Classifying pests in crop images using deep learning

  • Gabriel Sávio de Lima Mota UFV
  • Leandro H. F. P. Silva UFV
  • Larissa Ferreira Rodrigues Moreira UFV
  • João Fernando Mari UFV

Resumo


Pest control is essential for agricultural success, and rapid and accurate pest identification through computer vision and machine learning enables effective pest management. This paper proposes an approach to evaluate nine customizations of the IP102 dataset. Considering the extensive range of sub-datasets, a comparative analysis was conducted between different deep learning models, including ResNet and AlexNet Convolutional Neural Networks (CNNs), and Vision Transform (ViT). We carried out tests considering training from scratch and fine-tuning. Our experimental results demonstrate that ViT outperforms CNN models for the problem investigated and benefits significantly from data augmentation strategies. Our study provides valuable insights for efficient pest classification, paving the way for future research and advancements in precision agriculture.

Palavras-chave: computer vision, agriculture, pest classification, deep learning, data augmentation, cutmix

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Publicado
13/11/2023
MOTA, Gabriel Sávio de Lima; SILVA, Leandro H. F. P.; MOREIRA, Larissa Ferreira Rodrigues; MARI, João Fernando. Classifying pests in crop images using deep learning. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 42-47. DOI: https://doi.org/10.5753/wvc.2023.27530.