Identification of Morphologically Similar Seeds Using Multi-kernel Learning

Abstract

Use of digital image analysis for the identification of seeds has not been recognized as a validated method. Image analysis for seed identification has been previously studied, and good recognition rates have been achieved. However, the data sets used in these experiments either contain very few groups of non-verified specimens or little representation of intra-species variations. This study considered a data set containing seed specimens that were verified to represent the species and a typical population variation, as well as look-alike species that share the same morphological appearance, in particular, seeds from species in the same genus, which can be particularly difficult for even trained professionals to visually distinguish. With representative specimens, the image features and machine learning algorithms described herein can achieve a high recognition rate: >97%. Three different types of features from seed images: colour, shape, and texture were extracted, and a multi-kernel support vector machine was used as the classifier. We compared our features to the previous state-of-the-art features and the results showed that the features we selected performed better on our data set.