A robotic system employing deep learning for visual recognition and detection of weeds in Grasslands

Tsampikos Kounalakis, Michal Jerzy Malinowski, Leandro Chelini, Georgios A. Triantafyllidis, Lazaros Nalpantidis

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

13 Citations (Scopus)

Abstract

In this paper, we describe the vision system of a robot prototype that operates in dairy farm grasslands and detects the presence of the harmful Broad-leaved dock (Rumex obtusifolius L.). Image data were collected using the prototype robot from 3 fields in 2 different countries under real conditions. The proposed recognition and detection system is using solely 2D visual input and is based on state-of-the-art Convolutional Neural Networks (CNNs), which were used for high level feature extraction in combination with various examined classifiers. Gathered data were used to experimentally show that the proposed system yields state-of-the-art detection and recognition performance, while being able to keep low false-positive rates under challenging operation conditions.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Imaging Systems and Techniques (IST)
Number of pages6
Place of PublicationKrakow, Poland
PublisherIEEE
Publication date14 Dec 2018
Article number8577153
ISBN (Print)978-1-5386-6629-6
ISBN (Electronic)978-1-5386-6628-9
DOIs
Publication statusPublished - 14 Dec 2018
Event2018 IEEE International Conference on Imaging Systems and Techniques, IST 2018 - Krakow, Poland
Duration: 16 Oct 201818 Oct 2018

Conference

Conference2018 IEEE International Conference on Imaging Systems and Techniques, IST 2018
Country/TerritoryPoland
CityKrakow
Period16/10/201818/10/2018
SeriesIEEE International Conference on Imaging Systems and Techniques (IST)
ISSN1558-2809

Fingerprint

Dive into the research topics of 'A robotic system employing deep learning for visual recognition and detection of weeds in Grasslands'. Together they form a unique fingerprint.

Cite this