• Liming Xu
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 294)


The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.


Genetic Algorithm Light Intensity Neural Network System Output Layer Node Tomato Maturity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. B K Miller, Michael J.Delwiche. A Colour Vision System for Peach Grading. Transactions of the ASAE, 1989,32(4):1484–1490Google Scholar
  2. Cao Qixin, Liu Chengliang, Yin Yuehong, et al.Colour Image Processing Based on Quality Feature Extraction of Tomato.Robt, 2001,23(7):652–656Google Scholar
  3. Choi K, Lee G, Han Y J, et al. Tomato maturity evaluation using colour image analysis. Transaction of the ASAE, 1995,38(1):171–176Google Scholar
  4. Mao Hanping, XU Gui li, LI Ping ping. Study on Application of Genetic Algorithm to Feature Selection of Leaves Image for Diagnosing Vegetable Disease of Nutrient Deficiency. Journal of Jiangsu University of Science and Technology, 2003,24(2):1–5Google Scholar
  5. Masaru TOKUDA, Tsuneo KAWAMURA. Development of Visual System for Watermelon Harvesting Robot(Part2). Journal of Japanese Society of Agricultural Machinery, 1997, 59(4):47–52Google Scholar
  6. Masateru NAGATA. Study on Product Quality Estimation based on image process. Study report, March, 2000Google Scholar
  7. Qiu W,Shearer S A. Maturity assessment of broccoli using the discrete Fourier Transform. Transactions of the ASAE, 1992,35(6):2057–2062Google Scholar
  8. Wang Qiang, Shao Huih. Genetic Evoloved Neural Network and its Application in Formaldehyde Process Modeling and Optimization. Journal of Shanghai Jiaotong University, 1996,30(4):143–150Google Scholar
  9. Wang Shumao, JiaoQunying, Ji Junjie. An Impulse Response Method of Nondestructive Inspection of the Ripeness of Watermelon. Transaction of the CSAE, 1999, 15(3):241–245Google Scholar
  10. Xu Liming, Zhang Tiezhong. Influence of light intensity on extracted colour feature of different mature strawberry. New Zealand Journal of Agricultural Research. 2007, 50:559–565CrossRefGoogle Scholar
  11. Xu Zhenggang. Investigation of Non-destructive Citrus Maturity Determining Method Based on Image Information. Unpublished Master thesis. Zhe Jiang University, June, 2001Google Scholar
  12. Yang Xiukun, Chen Xiaoguang, Ma Chenglin, et al. Study on Automated Colour Inspection of Apples Using Genetic Neural Network. Transaction of the CSAE, 1997, 40:173–176Google Scholar
  13. Ye Qizheng, Yao Honglin, Li LI, et al. a Method for Measure Maturity According to the Feature Frequency of Resistance of Post-Harvest Fruit. Plant Physiology Communications. 1999, 35(4):304–307Google Scholar
  14. Yoshitaka MOTONAGA, Takaharu KAMEOKA, Atsushi HASHIMOTO. Constructing Colour Image Processing System for Managing the Surface Colour of Agricultural Products. Journal of Japanese Society of Agricultural Machinery, 1997, 59(3):13–21Google Scholar
  15. Zhang Changli, Fang Junlong, Fan Wei. Automated Identification of Tomato Maturation Using Multilayer Feedforward Nural Network with Genetic Alorithms(GA). Transaction of the CSAE, 2001, 17(3):153–156Google Scholar
  16. Zhao Jie wen; Zou Xiao-bo; Pan Yin-fei; Liu Shao-peng. Research on method of apples odorant recognition based on GA-neural network. Journal of Jiangsu University of Science and Technology, 2004, 25(1):1–4MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.China Agricultural UniversityBeijingChina

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