Portal de Periódicos da CAPES

Artigo Produção Nacional Revisado por pares

BIBTEX RIS

An experimental assessment of deep convolutional features for plant species recognition

2021; Elsevier BV; Volume: 65; Linguagem: Inglês

10.1016/j.ecoinf.2021.101411

1878-0512

Luciano Araújo Dourado-Filho, Rodrigo Tripodi Calumby,

Spectroscopy and Chemometric Analyses

The evolution of the Deep Convolutional Neural Networks (DCNN) has progressively increased their ability to transfer the weights learned with large generic datasets to tasks with smaller collections or more specific data. However, the adjustment of these networks for different domains usually demand a fine-tuning step for which data may not be abundant enough. That is the case of plant species recognition task, which also suffers from class imbalance. Moreover, there is still a large variety of classification effectiveness with the models trained with the features extracted with different networks. All these factors create a complex assessment scenario and demand costly experimental validation procedures. Hence, in the context of plant species recognition, this work performs a comparative study of multiple pre-trained DCNNs to extract deep features from images of multi-organ plant observations. Beyond it, Softmax and six variations of the Support Vector Machine (SVM) classifier were used for the assessment of the suitability of the evaluated DCNNs. The experimental validation demonstrates great effectiveness variances of different DCNNs for feature extraction and the importance of such an experimental assessment for classification accuracy maximization. Beyond it, our results also show that exploiting deep feature extraction and an SVM-based classification outperformed a traditional setting based on neural classifiers. In fact, considering a hyperparameter optimization, the top performing SVM configuration allowed 82% of Micro-F1 in contrast to 76% of the second best (Softmax). The experiments also highlight such behavior with an effectiveness evaluation which specially accounts for dataset imbalance, a usual scenario in plant species recognition.