Content
Pest and disease detection is an important component for Integrated Pest Management (IPM) strategies in all sectors of horticulture. Traditional methods rely on human scouts and laboratory testing to monitor and react to pest and disease incidence. Technologies including computer vision, data science and Artificial Intelligence (AI), and novel sensors examining air and fertigation samples are currently being developed and commercialized.
Content
The opportunity
Vineland seeks to explore new opportunities in this area including testing and validation of solutions on the market, co-development of new technologies with commercial partners, and assisting with adoption for growers and grower organizations. As new sensors, computer vision algorithms, and data processing and analysis techniques continue to be improved, we expect to see capabilities for improving traditional methods expand dramatically. We also envision digital twins, modelling, simulation, and generative AI as playing a key role in building upon real-life data to discover new strategies and improve technology performance.
The Goal
The goal of this project is to work with industry stakeholders and clients to develop, optimize, integrate, validate, and foster adoption for new high-tech pest and disease detection and forecasting solutions.
The Objectives
- Assist with development of new technologies through partnerships
- Test and validate current and upcoming technologies for technology providers
- Develop new IPM methods based on the latest technologies as they become available and proven
- Foster technology adoption by growers in various sectors of horticulture.