Identification of Fusarium Head Blight in Winter Wheat Ears Based on Fisher’s Linear Discriminant Analysis and a Support Vector Machine

Linsheng Huang,Wenjiang Huang,Jinling Zhao,Zhaochuan Wu,Huiqin Ma

doi:10.3390/app9183894

Abstract

Fusarium head blight (FHB) is one of the diseases caused by fungal infection of winter wheat (Triticum aestivum), which is an important cause of wheat yield loss. It produces the deoxynivalenol (DON) toxin, which is harmful to human and animal health. In this paper, a total of 89 samples were collected from FHB endemic areas. The occurrence of FHB is completely natural in experimental fields. Non-imaging hyperspectral data were first processed by spectral standardization. Spectral features of the first-order derivatives, the spectral absorption features of the continuum removal, and vegetation indices were used to evaluate the ability to identify FHB. Then, the spectral feature sets, which were sensitive to FHB and have significant differences between classes, were extracted from the front, side, and erect angles of winter wheat ear, respectively. Finally, Fisher’s linear discriminant analysis (FLDA) for dimensionality reduction and a support vector machine (SVM) based on radical basis function (RBF) were used to construct an effective identification model for disease severity at front, side, and erect angles. Among selected features, the first-order derivative features of SDg/SDb and (SDg-SDb)/(SDg+SDb) were most dominant in the model produced for the three angles. The results show that: (1) the selected spectral features have great potential in detecting ears infected with FHB; (2) the accuracy of the FLDA model for the side, front, and erect angles was 77.1%, 85.7%, and 62.9%. The overall accuracy of the SVM (80.0%, 82.9%, 65.7%) was slightly better than FLDA, but the effect was not obvious; (3) LDA combined with SVM can effectively improve the overall accuracy, user’s accuracy, and producer’s accuracy of the model for the three angles. The over accuracy of the side (88.6%) was better than the front (85.7%), while the over accuracy of the erect angle was the lowest (68.6%).

Highlights

IntroductionWheat (Triticum aestivum) is one of the world’s three major cereals, and its disease problems (yellow rust, powdery mildew, Fusarium head blight) have received widespread attention in China and abroad
Wheat (Triticum aestivum) is one of the world’s three major cereals, and its disease problems have received widespread attention in China and abroad
The results indicate the strong potential of near-infrared hyperspectral imaging in estimating Fusarium damage [14]

Summary

Introduction

Wheat (Triticum aestivum) is one of the world’s three major cereals, and its disease problems (yellow rust, powdery mildew, Fusarium head blight) have received widespread attention in China and abroad. Due to factors such as changes in climate and cultivation methods, the scope and severity of wheat Fusarium head blight (FHB) have been expanding and increasing year by year [1], meaning field monitoring of FHB is valued by scholars. The ear disease rate is 50% to 100%, which can reduce production by 10% to 40%. The diseased rate is 30% to 50%, which can reduce production by 5% to 15% [2]. FHB of wheat causes a significant drop in food production, but the DON produced by pathogens hurts human and animal health, causing food safety problems [6,7]. It is important to monitor the health condition of wheat in the field pre-harvest, and to identify the diseased ears

Methods

Results

Conclusion