Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data
Authors: Benjamin Akera, Joyce Nakatumba-Nabende, Ali Hussein, Daniel Ssendiwala, Jonathan Mukiibi
Keywords: keyword spotter, radio data, crop pest and disease, agriculture
TL;DR: This paper describes an approach to analyse community radio data with machine learning-based speech keyword spotting techniques for crop pest and disease monitoring.
Abstract: In societies with well developed internet infrastructure, social media is the leading medium of communication for various social issues especially for breaking news situations. In rural Uganda however, public community radio is still a dominant means for news dissemination. Community radio gives audience to the general public especially to individuals living in rural areas, and thus plays an important role in giving a voice to those living in the broadcast area. It is an avenue for participatory communication and a tool relevant in both economic and social development.This is supported by the rise to ubiquity of mobile phones providing access to phone-in or text-in talk shows. In this paper, we describe an approach to analysing the readily available community radio data with machine learning-based speech keyword spotting techniques. We identify the keywords of interest related to agriculture and build models to automatically identify these keywords from audio streams. Our contribution through these techniques is a cost-efficient and effective way to monitor food security concerns particularly in rural areas. Through keyword spotting and radio talk show analysis, issues such as crop diseases, pests, drought and famine can be captured and fed into an early warning system for stakeholders and policy makers.