CLASSIFICATION OF BROADLEAF AND GRASS WEEDS USING GABOR WAVELETS AND AN ARTIFICIAL NEURAL NETWORK

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.

[1]  Jian Wang,et al.  Texture analysis using Gabor wavelets , 1996, Electronic Imaging.

[2]  M. Porat,et al.  Localized texture processing in vision: analysis and synthesis in the Gaborian space , 1989, IEEE Transactions on Biomedical Engineering.

[3]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[4]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[5]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[6]  Gaines E. Miles,et al.  Application of machine vision to shape analysis in leaf and plant identification , 1993 .

[7]  Lei Tian,et al.  Environmentally adaptive segmentation algorithm for outdoor image segmentation , 1998 .

[8]  G. Meyer,et al.  Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .

[9]  Larry S. Davis,et al.  Texture Analysis Using Generalized Co-Occurrence Matrices , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Lei Tian,et al.  Real-time weed detection in outdoor field conditions , 1999, Other Conferences.

[12]  E. Franz,et al.  THE USE OF LOCAL SPECTRAL PROPERTIES OF LEAVES AS AN AID FOR IDENTIFYING WEED SEEDLINGS IN DIGITAL IMAGES , 1990 .

[13]  Jacob Beck,et al.  Spatial frequency channels and perceptual grouping in texture segregation , 1987, Comput. Vis. Graph. Image Process..

[14]  M. F. Kocher,et al.  Textural imaging and discriminant analysis for distinguishing weeds for spot spraying , 1998 .

[15]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[16]  Alfredo Restrepo,et al.  Localized measurement of emergent image frequencies by Gabor wavelets , 1992, IEEE Trans. Inf. Theory.

[17]  George E. Meyer,et al.  Shape features for identifying young weeds using image analysis , 1994 .

[18]  Michael Colclough.  The Ideas of Sampling. , 1984 .

[19]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  S. Shearer,et al.  PLANT IDENTIFICATION USING COLOR CO-OCCURRENCE MATRICES , 1990 .

[21]  Alex Martin,et al.  A simulation of herbicide use based on weed spatial distribution , 1995 .

[22]  Naoki Sakai,et al.  Identification of Idealized Leaf Types Using Simple Dimensionless Shape Factors by Image Analysis , 1996 .

[23]  Gaines E. Miles,et al.  MACHINE VISION AND IMAGE PROCESSING FOR PLANT IDENTIFICATION. , 1986 .

[24]  Stéphane Mallat,et al.  Wavelets for a vision , 1996, Proc. IEEE.