Researcher, agr., Ph.D.
450 653-7368
ext 340
Researcher, Ph.D.
450 653-7368
ext 360
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
Project duration
Fruit production
Activity areas
Pest, weed, and disease control
Service
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 aim of this project is to determine the combined impact on fungicide efficacy of rain and the appearance of new leaves to more accurately identify how long treatments remain effective.
Researcher: Vincent Philion
The objective of this project was to determine whether the addition of two types of organic fertilizers or biostimulants would produce more vigorous plants less subject to decline.
Researcher: Christine Landry
Improving the RIMpro software to better predict the risk of infection during rainfall.
Researcher: Vincent Philion