Monash University

Restricted Access

Reason: Restricted by author. A copy can be supplied under Section 51(2) of the Australian Copyright Act 1968 by submitting a document delivery request through your library

Deep-learning and Computer-vision Facilitated Pollination Monitoring for Agriculture

posted on 2022-10-25, 12:08 authored by MALIKA NISAL RATNAYAKE RATNAYAKE MUDIYANSELAGE
This research conceptualises, develops and applies novel computer vision and deep learning software to enhance our understanding of insect pollinator behaviour in agriculture and ecology. Pollinators underpin around a third of food for human consumption, and insects, especially bees, are essential for global sustainability. Improving our understanding of insect pollination through monitoring is required for enhancing crop production and food security. Whilst existing methods of pollination monitoring are manual, digital technologies I introduce automatically capture and analyse measures of flower visitation by insects. This solution enables data-driven crop pollination management and transforms insect biodiversity monitoring.


Campus location


Principal supervisor

Alan Dorin

Additional supervisor 1

Adrian G. Dyer

Year of Award


Department, School or Centre

Data Science & Artificial Intelligence


Doctor of Philosophy

Degree Type



Faculty of Information Technology