Identification of weed/corn using BP network based on wavelet features and fractal dimension

The aim of this study was to investigate weed/corn Back-propagation (BP) network discrimination method based on wavelet feature parameters and fractal dimension of young weed/corn image. In-filed images were taken under natural sunlight and various backgrounds, and five common weed species located corns fields were considered in this research. The obtained images were converted into gray level images on a black background by a color index (ExG – ExR). Energy values were calculated from wavelet coefficients by using two-level wavelet decomposed gray level images. Then the obtained seven energy parameters were used as input vector to construct BP network classifier. The results showed that monocotyledon and dicotyledon could be totally separated with 100% accuracy, whereas weed/corn could not be effectively separated. To improve identification accuracy, the fractal dimension of weed/corn image was added to the original input vector. The results of this experiment demonstrated that BP network classifier associated with seven wavelet energy parameters provided 77.14% recognition rate (correctly identify weeds and corns), whereas BP network classifier associated determined by wavelet energy parameters and fractal dimension achieved a better recognition rate 94.28%.   Key words: Corn seedling, weeds, wavelet transform, energy, fractal dimension, identify.

[1]  H. T. Søgaard,et al.  Determination of crop rows by image analysis without segmentation , 2003 .

[2]  Daniel Marçal de Queiroz,et al.  Fall Armyworm Damaged Maize Plant Identification using Digital Images , 2003 .

[3]  Bidyut Baran Chaudhuri,et al.  An efficient approach to estimate fractal dimension of textural images , 1992, Pattern Recognit..

[4]  J. A. Marchant,et al.  Tracking of row structure in three crops using image analysis , 1996 .

[5]  Dallas E. Peterson,et al.  Factors affecting color-based weed detection. , 2000 .

[6]  J. Hemming,et al.  PA—Precision Agriculture: Computer-Vision-based Weed Identification under Field Conditions using Controlled Lighting , 2001 .

[7]  Noel D.G. White,et al.  AE—Automation and Emerging Technologies: Specialist Neural Networks for Cereal Grain Classification , 2002 .

[8]  D. Jayas,et al.  Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images , 2008 .

[9]  John A. Marchant,et al.  Using a competition model to quantify the optimal trade‐off between machine vision capability and weed removal effectiveness , 2005 .

[10]  Asnor Juraiza Ishak,et al.  Original paper: Weed image classification using Gabor wavelet and gradient field distribution , 2009 .

[11]  Ismail Kavdir,et al.  Discrimination of sunflower, weed and soil by artificial neural networks , 2004 .

[12]  Alberto Tellaeche,et al.  A vision-based method for weeds identification through the Bayesian decision theory , 2008, Pattern Recognit..

[13]  Noel D.G. White,et al.  Comparison of a Neural Network and a Non-parametric Classifier for Grain Kernel Identification , 2003 .

[14]  Luc Van Gool,et al.  Multi-spectral vision system for weed detection , 2001, Pattern Recognit. Lett..

[15]  Gilles Rabatel,et al.  AE—Automation and Emerging Technologies: Weed Leaf Image Segmentation by Deformable Templates , 2001 .

[16]  H. J. Olsen.  Determination of row position in small-grain crops by analysis of video images , 1995 .

[17]  T. F. Burks,et al.  Evaluation of Neural-network Classifiers for Weed Species Discrimination , 2005 .

[18]  Ning Wang,et al.  A real-time, embedded, weed-detection system for use in wheat fields , 2007 .

[19]  Odemir Martinez Bruno,et al.  Fractal dimension applied to plant identification , 2008, Inf. Sci..

[20]  A. J. S. McDonald,et al.  Weed and crop discrimination using image analysis and artificial intelligence methods , 2003 .

[21]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[22]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

[23]  Francisca López-Granados,et al.  Discrimination of weed seedlings, wheat (Triticum aestivum) stubble and sunflower (Helianthus annuus) by near-infrared reflectance spectroscopy (NIRS) , 2003 .