Classification of Broadleaf and Grass Weeds Using Gabor Wavelets and an Artificial Neural NetworkTransactions of the ASAE
Publication VersionPublished Version
AbstractA 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.
Copyright OwnerAmerican Society of Agricultural Engineers
Citation InformationLie Tang, Lei F. Tian and Brian L. Steward. "Classification of Broadleaf and Grass Weeds Using Gabor Wavelets and an Artificial Neural Network" Transactions of the ASAE Vol. 46 Iss. 4 (2003) p. 1247 - 1254
Available at: http://works.bepress.com/lie_tang/4/