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Real-Time Apple Color Grading Based on Genetic Neural Network

LI Qing zhong, ZHANG Man, WANG Mao hua (Power and Electronics Engineering College, China Agriculture University, Beijing 100083)  
Color is one of the most significant inspection criteria related to apple external quality. In this paper, a computer vision experimental system for apple color estimation was first discribed. The system included a single lane conveyer, an apple illumination chamber, and the hardware fot apple image acuquisition and procession. A method of using HSI color system and neural network techniques for apple color inspection was developed. A GA based training algorithm was introduced to find optimal structure or the number of hidden layer nodes and connection weghts of artificial neural network. The results of experiment show that the approach is effective fot real time color grading and is accurate. The vision system achieved over 90% accuracy in color classification for apples by representing features with hue histograms and applying artificial neural network. The executing time of microcomputer for grading of one apple is 150ms.
【Fund】: 高等学校博士学科点专项科研基金!(95 0 80 1)
【CateGory Index】: TP391.4
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