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Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network

Author

Listed:
  • Iman Golpour

    (Department of Agricultural Machinery Engineering, Faculty of Agriculture. Bu-Ali Sina University. Hamedan, Iran)

  • Jafar Amiri Parian

    (Department of Agricultural Machinery Engineering, Faculty of Agriculture. Bu-Ali Sina University. Hamedan, Iran)

  • Reza Amiri Chayjan

    (Department of Agricultural Machinery Engineering, Faculty of Agriculture. Bu-Ali Sina University. Hamedan, Iran)

Abstract

We identify five rice cultivars by mean of developing an image processing algorithm. After preprocessing operations, 36 colour features in RGB, HSI, HSV spaces were extracted from the images. These 36 colour features were used as inputs in back propagation neural network. The feature selection operations were performed using STEPDISC analysis method. The mean classification accuracy with 36 features for paddy, brown and white rice cultivars acquired 93.3, 98.8, and 100%, respectively. After the feature selection to classify paddy cultivars, 13 features were selected for this study. The highest mean classification accuracy (96.66%) was achieved with 13 features. With brown and white rice, 20 and 25 features acquired the highest mean classification accuracy (100%, for both of them). The optimised neural networks with two hidden layers and 36-6-5-5, 36-9-6-5, 36-6-6-5 topologies were obtained for the classification of paddy, brown, and white rice cultivars, respectively. These structures of neural network had the highest mean classification accuracy for bulk paddy, brown and white rice identification (98.8, 100, and 100%, respectively).

Suggested Citation

  • Iman Golpour & Jafar Amiri Parian & Reza Amiri Chayjan, 2014. "Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network," Czech Journal of Food Sciences, Czech Academy of Agricultural Sciences, vol. 32(3), pages 280-287.
    • Handle: RePEc:caa:jnlcjf:v:32:y:2014:i:3:id:238-2013-cjfs
    DOI: 10.17221/238/2013-CJFS
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    Cited by:

    1. Benxue Ma & Cong Li & Yujie Li & Wenxia Wang & Guowei Yu & Wancheng Dong & Yuanjia Zhang, 2022. "Moisture content assessment of dried Hami jujube using image colour analysis," Czech Journal of Food Sciences, Czech Academy of Agricultural Sciences, vol. 40(1), pages 33-41.

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