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19 November 2004 Variety recognition of rice seeds using image analysis and artificial neural network
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Abstract
The objective of this research is to develop algorithms to classify varieties of rice seeds based on external features. The rice seeds used for this study involved five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou3207. Images of rice seeds were acquired with a color machine vision system. Each image was processed to extract twenty-two quantitative features. The classification ability of all the features was evaluated for different varieties recognition. The shape difference between Jinyou402 and Shanyou10 is obvious. The classification of Jinyou402 and Shanyou10 achieved an accuracy of 100% when a single feature such as the length-width ratio was used. Jinyou402 and IIyou couldn't be classified very well using one or two features. Then a perceptron was created and achieved an accuracy of 100% for both of Jinyou402 and IIyou. The shape difference between Jinyou402 and Zhongyou207 is obscure with naked eyes. All features were analyzed with principal components analysis method. A two-layer back propagation network was created and trained using gradient descent with momentum and adaptive learning rate. Nr. of hidden nodes was tested and early stopping skill was used. The total error of the finally established net is 2% for the classification of Jinyou402 and Zhongyou207. At last, all the images of five varieties were recognized as five classes. Another feed-forward network was created and trained using conjugate gradient back-propagation with Polak-Ribiere updates. Samples were disordered to train the network. The network achieved an average accuracy of about 85% for the five varieties.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fang Cheng and Yibin Ying "Variety recognition of rice seeds using image analysis and artificial neural network", Proc. SPIE 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources, (19 November 2004); https://doi.org/10.1117/12.570075
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