Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks

Grape diseases are main factors causing serious grapes reduction. So it is urgent to develop an automatic identification method for grape leaf diseases. Deep learning techniques have recently achieved impressive successes in various computer vision problems, which inspires us to apply them to grape diseases identification task. In this paper, a united convolutional neural networks (CNNs) architecture based on an integrated method is proposed. The proposed CNNs architecture, i.e., UnitedModel is designed to distinguish leaves with common grape diseases i.e., black rot, esca and isariopsis leaf spot from healthy leaves. The combination of multiple CNNs enables the proposed UnitedModel to extract complementary discriminative features. Thus the representative ability of UnitedModel has been enhanced. The UnitedModel has been evaluated on the hold-out PlantVillage dataset and has been compared with several state-of-the-art CNN models. The experimental results have shown that UnitedModel achieves the best performance on various evaluation metrics. The UnitedModel achieves an average validation accuracy of 99.17% and a test accuracy of 98.57%, which can serve as a decision support tool to help farmers identify grape diseases.