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Fusarium head blight (FHB) is one of the most significant diseases affectingwheat and other small grain cereals worldwide. The development of resistantvarieties requires the laborious task of field and greenhouse phenotyping. Theapplications considered in this work are the automated detection of FHB diseasesymptoms expressed on a wheat plant, the automated estimation of the totalnumber 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 dataused 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 imageswere collected using a multispectral 3D scanner, and the labelled UW-MRDC 3Dwheat dataset was created. The data was used to develop novel and efficient 3Dconvolutional neural network (CNN) models for FHB detection, which achieved100% accuracy. The influence of the multispectral information on performancewas evaluated, and our results showed the dominance of the RGB channels overboth the NIR and the NIR plus RGB channels combined. Furthermore, novel andefficient 3D CNNs were created to estimate the total number of spikelets andthe total number of infected spikelets on a wheat head, and our best modelsachieved mean absolute errors (MAE) of 1.13 and 1.56, respectively. Moreover,3D CNN models for FHB severity estimation were created, and our best modelachieved 8.6 MAE. A linear regression analysis between the visual FHB severityassessment and the FHB severity predicted by our 3D CNN was performed, and theresults showed a significant correlation between the two variables with a0.0001 P-value and 0.94 R-squared.

Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3D convolutional neural networks