Discriminating rapeseed varieties using computer vision and machine learning

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.