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

TL;DR

This paper presents novel 3D CNN models for automated detection, spikelet estimation, and severity assessment of Fusarium head blight in wheat using multispectral 3D point cloud data, achieving high accuracy and strong correlations.

Contribution

It introduces new 3D CNN architectures for FHB detection and severity estimation from multispectral 3D point clouds, demonstrating superior performance and the importance of RGB data.

Findings

FHB detection accuracy reached 100% with 3D CNNs.

Best spikelet estimation models had MAE of 1.13 and 1.56.

Severity estimation MAE was 8.6, with high correlation to visual assessments.

Abstract

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.