Discriminating rapeseed varieties using computer vision and machine learning
Date
2015-03
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Pergamon-Elsevier
Abstract
Rapeseed is widely cultivated throughout the world for the production of animal feed, vegetable fat for human consumption, and biodiesel. Since the seeds are evaluated in many areas for sowing and oilseed processing, they must be identified quickly and accurately for selection of a correct variety. An affordable method based on computer vision and machine learning was proposed to classify the seven rapeseed varieties. Different types of feature sets, feature models, and machine learning classifiers were investigated to obtain the best predictive model for rapeseed classification. The training and test sets were used to tune the model parameters during the training epochs by varying the complexity of the predictive models with grid-search and K-fold cross validation. After obtaining optimized models for each level of complexity, a dedicated validation set was used to validate predictive models. The developed computer vision system provided an overall accuracy rate of 99.24% for the best predictive model in discriminating rapeseed variety.
Description
Keywords
Machine learning, Rapeseed, Variety discrimination, Color texture features, Mechanical-properties, Classification, Identification, Recognition, Computer science, Engineering, Operations research & management science, Artificial intelligence, Learning systems, Oilseeds, Computer vision system, Learning classifiers, Oil-seed processing, Overall accuracies, Predictive modeling, Predictive models, Rapeseed, Variety discriminations, Computer vision
Citation
Kurtulmuş, F. ve Ünal, H. (2015). "Discriminating rapeseed varieties using computer vision and machine learning". Expert Systems with Applications, 42(4), 1880-1891.