Automated traps theoretically increase monitoring accuracy, allow for better targeting of pesticide treatments at a lower cost, reduce the number of field visits (longer monitoring intervals), and facilitate sharing of monitoring data while maintaining its accuracy. The aim of this project is to measure the potential of this technology and extrapolate it to an apple-monitoring network. The five parameters identified above will be measured for three years using a monitoring network for five species on a minimum of seven sites in Québec’s main apple-growing regions. Various types of automated attractant traps (by Spensa, Trapview, and IRDA) will be compared to standard monitoring traps for the following pests (excluding cases of incompatibility of a system with certain pests): apple sawflies, apple maggots, obliquebanded leafrollers, codling moths, and dogwood borers. The IRDA trap is a homemade assembly consisting of a trap, a camera, a modem, and commonly available accessories. The comparisons will serve to determine the recommended methods for the tested technologies on the farm and in Québec’s apple R&D and knowledge transfer network.
From 2018 to 2021
Pest, weed, and disease control
This project will help to better target pesticide treatments and improve their cost-effectiveness.
Centre de recherche sur les grains | Ministère de l'Agriculture, des Pêcheries et de l'Alimentation du Québec - Prime-Vert Programme | Technical Support Clubs
The goal of this project was to document the impact of climate change on fruit crop pests and diseases in Québec.
Researcher: Annabelle Firlej
Exploration of the potential of detecting water stress in lowbush blueberries using a thermal infrared imaging sensor installed on a drone.
Researcher: Carl Boivin
The aim of this project was to test the general hypothesis that exclusion nets, when properly used, can prevent attacks by most apple pests and reduce disease incidence with no major adverse effects on fruit quality.
Researcher: Gérald Chouinard