Acquirement of Melon Morphologies through High-Precision and Non-Destructive Vision Measurement


Article Preview

For the purpose of measuring the micro changes of morphological parameters of melon organ, this paper put forth a new algorithm based on mathematical morphology and spline interpolation to obtain the phenotype information of melon such as area and horizontal and vertical diameter and developed a high-resolution non-destructive and contactless measuring system based on vision processing to get the projection area of melon and its diameter. The algorithm is easy to carry out, and can get more ideal edge information than some traditional algorithms. It supplies theoretical basis for revealing the combined response relationship and temporal and spatial variation character between melon morphologies and key environmental factors.



Advanced Materials Research (Volumes 383-390)

Edited by:

Wu Fan






F. X. Lv et al., "Acquirement of Melon Morphologies through High-Precision and Non-Destructive Vision Measurement", Advanced Materials Research, Vols. 383-390, pp. 5292-5299, 2012

Online since:

November 2011




[1] Lin Kaiyan, Xu Lihong, Wu Junhu. Advances in the application of computer-vision to plant growth monitoring, [J]. Transactions of the Chinese Society of Agricultural Engineering, 2004, 20(2): 279-283. (in Chinese).

[2] Sun Hong, Sun Ming, Wang Yiming. Status and trend of research on non-destructive measurement of plant growth based on machine vision, [J]. Transactions of the Chinese Society for Agricultural Machinery, 2006, 37(10): 181~185. (in Chinese).

[3] Stajnko D, Lakota M, Hocevar M. Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging, [J]. Computers and Electronics in Agriculture, 2004, 42(1); 31-42.

DOI: 10.1016/s0168-1699(03)00086-3

[4] Zeng Q B, Liu C L, Miao Y B, et al. A machine vision system for continuous field measurement of grape fruit diameter, [C]. Proceedings of 2th International Symposium on Intelligent Information Technology Application. Shanghai. 2008; 1064-1068.

DOI: 10.1109/iita.2008.274

[5] Guo Feng, Cao Qixin Xie Guojun, Zhou Jinliang. OHTA Color Space Based Method for Fruit Contour Detection, [J]. Transactions of the Chinese Society for Agricultural Machinery, 119-122(2005). (in Chinese).

[6] Jiangsheng Gui. Algorithms for 2-D Fruit Shape Detection and Classification, [Ph D Thesis]. Hangzhou: Zhejiang University, 2007. 5(in Chinese).

[7] Math works. Image Types and Type Conversions [EB]. http: /www. mathworks. com.

[8] Yang Fan. Data Image Processing and Analysis [M]. Beijing: Beijing University of Aeronautics And Astro-nautics Press, 2007. 10.

[9] Gonzalez R C, Woods R E. Digital Image Processing [M]. New Jersey: Prentice Hall, (2002).

[10] Ton Y, Nilov N, Kopyt M. Phytomonitoring: The new information technology for improving crop production, [J]. Acta Horticulture, 2001, 562; 257-262.

DOI: 10.17660/actahortic.2001.562.29

In order to see related information, you need to Login.