Plant Species Classification Using Evolving Ensemble and Siamese Networks

Image-based dried plant specimen identification poses a significant challenge due to the large number of possible classes and the extreme scarcity of labelled training samples. To tackle these limitations and mitigate classification biases, this research proposes a Particle Swarm Optimisation (PSO)-based weighted evolving ensemble model as well as a Siamese network for plant species classification. Specifically, we first diversify the base classifier pool by employing three networks, i.e. ResNet50, Xception, and VGG19, fine-tuned using the specimen samples. Besides the adoption of a mean average ensemble model, a weighted ensemble scheme with PSO-based optimal weighting factor generation is also utilised to integrate the outputs of the three base networks for tackling classification variances. In addition, to further tackle species classification with extremely imbalanced data, a Siamese network with ResNet50 as the backbone is utilised. Evaluated using a challenging FGVC6 data set with Melastomataceae images, the PSO-based weighted ensemble model is able to assign more influence to the best performing base networks for ensemble prediction and outperforms the traditional mean average ensemble method. Moreover, the Siamese network also obtains competitive performance for solving imbalanced specimen classification by performing comparing similarity scores between image embeddings.