Agricultural and Biosystems Engineering Publications

Campus Units

Agricultural and Biosystems Engineering

Document Type

Article

Publication Version

Published Version

Publication Date

2003

Journal or Book Title

Transactions of the ASAE

Volume

46

Issue

4

First Page

1247

Last Page

1254

Abstract

A texture–based weed classification method was developed. The method consisted of a low–level Gabor wavelets–based feature extraction algorithm and a high–level neural network–based pattern recognition algorithm. This classification method was specifically developed to explore the feasibility of classifying weed images into broadleaf and grass categories for spatially selective weed control. In this research, three species of broadleaf weeds (common cocklebur, velvetleaf, and ivyleaf morning glory) and two grasses (giant foxtail and crabgrass) that are common in Illinois were studied. After processing 40 sample images with 20 samples from each class, the results showed that the method was capable of classifying all the samples correctly with high computational efficiency, demonstrating its potential for practical implementation under real–time constraints.

Comments

This article is from Transactions of the ASAE, 46, no. 4 (2003): 1247–1254.

Access

Open

Copyright Owner

American Society of Agricultural Engineers

Language

en

File Format

application/pdf

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