Fusarium head blight (FHB) is one of the most significant diseases affecting
wheat and other small grain cereals worldwide. The development of resistant
varieties requires the laborious task of field and greenhouse phenotyping. The
applications considered in this work are the automated detection of FHB disease
symptoms expressed on a wheat plant, the automated estimation of the total
number of spikelets and the total number of infected spikelets on a wheat head,
and the automated assessment of the FHB severity in infected wheat. The data
used to generate the results are 3-dimensional (3D) multispectral point clouds
(PC), which are 3D collections of points - each associated with a red, green,
blue (RGB), and near-infrared (NIR) measurement. Over 300 wheat plant images
were collected using a multispectral 3D scanner, and the labelled UW-MRDC 3D
wheat dataset was created. The data was used to develop novel and efficient 3D
convolutional neural network (CNN) models for FHB detection, which achieved
100% accuracy. The influence of the multispectral information on performance
was evaluated, and our results showed the dominance of the RGB channels over
both the NIR and the NIR plus RGB channels combined. Furthermore, novel and
efficient 3D CNNs were created to estimate the total number of spikelets and
the total number of infected spikelets on a wheat head, and our best models
achieved mean absolute errors (MAE) of 1.13 and 1.56, respectively. Moreover,
3D CNN models for FHB severity estimation were created, and our best model
achieved 8.6 MAE. A linear regression analysis between the visual FHB severity
assessment and the FHB severity predicted by our 3D CNN was performed, and the
results showed a significant correlation between the two variables with a
0.0001 P-value and 0.94 R-squared