This project is about using artificial intelligence to interpret agricultural remote sensing data. We will develop new means to integrate repeated imagery data of targeted agricultural fields to pinpoint agronomically significant anomalies (e.g., water or nutrient stress, crop pathology, weeds, etc.) and provide field managers easy to follow recommendations guiding development of the most cost […]

The project aims to improve the amount of labelled samples in a semi-automatic or automatic manner using AI to impove a CNN performance. We will test various state-of-the-art AI methods, in the context of forest inventory, and select the most effective ones. The benefits will be significant because labelling is an important but tedious task, […]