Detection of fusarium head blight in wheat using hyperspectral data and deep learning

Early diagnosis of fusarium head blight (FHB) presence and intensity in wheat can assist decision support for reducing disease spread and minimizing mycotoxin contamination in the grain. Although hyperspectral data was used successfully for the detection of FHB, using the traditional machine learning methods, these rely on time-consuming manual feature extraction, requiring expert skills. This study explores the use of deep learning models which can automatically extract FHB features. Images were generated from single lines hyperspectral (400-750 nm) data collected from wheat canopy in the laboratory and fed to a convolutional neural network (CNN) for pixel classification into two classes of healthy or FHB infected pixels. Four different types of image conversion schemes were explored, which resulted in a spectral (line & bar) graph, compressed spectral line graph and 2D generated band image. Eight different pre-trained lightweight CNN models that require limited computing re-sources were utilized. The preliminary analysis showed that DarkNet 19 model using the spectral line graph images from both smoothed and unsmoothed data resulted in the best accuracy and F1 score of 100 % with a prediction score of 1 for the sample test dataset. The application of feature visualization using an occlusion sensitivity map was able to elucidate the spectral features responsible for the high accuracy for classification. The results suggest the robustness of the developed method for recognition of pixels corresponding to the FHB infected and healthy ears under the laboratory conditions that motivate for potential development with field data.

Automated summary

This study explores the use of deep learning models for automatically extracting features of fusarium head blight (FHB) in wheat. Images generated from hyperspectral data were classified using a convolutional neural network, resulting in high accuracy and F1 scores when using specific image conversion schemes and appropriate pre-trained models.