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Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification

School of Technology, Beijing Forestry University, Beijing 100083, China
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Author to whom correspondence should be addressed.
Information 2020, 11(2), 95; https://doi.org/10.3390/info11020095
Received: 27 December 2019 / Revised: 6 February 2020 / Accepted: 6 February 2020 / Published: 10 February 2020
(This article belongs to the Section Artificial Intelligence)
Diseases from Ginkgo biloba have brought great losses to medicine and the economy. Therefore, if the degree of disease can be automatically identified in Ginkgo biloba leaves, people will take appropriate measures to avoid losses in advance. Deep learning has made great achievements in plant disease identification and classification. For this paper, the convolution neural network model was used to classify the different degrees of ginkgo leaf disease. This study used the VGGNet-16 and Inception V3 models. After preprocessing and training 1322 original images under laboratory conditions and 2408 original images under field conditions, 98.44% accuracy was achieved under laboratory conditions and 92.19% under field conditions with the VGG model. The Inception V3 model achieved 92.3% accuracy under laboratory conditions and 93.2% under field conditions. Thus, the Inception V3 model structure was more suitable for field conditions. To our knowledge, there is very little research on the classification of different degrees of the same plant disease. The success of this study will have a significant impact on the prediction and early prevention of ginkgo leaf blight.
Keywords: Ginkgo leaf disease; classification; deep learning; VGG16; Inception V3 Ginkgo leaf disease; classification; deep learning; VGG16; Inception V3
MDPI and ACS Style

Li, K.; Lin, J.; Liu, J.; Zhao, Y. Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification. Information 2020, 11, 95.

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