  Effective early detection of potato late blight (PLB) is an essential aspect
of potato cultivation. However, it is a challenge to detect late blight at an
early stage in fields with conventional imaging approaches because of the lack
of visual cues displayed at the canopy level. Hyperspectral imaging can,
capture spectral signals from a wide range of wavelengths also outside the
visual wavelengths. In this context, 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 (492nm, 519nm,
560nm, 592nm, 717nm and 765nm). This study shows an encouraging result for
early detection of PLB with deep learning and proximal hyperspectral imaging.
