Using High-Resolution Imagery and Artificial Intelligence to Support Climate Change Resilience in Agroforestry Across the Pacific
Description
On remote Pacific islands and outer atolls, agroforestry (i.e., the cultivation and conservation of trees for agriculture) provides food security and income to local communities. Growing instability from climate change and invasive species like the coconut rhinoceros beetle threaten these resources. Actively managing and sustaining agroforestry resources requires detailed and up-to-date knowledge of forest inventories and conditions.
Detailed example
Project researchers will build capacity for conducting detailed agroforestry assessment and monitoring in Pacific Island nations, by using imagery collected from small unmanned aerial systems (sUAS or “drones”) and custom computer algorithms to automatically detect and monitor the health of coconut trees and other species of importance.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The results from this work can be used by smallholder coconut farmers and processors and local and national government agencies to better manage agroforestry resources for coconut, pandanus, and other species of interest across the Pacific Island region.
Controls / human review
ATO: No; PIA: Not published