Smart traps for improved surveillance and early detection of regulated plant pests
Lead Participant:
UNIVERSITY OF GREENWICH
Abstract
The whitefly _Bemisia_ _tabaci_ is a major pest of economical important crops. The pest causes damage to plants directly by feeding and indirectly through honeydew produced that encourages sooty mould growth on leaves. _B_. _tabaci_ is also a vector of plant viruses that can cause serious damage to horticultural crops including tomato and cucumber.
_Bemisia_ _tabaci_ is a regulated plant pest in the UK. It is one of the most frequently intercepted pests on imported plants especially in poinsettia (_Euphorbia_ _pulcherrima_), with increasing number of outbreaks occurring in ornamental production nurseries. This can have follow-on effects for the horticultural sector in the UK due to the potential for _B_. _tabaci_ to introduce and spread viruses on high-value crops (e.g., tomato).
Currently inspections are carried out by visual inspection of plants and/or using in-field yellow sticky traps. This provides monitoring information on the abundance and diversity of pests in imported plants or plant material. These inspections however are time consuming and labour intensive as they require inspectors to invest large amounts of time checking the traps as insects stuck on these can be difficult to identify due to similarities with nonquarantine species such the common glasshouse whitefly _Trialeurodes_ _vaporariorum_. As a result, traps need to be sent to a laboratory for identification. This proposal brings an innovative approach to enhance the efficiency of inspections. We will develop and evaluate a novel prototype smart trap for automatic and real-time detection of _B_. _tabaci_ based on those successfully used for mosquitos and bees and that can be used to support UK biosecurity against regulated plant pests. The smart trap is a combination of a suction trap complemented with an optical sensor powered by machine learning. Automation, passive and real-time data collection and transfer are crucial in building effective surveillance systems to provide early warning of the presence of pests and should facilitate the uptake of this technology.
_Bemisia_ _tabaci_ is a regulated plant pest in the UK. It is one of the most frequently intercepted pests on imported plants especially in poinsettia (_Euphorbia_ _pulcherrima_), with increasing number of outbreaks occurring in ornamental production nurseries. This can have follow-on effects for the horticultural sector in the UK due to the potential for _B_. _tabaci_ to introduce and spread viruses on high-value crops (e.g., tomato).
Currently inspections are carried out by visual inspection of plants and/or using in-field yellow sticky traps. This provides monitoring information on the abundance and diversity of pests in imported plants or plant material. These inspections however are time consuming and labour intensive as they require inspectors to invest large amounts of time checking the traps as insects stuck on these can be difficult to identify due to similarities with nonquarantine species such the common glasshouse whitefly _Trialeurodes_ _vaporariorum_. As a result, traps need to be sent to a laboratory for identification. This proposal brings an innovative approach to enhance the efficiency of inspections. We will develop and evaluate a novel prototype smart trap for automatic and real-time detection of _B_. _tabaci_ based on those successfully used for mosquitos and bees and that can be used to support UK biosecurity against regulated plant pests. The smart trap is a combination of a suction trap complemented with an optical sensor powered by machine learning. Automation, passive and real-time data collection and transfer are crucial in building effective surveillance systems to provide early warning of the presence of pests and should facilitate the uptake of this technology.
Lead Participant | Project Cost | Grant Offer |
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Participant |
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UNIVERSITY OF GREENWICH |
People |
ORCID iD |
Goncalo Silva (Project Manager) |