AI for weed management and detection

EMILI is working with Geco Engineering to assess the performance of their techniques for integrated weed management including predicting weed locations and detecting emerging herbicide resistance. We are using drone flights and satellites to collect imagery on kochia and wild oat.

This project will enable farmers to detect, isolate and control herbicide resistant weeds earlier in-season while they are still young and easier to manage. In the future, digital agriculture technologies such as strategic weed targeting through AI will help limit yield losses and the spread of herbicide resistant weeds. This will increase both production and income for farmers worldwide

Using AI to predict herbicide resistant weed patches

We will be gathering an assortment of weed data with a particular interest in locations with recurring wild oat and kochia weed patches on Innovation Farms. Geco Engineering will use the data we collect to assess the performance of artificial intelligence (AI) in predicting the locations of herbicide resistant weeds. The AI will allow farmers to target specific weed patches, which reduces pesticide applications and promotes environmental health and sustainability while saving time and money.

An integrated weed management approach

If weed species are not eradicated early in the season, they can contribute to the weed seed bank in the field and drastically affect crop yields by competing for valuable sunlight, water, and nutrients. An integrated weed management approach combines cultural and chemical practices. The goal is to increase crop competition against weeds by using multiple modes of effective chemical action to limit the introduction or spread of resistant seeds. Preventing the spread of herbicide resistant weeds is critical to avoid further amplifying resistant populations.

Learn more about Geco Engineering

Watch this video to learn more about Geco Engineering's approach to detecting and predicting herbicide resistant weeds in farm fields.