The nature of statistical learning theory
The nature of statistical learning theory
Wavelet based multi-spectral image analysis of maize leaf chlorophyll content
Computers and Electronics in Agriculture
Biological feature isolation by wavelets in biospeckle laser images
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Application of distributed SVM architectures in classifying forest data cover types
Computers and Electronics in Agriculture
Automatic acoustic detection of the red palm weevil
Computers and Electronics in Agriculture
Real time computer stress monitoring of piglets using vocalization analysis
Computers and Electronics in Agriculture
Wavelet transform to discriminate between crop and weed in perspective agronomic images
Computers and Electronics in Agriculture
Application of support vector machine technology for weed and nitrogen stress detection in corn
Computers and Electronics in Agriculture
Original paper: Real-time analysis of chicken embryo sounds to monitor different incubation stages
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
The identification and filtering of fertilized eggs with a thermal imaging system
Computers and Electronics in Agriculture
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Recent studies on the detection of eggshell cracks have mainly focused on the Fourier frequency analysis of a vibration-based response. This paper proposes a new detection methodology for eggshell cracks using a continuous wavelet transform and a support vector machine (SVM) technique. The proposed methodology consists of an experimental system and a data processing system. The experimental system was used to generate the impact force and to measure the acoustic signal, whereas the data processing system extracted timely signal features and classified the signals based on an SVM algorithm that is presented herein. The wavelet transform was introduced with the objective of overcoming the drawbacks of the conventional Fourier analysis and of finding new crack-sensitive features based on the local time and frequency information of the signal. Several wavelet-based features were extracted through the analysis of the energy distribution of the wavelet transform coefficients for cracked and intact eggs. These features include the first resonance scale (FS), the s-coordinate (SC) and the corrected t-coordinate (TC) of the centroid for the time-scale domain, and the weighted standard deviation (WD). These features, as well as their various combinations, were used to construct the SVM classifiers. The classification effects of the best single-, two-, and three-feature SVM classifiers and the four-feature SVM classifier are provided. With a detection scheme based on four measurements for every egg, the experimental example achieved the highest crack detection rate of 98.9%, and the smallest false rejection rate of 0.8%.