Eur.J.Hortic.Sci. 80 (2) 68-75 | DOI: 10.17660/eJHS.2015/80.2.4|
ISSN 1611-4426 print and 1611-4434 online | © ISHS 2015 | European Journal of Horticultural Science | Original article
Assessment of germination rate of the tomato seeds using image processing and machine learning
U. Škrubej, Č. Rozman and D. Stajnko
University of Maribor, Faculty of Agriculture and Life Sciences, Hoče, Slovenia
This paper describes a computer vision system, based on image processing and machine learning techniques, which was implemented for automatic assessment of germination rate the tomato seeds (Solanum lycopersicum L.). The entire system was built using the open source applications ImageJ, WEKA and their public Java classes and was linked by a specially developed code. No expensive commercial software was used. Several machine learning classification algorithms, Naive Bayes classifiers (NBC), k-nearest neighbours (k-NN), decision trees, support vector machines (SVM) and artificial neural networks (ANN) were implemented and directly compared on a sample of 700 seeds for the first time. The results indicated that the ANN (multilayer perceptron architecture) showed better performance in classification than other models. The automated system was able to correctly classify 95.44% of germinated tomato seeds in Petri dishes (90x98x18 mm).
image processing, machine learning, neural networks, seeds, tomato
Significance of this study
What is already known on this subject?
What are the new findings?
Seed tests are designed to evaluate seeds under controlled conditions and they are key indicator of seed’s field performance. Potential enhancement of seed testing methods can be achieved by applying visual techniques to the collection of sample data. The culmination of recent research in seed technology was the development of rapid automated tests for germination and vigour detection.
What is the expected impact on horticulture?
Our computer vision system based on open source software was successfully applied for classification between germinated and non-germinated seeds of tomato. The results indicated that the ANN (multilayer perceptr on architecture) showed better performance in classification than other models. The prototype system classified one sample of 400 germinated seeds in 13 s and outperformed trained analysts.
The purpose of our investigation was to replace time-consuming and labor-intensive human visual inspections and to improve the efficiency of the current seed testing process. Thus, we examined ways of automating tasks by means of image processing and machine learning, which can provide an alternative to already presented computer vision systems and manual counting in inspection of seed samples.
The system could be easily improved for testing the seed germination rate of other horticultural plants, so this methodology could be adopted by seed laboratories and finally extended to practical use in the nursery industry.
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Received: 28 April 2014 | Revised: 9 January 2015 | Accepted: 3 March 2015 | Published: 22 April 2015 | Available online: 22 April 2015