Current issue
Online First
Archive
Instructions for Authors
Guide for Authors
Peer Review Policy
Research Ethics Policy
Ghostwriting and Guest Authorship
Copyright
Open Access Policy
Plagiarism
About the Journal
Aim and Scope
Scientific Board
Publisher
Editorial Board
Indexing in Databases
Personal Data Protection
Repository Policy
Contact
ORIGINAL PAPER
Comparison of selected classification methods in automated oak seed sorting
P. Tylek
3
1
AGH University of Science and Technology, Cracow, Poland
2
Industrial Institute of Agricultural Engineering, Poznan, Poland
3
University of Agriculture in Cracow, Poland
Journal of Research and Applications in Agricultural Engineering 2017;62(1):31-33
KEYWORDS
ABSTRACT
In this paper the results of automated, vision based classification of oak seeds viability i.e. their ability to germinate are presented. In the first stage, using a photo of the seed cross-section, a set of feature vectors were determined. Then three classification methods were examined: k-nearest neighbours (k-NNs), artificial neural networks (ANNs) and support vector machines (SVMs). Finally, a 73.1% precision was obtained for kNN and a 64 bin histogram, 78.5% for ANN and a 4 bin histogram and 78.8% for SVM with a 64 bin histogram.
REFERENCES (10)
1.
Cortes C., Vapnik, V.: Support-Vector Networks, Machine Learning, 1995, Vol. 20, 273-297.
2.
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. Computers and Electronics in Agriculture, 2004, 44, 3, 189-202.
3.
Duda R.O., Hart P.E., Stork D.G.: Pattern Classification, 2nd edition. John Wiley & Sons, 2001.
4.
Dudek-Dyduch E., Tadeusiewicz R., Horzyk A., Neural Network Adaptation Process Effectiveness Dependent of Constant Training Data Availability, Elsevier, Neurocomputing, 2009, 72, 3138-3149.
5.
Jabłoński M, Tylek P., Walczyk J., Tadeusiewicz R., Piłat A.: Colour-Based Binary Discrimination of Scarified Quercus robur Acorns under Varying Illumination. Sensors, 2016, 16(8), 1319.
6.
Jain A.K., Duin R.P., Mao Jianchang W.: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22, 1, 4-37.
7.
Jayas D.S., Paliwal J., Visen N.S.: Review Paper (AE-Automation and Emerging Technologies): Multi-layer Neural Networks for Image Analysis of Agricultural Products. Journal of Agricultural Engineering Research, 2000, 77, 2, 119-128.
8.
Kubiak A., Mikrut Z.: Application of Neural Networks and Two Representations of Color Components for Recognition of Wheat Grains Infected by Fusarium Culmorum Fungi. Lecture Notes in Proc. of the 7th ICAISC, Zakopane, Poland, 2004, Rutkowski L. et al (eds), Springer.
9.
Shigeo A.: Support Vector Machines for Pattern Classification. Springer-Verlag London, 2010.
10.
Tadeusiewicz R., Chaki R., Chaki N.: Exploring Neural Networks with C#. CRC Press, 2015.
Share
RELATED ARTICLE
| eISSN: | 2719-423X |
| ISSN: | 1642-686X |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
