Applicability of computer vision in seed identification: Deep learning, random forest, and support vector machine classification algorithms

Abstract

The use of computer image analysis can assist the extraction of morphological information from seeds, potentially serving as a resource for solving taxonomic problems that require extensive training by specialists whose primary method of examination is visual identification. We propose to test the ability of deep learning, SVM and random forest algorithms to classify seeds from twelve species of aquatic plants as an alternative to traditional classification methods. A total of 150 seeds of the species were collected. The attributes of colour, shape, and texture were analysed through the machine learning algorithms of deep learning, random forest, and support vector machine (SVM). Computer vision proved to be efficient at classifying species using all three algorithms, with an accuracy rate for SVM of 97.91 %, random forest 97.08 % and deep learning 92.5 %. We believe that the method performed well in our experiment and improved seed classification accuracy. As a result, the algorithms SVM and random forest were found to be enough at aquatic plant seed recognition.