Assessment of germination rate of the tomato seeds using image processing and machine learning

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).