Maize leaf disease classification using convolutional neural networks and hyperparameter optimization

  • Erik Lucas da Rocha UFV
  • Larissa Rodrigues UFV
  • João Fernando Mari UFV


Maize is an important food crop in the world, but several diseases affect the quality and quantity of agricultural production. Identifying these diseases is a very subjective and time-consuming task. The use of computer vision techniques allows automatizing this task and is essential in agricultural applications. In this study, we assess the performance of three state-of-the-art convolutional neural network architectures to classify maize leaf diseases. We apply enhancement methods such as Bayesian hyperparameter optimization, data augmentation, and fine-tuning strategies. We evaluate these CNNs on the maize leaf images from PlantVillage dataset, and all experiments were validated using a five-fold cross-validation procedure over the training and test sets. Our findings include the correlation between the maize leaf classes and the impact of data augmentation in pre-trained models. The results show that maize leaf disease classification reached 97% of accuracy for all CNNs models evaluated. Also, our approach provides new perspectives for the identification of leaf diseases based on computer vision strategies.

Palavras-chave: Convolutional neural networks, maize leaf, classification, data augmentation, hyperparameter, Bayesian optimization


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DA ROCHA, Erik Lucas; RODRIGUES, Larissa; MARI, João Fernando. Maize leaf disease classification using convolutional neural networks and hyperparameter optimization. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 104-110. DOI: