Tampere University of Technology

TUTCRIS Research Portal

Automatic Flower and Visitor Detection System

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages405-409
Number of pages5
ISBN (Electronic)978-9-0827-9701-5
ISBN (Print)978-1-5386-3736-4
DOIs
Publication statusPublished - Sep 2018
Publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Period1/01/00 → …

Abstract

The visit patterns of insects to specific flowers at specific times during the diurnal cycle and across the season play important roles in pollination biology. Thus, the ability to automatically detect flowers and visitors occurring in video sequences greatly reduces the manual human efforts needed to collect such data. Data-dependent approaches, such as supervised machine learning algorithms, have become the core component in several automation systems. In this paper, we describe a flower and visitor detection system using deep Convolutional Neural Networks (CNN). Experiments conducted in image sequences collected during field work in Greenland during June-July 2017 indicate that the system is robust to different shading and illumination conditions, inherent in the images collected in the outdoor environments.

Keywords

  • image sequences, learning (artificial intelligence), neural nets, visitor detection system, season play important roles, pollination biology, video sequences, manual human efforts, data-dependent approaches, automation systems, deep Convolutional Neural Networks, Image segmentation, Training, Task analysis, Insects, Image resolution, Signal processing, Lighting

Publication forum classification

Field of science, Statistics Finland