Machine Learning in Ethnobotany -- a first experiment

TL;DR

This paper explores how machine learning can enhance ethnobotany documentation, focusing on mobile-based methods in emerging economies, exemplified by a Bali case study, to better capture traditional ecological knowledge.

Contribution

It introduces a novel machine learning approach tailored for ethnobotany documentation using mobile phones, addressing disparities between global research contexts.

Findings

Machine learning can improve ethnobotanical data collection.

Mobile technology enables documentation in resource-limited settings.

The Bali case study demonstrates practical application and potential benefits.

Abstract

We describe new opportunities afforded by bring A.I to the field of ethnobotany. In particular we describe a novel approach to ethnobotany documentation that harnesses machine learning opportunities, specifically for the documentation of traditional ecological knowledge with mobile phones in emerging economies. Using a case study on the island of Bali as a departure point, the project maps out machine learning approaches to documentation and responds to technology and capital gradients between research contexts in the global north and south in an attempt to capture knowledge that might otherwise not be represented.