ǂThe ǂaccuracy of the germination rate of seeds based on image processing and artificial neural networks

Uroš Škrubej (Author), Črtomir Rozman (Author), Denis Stajnko (Author)

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 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.).

Keywords

image processing;artificial neural networks;seeds;tomato;

Data

Language:	English
Year of publishing:	2015
Typology:	1.01 - Original Scientific Article
Organization:	UM FKBV - Faculty of Agriculture
UDC:	004.9:631.547.1
COBISS:	4129068
ISSN:	1580-8432
Parent publication:	Agricultura
Views:	1028
Downloads:	342
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary title:	Natančnost določanja kalečih semen s pomočjo obdelave slik in nevronskih mrež
Secondary abstract:	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.).
Secondary keywords:	obdelava slik;umetne nevronske mreže;semena;paradižnik;
URN:	URN:NBN:SI
Type (COBISS):	Scientific work
Pages:	str. 19-24
Volume:	ǂVol. ǂ12
Issue:	ǂno. ǂ1/2
Chronology:	Dec. 2015
DOI:	10.1515/agricultura-2016-0003
ID:	10878853