Next Article in Journal
Preceding Vehicle Detection and Tracking Adaptive to Illumination Variation in Night Traffic Scenes Based on Relevance Analysis
Next Article in Special Issue
Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry
Previous Article in Journal
Sensors and Technologies in Spain: State-of-the-Art
Previous Article in Special Issue
Automated In-Situ Laser Scanner for Monitoring Forest Leaf Area Index
Sensors 2014, 14(8), 15304-15324; doi:10.3390/s140815304
Article

A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method

1,* , 2,*  and 1
Received: 5 March 2014 / Revised: 7 July 2014 / Accepted: 8 August 2014 / Published: 19 August 2014
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
View Full-Text   |   Download PDF [1403 KB, uploaded 19 August 2014]   |   Browse Figures

Abstract

An important objective in weed management is the discrimination between grasses (monocots) and broad-leaved weeds (dicots), because these two weed groups can be appropriately controlled by specific herbicides. In fact, efficiency is higher if selective treatment is performed for each type of infestation instead of using a broadcast herbicide on the whole surface. This work proposes a strategy where weeds are characterised by a set of shape descriptors (the seven Hu moments and six geometric shape descriptors). Weeds appear in outdoor field images which display real situations obtained from a RGB camera. Thus, images present a mixture of both weed species under varying conditions of lighting. In the presented approach, four decision-making methods were adapted to use the best shape descriptors as attributes and a choice was taken. This proposal establishes a novel methodology with a high success rate in weed species discrimination.
Keywords: precision agriculture; weed species discrimination; fuzzy decision making strategy; colour segmentation; Hu invariant moments; geometric shape descriptors precision agriculture; weed species discrimination; fuzzy decision making strategy; colour segmentation; Hu invariant moments; geometric shape descriptors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Herrera, P.J.; Dorado, J.; Ribeiro, Á. A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method. Sensors 2014, 14, 15304-15324.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert