Sugar content and the valid acidity test of the citrus based on the fractal dimensions of hue

Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery

Volumn 41, Issue 3, 2010, Pages 143-148

  Cao, Leping ^a   Wen, Zhiyuan ^b   Shen, Luming ^b  

^a Hunan Biological and Electromechanical Polytechnic   (China)

^b HUNAN AGRICULTURAL UNIVERSITY   (China)

Author keywords

Citrus; Fractal dimensions; Hue; Machine vision; Non destructive test; Sugar degree; Valid acidity

Indexed keywords

BP NEURAL NETWORKS; CITRUS FRUIT; COMPUTER VISION SYSTEM; HSI SPACE; HUE; HUE VALUES; MACHINE VISION; NON-DESTRUCTIVE TEST; NONDESTRUCTIVE DETECTION; SUGAR CONTENT; TEST SAMPLES; TWO PARAMETER;

COMPUTER CRIME; COMPUTER VISION; INITIATORS (CHEMICAL); NEURAL NETWORKS; NONDESTRUCTIVE EXAMINATION; SUGAR (SUCROSE);

FRACTAL DIMENSION;

EID: 77951902765     PISSN: 10001298     EISSN: None     Source Type: Journal    
DOI: 10.3969/j.issn.1000-1298.2010.03.030     Document Type: Article

References (16)

1. Carlomagno G, Capozzo L, Attolico G. Non-destructive grading of peaches by near-infrared spectrometry[J]. Infrared Physics & Technology, 2004, 46(1-2): 23-29.
2. Butz P, Hofmann C, Tauscher B. Recent developments in noninvasive techniques for fresh fruit and vegetable internal quality analysis[J]. Journal of Food Science, 2005, 70 (9): 131-141
3. Zdunek A, Muravsky L, Frankevych L, et al. New nondestructive method based on spatial-temporal speckle correlation technique for evaluation of apples quality during shelf-life[J]. Int. Agrophysics, 2007, 21(3): 305-310
4. Artur Zdunek, Ludmyla Frankevych, Krystyna Konstankiewicz, et al. Comparison of puncture test, acoustic emission and spatial-temporal speckle correlation technique as methods for apple quality evaluation[J].Acta Agrophysica, 2008, 11(1): 303-315
5. Barcelon E G, Tojo S, Watanabe K. X-ray computed tomography for internal quality evaluation of peaches[J]. Journal of Agricultural Engineering Research, 1999, 73 (4): 323-330
6. Guo Enyou, Liu Muhua, Zhao Jiewen, et al. Nondestructive detection of sugar content on navel orange with hyperspectral imaging[J]. Transactions of the Chinese Society for Agricultural Machinery, 2008, 39(5): 91-93, 103. (in Chinese)
7. Sun Xudong, Zhang Hailiang, Ouyang Aiguo, et al. Selection of NIR characteristic wavelength bands for soluble solids content in Zanfeng mandarin fruit[J]. Transactions of the Chinese Society for Agricultural Machinery, 2009, 46(7): 129-132. (in Chinese)
8. Naoshi Kondo, Usman Ahmad, Mitsuji Monta, et al. Machine vision based quality evaluation of Iyokan orange fruit using neural networks[J]. Computers and Electronics in Agriculture, 2000, 29(1-2): 135-147
9. Mendoza F, Aguilera J M. Application of image analysis for classification of ripening bananas[J]. Journal of Food Science, 2004, 69 (9): E471-E477
10. Steinmetz V, Roger J M, Molto E, et al. Online fusion of colour camera and spectrophotometer for sugar content prediction of apples[J].Journal of Agricultural Engineering Research, 1999, 73( 2): 207-216.
11. Ying Yibin, Rao Xiuqin, Ma Junfu. Methodology for nondestructive inspection of citrus maturity with machine vision[J].Transactions of the CSAE, 2004, 20(2): 144-147. (in Chinese)
12. Ying Yibin, Xu Huirong, Xu Zhenggang. Non-destructive maturity evaluation of citrus by hue frequency sequence method[J].Journal of Biomathematcs, 2006, 21(2): 306-312. (in Chinese)
13. Cai Jianrong, Zhao Jiewen. Recognition of mature fruit in natural scene using computer vision[J]. Transactions of the Chinese Society for Ayricultural Machinery, 2005, 36 (2): 61-64. (in Chinese)
14. Ling Yun, Wang Yiming, Sun Ming, et al. Detection of chalky rice based on fractal dimension[J]. Transactions of the Chinese Society for Agricultural Machinery, 2005, 36 (7): 92-95, 91. (in Chinese)
15. Cao Leping, Wen Zhiyuan. Study on the error of the compensation fuzzy neural fruit grader[J].Transactions of the CSAE, 2008, 24 (12): 102-106. (in Chinese)
16. Cao Leping, Wen Zhiyuan, Chen Liyuan. Investigation of citrus fruits pH value measurement based on machine vision with wavelet neural network model[J]. Scientia Agricultura Sinica, 2008, 4(11): 3741-3745. (in Chinese)