In-field early disease recognition of potato late blight based on deep learning and proximal hyperspectral imaging

TL;DR

This paper presents a deep learning approach combining 2D and 3D CNNs with attention mechanisms for early detection of potato late blight using hyperspectral imaging, achieving high accuracy in field conditions.

Contribution

It introduces a novel deep learning architecture integrating 2D-CNN, 3D-CNN, and attention networks for hyperspectral image classification of potato late blight.

Findings

Achieved 73.9% accuracy on full spectral data.

Achieved 79.0% accuracy on selected spectral bands.

Demonstrated effective early disease detection in field conditions.

Abstract

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.