In-field classification of the asymptomatic biotrophic phase of potato late blight based on deep learning and proximal hyperspectral imaging
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Effective detection of potato late blight (PLB) is an essential aspect of potato cultivation. However, it is a challenge to detect late blight in asymptomatic biotrophic phase in fields with conventional imaging approaches because of the lack of visual symptoms in the canopy. Hyperspectral imaging can capture spectral signals from a wide range of wavelengths also outside the visual wavelengths. Here, we propose a deep learning classification architecture for hyperspectral images by combining 2D convolutional neural network (2D-CNN) and 3D-CNN with deep cooperative attention networks (PLB-2D-3D-A). First, 2D-CNN and 3D-CNN are used to extract rich spectral space features, and then the attention mechanism AttentionBlock and SE-ResNet are used to emphasize the salient features in the feature maps and increase the generalization ability of the model. The dataset is built with 15,360 images (64x64x204), cropped from 240 raw images captured in an experimental field with over 20 potato genotypes. The accuracy in the test dataset of 2000 images reached 0.739 in the full band and 0.790 in the specific bands (492 nm, 519 nm, 560 nm, 592 nm, 717 nm and 765 nm). This study shows an encouraging result for classification of the asymptomatic biotrophic phase of PLB disease with deep learning and proximal hyperspectral imaging.
Originalsprog | Engelsk |
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Artikelnummer | 107585 |
Tidsskrift | Computers and Electronics in Agriculture |
Vol/bind | 205 |
Antal sider | 14 |
ISSN | 0168-1699 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
We would like to thank Mathias Timmerman (Danespo) for his assistance. This research was founded by the Nordic Council of Ministers, Copenhagen, Denmark (PPP #6P2 and #6P3) and NordForsk, Norway (#84597). This work was partially supported by the European Union's Horizon 2020 (H2020) Marie Skłodowska-Curie Actions (grant agreement number 766048) and Lincoln Agri-Robotics as part of the Expanding Excellence in England (E3) Programme.
Funding Information:
We would like to thank Mathias Timmerman (Danespo) for his assistance. This research was founded by the Nordic Council of Ministers, Copenhagen, Denmark (PPP #6P2 and #6P3) and NordForsk, Norway (#84597). This work was partially supported by the European Union’s Horizon 2020 (H2020) Marie Skłodowska-Curie Actions (grant agreement number 766048) and Lincoln Agri-Robotics as part of the Expanding Excellence in England (E3) Programme.
Publisher Copyright:
© 2022 The Author(s)
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