Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Content-based image classification using a neural network
Pattern Recognition Letters
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Computers and Electronics in Agriculture
Grading Method of Leaf Spot Disease Based on Image Processing
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 06
Color image segmentation using adaptive mean shift and statistical model-based methods
Computers & Mathematics with Applications
Large-scale investigation of weed seed identification by machine vision
Computers and Electronics in Agriculture
A novel color detection method based on HSL color space for robotic soccer competition
Computers & Mathematics with Applications
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In this study, we present an application of neural networks and image processing techniques for detecting and classifying the quality of areca nuts. Defects with diseases or insects of areca nuts were segmented by a detection line (DL) method. Six geometric features (i.e., the principle axis length, the secondary axis length, axis number, area, perimeter and compactness of the areca nut image), 3 color features (i.e., the mean gray level of an areca nut image on the R, G, and B bands), and defects area were used in the classification procedure. A back-propagation neural network classifier was employed to sort the quality of areca nuts. The methodology presented herein effectively works for classifying areca nuts to an accuracy of 90.9%.