An effective and friendly tool for seed image analysis
Andrea Loddo, Cecilia Di Ruberto, Alessandra Mendes Pacheco Guerra Vale, Mariano Ucchesu, Júlia Martins Soares, Gianluigi Bacchetta
Abstract
Image analysis is an essential field for several topics in the life sciences, such as biology or botany. In particular, the analysis of seeds (e.g. fossil research) can provide important information on their evolution, the history of agriculture, plant domestication, and diets knowledge in ancient times. This work presents software that performs image analysis for feature extraction and classification from images containing seeds through a novel and unique framework. In detail, we propose two plugins for ImageJ, one able to extract morphological, texture, and colour features from seed images, and another to classify seeds using the extracted features. The experimental results demonstrated the correctness and validity of both the extracted features and the classification predictions on two public seeds datasets, showing that combining the handcrafted features with the Random Forest classifier can reach outstanding performance on both datasets. The proposed tool is easily extendable to other fields of image analysis.