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.

摘要
计算机图像分析可辅助提取种子的形态学信息，有望为解决分类学难题提供新途径——此类难题往往需要以视觉鉴定为主要检查手段的专家开展大量专业训练。本研究拟测试深度学习、支持向量机（SVM）以及随机森林三种算法对12种水生植物种子的分类能力，以此作为传统分类方法的替代方案。本研究共收集了该12种水生植物的150粒种子。研究人员通过深度学习、随机森林与支持向量机三种机器学习算法，对种子的颜色、形状及纹理特征进行了分析。实验结果表明，采用三种算法开展物种分类时，计算机视觉技术均展现出良好的有效性：支持向量机分类准确率达97.91%，随机森林为97.08%，深度学习则为92.5%。本研究认为，该方法在本次实验中表现优异，有效提升了种子分类的准确率。综上，支持向量机与随机森林算法足以胜任水生植物种子的识别任务。