The accuracy of the germination rate of seeds based on image processing and artificial neural networks

Open access


This paper describes a computer vision system based on image processing and machine learning techniques which was implemented for automatic assessment of the tomato seed germination rate. The entire system was built using open source applications Image J, Weka and their public Java classes and linked by our specially developed code. After object detection, we applied artificial neural networks (ANN), which was able to correctly classify 95.44% of germinated seeds of tomato (Solanum lycopersicum L.).

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Information Retrieval. Addison-Wesley Longman Publishing Co. Boston 1999: 544 p.

  • 2. Dell’Aquila A van Eck JW van der Heidjen GWAM. -The application of image analysis in monitoring the imbibition process of white cabbage (Brassica oleracea L.) seeds. Seed Sci. Res. 2000;10:163-169.

  • 3. Ducournau S Feutry A Plainchault P Revollon P Vigouroux B Wagner MH. An image acquisition system for automated monitoring of the germination rate of sunflower seeds. Comp. Electron. Agricult. 2004;44:189-202.

  • 4. Geneve RL Kester ST. Evaluation of seedling size following germination using computer-aided analysis of digital images from that-bed scanner. Hortscience 2001;36(6):1117-1120.

  • 5. Granitto PM Navone HD VerdesPF Ceccato HA. Weed seeds identi­cation by machine vision. Comp. Electron. Agricult. 2002;33:91-103.

  • 6. Howarth MS Stanwood PC. Extracting 3-D information using 2-D images of seeds. Comp. Electron. Agricult. 1994;10:175-188.

  • 7. Jossen R Kodde V Willems L Ligterink W Van der Plas L Hilhorst H. Germinator: a software package for highthroughput scoring and curve ­tting of Arabidopsis seed germination. Plant J. 2010; 62(1):148-159.

  • 8. Mayo M. Watson AT. Automatic species identi­cation of live moths. Knowledge-Based Systems 2007;20:195-202.

  • 9. McDonald MB. Seed quality assessment. Seed Sci. Res. 1998;8:265-275.

  • 10. Rasband WS. Image. US National Institutes of Health Bethesda Maryland USA 2008.

  • 11. Accessed August 2012.

  • 12. Uchigasaki M Serata K Miyamoto S. An automated machine vision system for classification of seeds using color features. J. Agricult. Struct. 2000;30(4):325-332.

  • 13. Ureña R Rodriguez F in Berenguel M. A machine vision system for seeds germination quality evaluation using fuzzy logic. Comp. Electron. Agricult. 200132:1-20.

  • 14. Witten I. and Frank E. (2005).Data Mining: Practical Machine Learning Toolsand Techniques (2nd Ed.). Morgan Kaufmann

Journal information
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 240 148 12
PDF Downloads 154 109 9