Abstract
Utilizing image processing technology, the researcher calculated the length of the long-short-axis, marked the location of it and calculated the 7 parameters, chroma, length, width and etc, 4 of which were chosen as the key characteristics of the input to build a neural network and identify the level of raisins through analysis of the external characteristics of raisins. The method was based on traditional characteristics detection, used by boundary tracking algorithm and the length of the new long-short-axis detection algorithm. The result of the experiment indicates that the calculating method and judging of the level of raisins are precise and accurate, with an average recognition rate of 92%. Therefore, the method has a great practical value, which can be applied to other agricultural products classification.
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Li, X., Yuan, J., Gu, T., Liu, X. (2009). Level Detection of Raisins Based on Image Analysis and Neural Network. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_36
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DOI: https://doi.org/10.1007/978-3-642-01216-7_36
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