Experimental evaluation of Data Augmentation heuristics for plant identification systems based on Deep Learning

• Luciano Araújo Dourado Filho UEFS http://orcid.org/0000-0002-0507-2201
• Rodrigo Tripodi Calumby UEFS http://orcid.org/0000-0001-8515-265X

DOI: https://doi.org/10.5753/sbiagro.2021.18384

Resumo

Data augmentation (DA) allows increasing datasets for training machine learning models that demands large amounts of data. In real-world applications in which data may not be abundant enough and data acquisition is not easy, DA enables increasing diversity and introducing model generalization. In this work we evaluate several DA techniques and combining approaches to extend image datasets used to train plant species recognition models. We experimentally validated Deep Convolutional Neural Networks (DCNN) with several datasets obtained from common augmentation techniques and combinations. The results allowed the identification of the Translate + Crop augmentation policy as the most effective within the scope of evaluation.

Palavras-chave: Data augmentation, Plant identification, Deep Learning

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