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Grapevine Variety Identification Through Grapevine Leaf Images Acquired in Natural Environment | IEEE Conference Publication | IEEE Xplore

Grapevine Variety Identification Through Grapevine Leaf Images Acquired in Natural Environment


Abstract:

In this paper we present a Deep Learning-based methodology to automatically classify 12 of the most representative grape-varieties existing in the Douro Demarked region, ...Show More

Abstract:

In this paper we present a Deep Learning-based methodology to automatically classify 12 of the most representative grape-varieties existing in the Douro Demarked region, Portugal. The dataset used consisted of images of leaves at different stages of development, collected on their natural environment. The development of such methodologies becomes particularly important, in a scenario in which ampeleographers are disappearing, creating a gap in the task of inspection of grape varieties. Our approach was based on the transfer learning of the Xcepetion model, using Focal Loss, adaptive learning rate decay and SGD. The model obtained a F1 score of 0.93. To clearly understand the predictions of the model, and realize which regions of the image contributed the most to the classification, the LIME library was used. This way it was possible to identify the parts of the images that were considered for and against each prediction.
Date of Conference: 11-16 July 2021
Date Added to IEEE Xplore: 12 October 2021
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Conference Location: Brussels, Belgium

References

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