Overlapped Sunflower Weighted Crop Yield Estimation Based on Edge Detection

  • Hemant RathoreEmail author
  • Vijay Kumar Sharma
  • Shubhra Chaturvedi
  • Kapil Dev Sharma
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)


Today agriculture field’s demands to develop such an intelligent system those provide accurate and timely information for an estimation of crop productivity. This paper designed an automated decision support system to estimate sunflower crop productivity information with interface between camera and computer software. The earlier steps of system generate overlapped flower yield information and latter steps count the seed from the flower head. Some beautiful flowers in the nature have Fibonacci relationship in their seeds pattern, i.e. sunflower, pineapple etc. The implementation parts based on two color model RGB and HSV. HSV provide better results for overlapped flower. The technique use image segmentation, morphological operation for overlapped flower count and edge detection for seed count.


Edge detection Morphological operation Segmentation Filters 


  1. 1.
    Chadha, K.L.: Horticulture: new avenues for growth. The Hindu Survey of Indian Agriculture, 155–160 (1999)Google Scholar
  2. 2.
    Wang, Q., Nuske, S., Bergerman, M., Singh, S.: Automated crop yield estimation for apple orchards. In: 13th International Symposium on Experimental Robotics (ISER 2012), pp. 1–15 (2012)Google Scholar
  3. 3.
    Stajnko, D., Rakun, J., Blanke, M.: Modelling apple fruit yield using image analysis for fruit colour, shape and texture. Europ. J. Hortic. Sci. 74, 260–267 (2009)Google Scholar
  4. 4.
    Bairwa, N., Agrawal, N., Gupta, S.: Development of counting algorithm for overlapped agricultural products. In proceeding of National Conference on Recent Advances in Wireless Communication and Artificial Intelligence (RAWCAI-2014) (2014)Google Scholar
  5. 5.
    Zhou, R., Damerow, L., Sun, Y., Blanke, M.: Using colour features of cv. ‘Gala’ apple fruits in an orchard in image processing to predict yield. Precis. Agric. 13, 568–580 (2012)CrossRefGoogle Scholar
  6. 6.
    Stajnko, D., Rozmana, Č., Pavloviča, M., Beber, M., Zadravec, P.: Modeling of ‘Gala’ apple fruits diameter for improving the accuracy of early yield prediction. Sci. Hortic. 160, 306–312 (2013)Google Scholar
  7. 7.
    Kelman, E., Linker, R.: Vision-based localisation of mature apples in trees images using convexity. Biosyst. Eng. 118, 174–185 (2014)CrossRefGoogle Scholar
  8. 8.
    Bairwa, N., Agrawal, K.N.: Counting algorithm for flowers using image processing. Int. J. Eng. Res. Technol. (IJERT) 3, 775–779 (2014)Google Scholar
  9. 9.
    Gil, J., Kimmel, R.: Efficient dilation, erosion, opening and closing algorithms. In: Goutsias, V.J., Vincent, L., Bloomberg, D. (eds.) Mathematical Morphology and its Applications to Image and Signal Processing, Palo-Alto, USA, June 2000, pp. 301–310. Kluwer Academic Publishers (2000)Google Scholar
  10. 10.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for image classification. IEEE Trans. Syst. Man Cybernatics 3(6), 610–621 (1973)CrossRefGoogle Scholar
  11. 11.
    Guru, D.S., Sharath, Y.H., Manjunath, S.: Texture features and KNN in classification of flower images. IJCA (2010). Special Issue on Recent Trends in Image Processing and Pattern Recognition RTIPPRGoogle Scholar
  12. 12.
    Patel, H.N., Patel, A.D.: Automatic segmentation and yield measurement of fruit using shape analysis. Int. J. Comput. Appl. 45(7) (2012). (0975 – 8887)Google Scholar
  13. 13.
    Aggelopoulou, A.D., Bochtis, D., Fountas, S., Swain, K.C., Gemtos, T.A., Nanos, G.D.: Yield prediction in apple orchards based on image processing. Precis. Agric. 12, 448–456 (2011)CrossRefGoogle Scholar
  14. 14.
    Sharma, V.K., Srivastava, D.K., Mathur, P.: Efficient image steganography using graph signal processing. IET Image Process. 12(6), 1065–1071 (2018)CrossRefGoogle Scholar
  15. 15.
    Kaur, S., Kaur, S.: Performance evaluation of different filters in image denoising for different noise. Int. J. Innov. Res. Sci. Eng. Technol. 5(7) (2016). ISSN (Online) 2319-8753, ISSN (Print) 2347-6710Google Scholar
  16. 16.
    Singh, P., Shree, R.: A comparative study to noise models and image restoration techniques. Int. J. Comput. Appl. 149(1) (2016). (0975 – 8887)Google Scholar
  17. 17.
    Saravanan, G., Yamuna, G., Nandhini, S.: Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models. In: International Conference on Communication and Signal Processing, India, 6–8 April 2016 (2016)Google Scholar
  18. 18.
    Nnolim, U.A.: Design and implementation of novel, fast, pipelined HSI2RGB and log-hybrid RGB2HSI color converter architectures for image enhancement. Microprocess. Microsyst. 39(4–5), 223–236 (2015)CrossRefGoogle Scholar
  19. 19.
    Nilsback, M.E., Zisserman, A.: Delving into the whorl of flower segmentation. In the Proceedings of British Machine Vision Conference, vol. 1, pp. 27–30 (2004)Google Scholar
  20. 20.
    Islam, S., Ahmed, M.: A study on edge detection techniques for natural image segmentation. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 2(3) (2013). ISSN 2278-3075Google Scholar
  21. 21.
    Wang, H.: Robust statistics for computer vision: model fitting, image segmentation and visual motion analysis, Ph.D thesis, Monash University, Australia (2004)Google Scholar
  22. 22.
    Yuen, H.K., Princen, J., Illingworth, J., Kittler, J.: A comparative study of hough transform methods for circle finding. Image Vis. Comput. 8, 71–77 (1990)CrossRefGoogle Scholar
  23. 23.
    Ahmad, S.: Environmental effects on seed characteristics of sunflower. J. Agron. Crop Sci. 187(3), 213–216 (2001)CrossRefGoogle Scholar
  24. 24.
    StatCrunch: Data Analysis on Web. College of Western Idaho, Pearson Education 2017–2018Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hemant Rathore
    • 1
    Email author
  • Vijay Kumar Sharma
    • 1
  • Shubhra Chaturvedi
    • 2
  • Kapil Dev Sharma
    • 1
  1. 1.Department of Computer Science and EngineeringRajasthan Institute of Engineering and TechnologyJaipurIndia
  2. 2.Department of BotanyGovernment College MalpuraTonkIndia

Personalised recommendations