Uroš ŠKRUBEJ, Črtomir ROZMAN and Denis STAJNKO

pp. 19-24

ABSTRACT

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 ImageJ, 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.).

Key words: image processing, artificial neural networks, seeds, tomato

Slovenian:

Natančnost določanja kalečih semen s pomočjo obdelave slik in nevronskih mrež

Članek opisuje sistem računalniškega vida, ki temelji na tehnikah obdelave slik in strojnega učenja, ki je bil izdelan za avtomatsko oceno stopnje kaljenja semen paradižnika. Celoten sistem je bil zgrajen s pomočjo odprtokodnih aplikacij ImageJ, Weka in njihovih javno dostopnih javanskih kod, ki smo jih povezali v lastno originalno razvito kodo. Po odkrivanju predmetov na RGB slikah, smo uporabili umetne nevronske mreže (ANN), ki so bile sposobne pravilno razvrstiti 95,44% nakaljenih semen paradižnika (Solanum lycopersicum L.).

Ključne besede: /

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