Author(s): Pooja Amudala

Abstract: Grape diseases are the main causes leading to a serious reduction in grapes. Thus, it is urgent to develop an automatic method of identification for diseases of the grape leaf. Deep learning techniques have recently achieved impressive successes in various computer vision problems, which inspire us to apply them to the identification of grape diseases. In this article, the convolution neural Networks (CNN) The proposed CNN architecture. The combination of multiple CNNs enables the proposed United Model to extract additional discriminatory features. The representative potential of United Model has thus been enhanced. The United Model were evaluated on the Plant Village dataset and compared to other state-of-the-art CNN models. Experimental results have shown that United Model achieves the best performance in the various evaluation metrics. The United Model achieves an average validation accuracy of 99.17 million quarter and a test accuracy of 98.57 per cent, which can act as a decision support tool to help improve decision-making.

DOI: 10.33545/27075923.2020.v1.i2a.11

Pages: 01-04 | Views: 1652 | Downloads: 1245

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