Detection of Chemically Ripened Fruits Based on Visual Features and Non-destructive Sensor Techniques

  • N. R. MeghanaEmail author
  • R. Roopalakshmi
  • T. E. Nischitha
  • Prajwal Kumar
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Nowadays great concern for everyone is health; hence primary requirement for sound health is eating good quality fruits. However, most of the available fruits in the market are ripened using hazardous chemicals such as calcium carbide, which is highly hazardous to human health. In the existing literature, less focus is given towards addressing the problem of identification of artificially as well as naturally ripened fruits, due to the complex nature of problem. In order to solve this problem, a new framework is proposed in this paper, which utilizes both the image features- and sensor-based techniques to identify whether the fruit is ripened by chemicals or not. By employing pH-sensor based techniques and visual features, it is possible to detect artificially ripened fruits and save the human beings from serious health hazards. The experiments were conducted and the results indicate that the proposed technique is performing better for the identification of artificially ripened banana fruits.


VM K-means pH-sensor 


  1. 1.
    Nelson M, Haber ES (1928) The Vitamin A, B, and C Content of artificially versus naturally ripened tomatoes. J Biol Chem 2:3Google Scholar
  2. 2.
    Ahmad S, Thompson AK (2001) Effect of temperature on the ripening behavior and quality of banana fruit. Int J Agric Biol 3:2Google Scholar
  3. 3.
    García-Ramos FJ, Valero C (2005) Non-destructive fruit firmness sensors. J Agric Res 3:61–73Google Scholar
  4. 4.
    de Mora K, Joshi N (2011) A pH-based biosensor for detection of arsenic in drinkingwater. In: Proceeding Springer, pp 1031–1039Google Scholar
  5. 5.
    Dhembare AJ (2013) Bitter truth about fruit with reference to artificial ripener. Proc Arch Appl Sci Res 5:45–54Google Scholar
  6. 6.
    Zhu A, Yang L (2013) An improved FCM algorithm for ripe fruit image segmentation. In: Proceeding of IEEE international conference on information and automation Yinchuan, pp 436–441Google Scholar
  7. 7.
    Bhosale AA, Sundaram KK (2015) Nondestructive method for ripening prediction of papaya. Proc Int Conf Interdisciplinarity Eng 19:623–630Google Scholar
  8. 8.
    Ray PP, Pradhan S, Sharma RK (2016) loT based fruit quality measurement system. In: Proceeding of IEEE international conference on green engineering and technologies, pp 224–229Google Scholar
  9. 9.
    Karthika R, Ragadevi KVM (2017) Detection of artificially ripened fruits using image processing. Int J Adv Sci Eng Res 2(1):20–34Google Scholar
  10. 10.
    Ansari Sheeba (2017) An overview on thermal image processing. Proc Second Int Conf Res Intell Comput Eng 10:117–120CrossRefGoogle Scholar
  11. 11.
    Sahu D, Potdar RM (2017) Defect identification and maturity detection of mango fruits using image analysis. Am J Artif Intell 1(1):5–14Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. R. Meghana
    • 1
    Email author
  • R. Roopalakshmi
    • 1
  • T. E. Nischitha
    • 1
  • Prajwal Kumar
    • 1
  1. 1.Alvas Institute of Engineering and TechnologyMangaluruIndia

Personalised recommendations