer Science
Abstract
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.
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.
Technical Summary
Plant 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. This project will provide a transformative solution for this technical skills gap by developing, training, testing, and benchmarking a machine learning based automatic image recognition technique that will act as an alternative to the standard PPN identification system. This is the first time that machine learning has been adopted for the identification of multiple economically important PPN genera from soil. The first step in the process involves se
People |
ORCID iD |
| Po Yang (Principal Investigator) | |
| Matthew Back (Co-Investigator) |