AI in Crop Monitoring

Course Description

Learn how AI turns imagery and field data into timely, actionable insights for crop health and performance. This micro-course shows how to analyse satellite and drone images, detect diseases and pests early, predict yields, and assess soil health and moisture—reducing guesswork, inputs, and risk while boosting sustainability.

Who Should Take This Course

Ideal for farm managers, agronomists, sustainability leaders, operations managers, agtech teams, and data/BI analysts supporting agriculture. Also suitable for consultants and managers tasked with planning or overseeing AI upskilling across agricultural organisations.

Prerequisites

No prior experience needed. Helpful but optional: basic agriculture concepts, familiarity with spreadsheets and map-based tools (e.g., GIS).

What You Will Learn

• How to interpret satellite and drone imagery with AI (e.g., vegetation indices and segmentation)
• Practical approaches for early disease and pest detection using computer vision
• Methods to build or evaluate predictive yield models for field prioritisation
• Techniques to infer soil health and moisture from multispectral data and simple ML workflows
• A framework to turn model outputs into alerts, dashboards, and decisions—plus key risks, ethics, and adoption tips