BugIoT: An IoT system for automated, long-life IPM
69,770
2024-02-01 to 2025-01-31
Collaborative R&D
Our food supply is under unprecedented pressure. The FAO (2021) estimates that 20-40% of our crops are lost to pests, at a global industry cost of $70bn each year (WeForum 2021).
Therefore integrated pest management (IPM) strategies are increasingly adopted to control outbreaks and minimise economic impact and environmental damage. IPM approaches include use of biological control agents/mating disruption that rely upon precision monitoring to rapidly identify pest outbreaks in order to take timely action, reducing pesticide use by up to 30% and increasing yield by 40% . However, monitoring largely relies on traps with manual checks and counts and therefore monitoring doesn't always occur with the frequency needed to optimise control methods.
This project will develop a novel system to help farmers manage pests through precision monitoring without additional labour commitment.
Specifically, the project will develop a detection system that distinguishes and counts insect species, implemented as part of a trap or monitoring device adaptable to a range of crops/pests. Traps and monitoring devices will contain innovative chemical lure formulations that improve pest catch.
The resultant offering will thereby enhance agricultural productivity, reducing waste and pesticide usage, which reduces the overall carbon footprint of the industry. The innovation supports a transformative change in fruit/vegetable production by enabling farmers to get greater benefit from sustainable pest management approaches.
MothNet: Novel Approaches to Enable Fully-automated Smart Monitoring of Codling Moth
89,798
2020-09-01 to 2022-02-28
Collaborative R&D
The MothNet project will assess the early stage technical feasibility of a fully automated codling moth monitoring system. The innovative project will gather and analyse data on codling moth features based on a wide range of parameters in order to identify previously unexploited features that can be used to distinguish the species. In addition new long-life pheromone lure designs will be trialled. In this way, the project will enable partners to assess whether the system concept is feasible and should be developed further.
This project was conceived in response to feedback from within the fruit growers industry that precision trapping and monitoring systems are urgently needed. Industry requirements are for highly accurate real-time data collection without the need for skilled labour for moth identification and replacement of trap parts - such a system is not currently on the market. Alongside technical feasibility, dedicated work packages explore different business models for the product and routes to adoption in a range of geographical contexts. End-users are engaged from the outset to define target price points and technical requirements.
Successful completion of the MothNet project will lead to the development of a precision pest monitoring system that helps to reduce emissions and improve resource use efficiency by enabling rapid targeted response to pest outbreaks that in turn reduces pesticide usage and increases yields from existing agricultural land. Specifically our potential solution to the challenge of pest-predation in the fruit industry is data-driven, using widespread occurrence counts combined with environmental data to define action thresholds. By developing a fully automated system designed around reducing labour for fruit growers, our approach will increase adoption of precision approaches to bridge the productivity gap. Once the initial feasibility of species distinction has been proven, our ambition is to further refine the system to characterise sex and age of codling moth, enabling yet greater precision of control through models based on lifecycle stage of the insects. Following this, adaptation of the system to distinguish and record a range of insect species (both beneficial and harmful) creates an entirely novel device bring about a step-change in agricultural and environmental monitoring.
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