1. 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. In recent years, 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 also valued by scholars. Wheat FHB is a typical climatic disease, occurring in rainy and moist climate areas with a characteristic epidemic period [
2]. Frequent rainfall and high humidity coinciding with flowering and early kernel-fill periods of wheat favor infection and development of the FHB disease [
3]. In a pandemic year, the ear disease rate is 50% to 100%, which can reduce production by 10% to 40%. In an epidemic year, the diseased rate is 30% to 50%, which can reduce production by 5% to 15% [
2]. The main pathogen of wheat FHB is Fusarium graminearum, which accounts for 94.5% of the pathogens in wheat FHB in China. After the infection of wheat, the pathogen can produce a variety of fungal toxins, among which the most toxic one is deoxynivalenol (DON) [
2]. DON, commonly known as vomitoxin, has acute adverse effects in animals, including food refusal, diarrhea, emesis, alimentary hemorrhaging, and contact dermatitis [
4,
5]. FHB of wheat not only causes a significant drop in food production, but the DON produced by pathogens also hurts human and animal health, causing food safety problems [
6,
7]. Therefore, it is important to monitor the health condition of wheat in the field pre-harvest, and to identify the diseased ears.
FHB is caused by fungal infection, which affects the normal physiological function of wheat and changes the external morphology and internal physiological structure [
8,
9,
10]. The primary inoculum for FHB comes from infected plant debris, on which the fungus overwinters as saprophytic mycelia. The wheat ear tissue collapses after being seriously infested, the cells disintegrate, and the external appearance of browning becomes obvious [
11]. The infected kernels appear shriveled, lightweight, chalky, and covered with mycelia [
7,
12].
At present, hyperspectral remote sensing technology is often used in wheat disease and pest monitoring research. In the research on remote sensing monitoring and prediction methods for wheat diseases, previous studies have focused on the yellow rust and powdery mildew. However, compared with yellow rust and powdery mildew, there are fewer studies on FHB using hyperspectral techniques. Liang et al. (2015) used hyperspectral imaging techniques to identify the FHB of wheat kernels by spectral analysis and image processing. The constructed linear discriminant analysis SVM and back propagation (BP) neural network models have good success in identifying FHB-infected kernels, with accuracy rates above 90% [
13]. Ewa et al. (2018) constructed a classification model based on texture parameters of hyperspectral images to identify the infected kernels, and kernels positioned on the ventral side were classified with 100% accuracy [
6]. Delwiche et al. (2019) used hyperspectral imaging on individual kernels and linear discriminant analysis models to differentiate between healthy and Fusarium-damaged kernels based on the mean reflectance values of the interior pixels of each kernel at four wavelengths (1100, 1197, 1308, and 1394 nm). The results indicate the strong potential of near-infrared hyperspectral imaging in estimating Fusarium damage [
14]. The previous studies mainly focused on classification of diseased kernels using hyperspectral imaging or identification of diseased ears under laboratory conditions, and therefore, cannot be applied to FHB identification under field conditions. There is limited literature available in the field environment [
15]. The literature that is available is incomplete—it relates only to identification of the disease, and either the ideal identification effect is not achieved or the disease severity is not classified. Bauriegel et al. (2011) used hyperspectral imagery and the derived head blight index (HBI), which uses spectral differences in the ranges of 665–675 nm and 550–560 nm, as a suitable outdoor classification method for the identification of head blight; the mean hit rates were 67% during the study period [
16]. Jin et al. (2018) classified wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in wild fields, with the classification accuracy reaching 74.3% [
17]. Whetton et al. (2018) implemented a hyperspectral line imager for online measurement of FHB wheat in the field, and RGB photos collected from the ground truth plots were used to assess crop disease incidence (the number of individual infected ears in relation to the healthy individuals). The study achieved good accuracy (82%) but did not identify the severity of the disease [
15]. This study will make up for the shortcomings of previous studies, using non-imaging hyperspectral techniques to target individual ears to achieve identification and classification of the severity of ear disease.
The algorithm of Fisher’s linear discriminant analysis (FLDA) performs dimensionality reduction on the original high-dimensional eigenvectors. By linearly combining each dimension of an existing high-dimensional eigenvector, the factors that have no effect on the classification are eliminated. Information that facilitates classification is completely preserved, making the final classification easier and more efficient. At the same time, the dimension of the eigenvector is reduced, which effectively reduces the computational complexity in the classification [
18]. The goal of the support vector machine (SVM) algorithm is to seek the optimal combination of learning accuracy and learning ability using finite sample information, which can avoid the problem of falling into local extremum, which often occurs in neural networks [
19]. After literature research, it was found that the SVM model has high application value in the classification and identification of small-scale disease data [
20]. Therefore, we tried to apply the SVM algorithm to the identification of wheat FHB at the individual ear level. In order to further improve the accuracy of the model, the LDA–SVM model was constructed, combining the advantages of FLDA and SVM.
Research on remote sensing monitoring of crop diseases is the basis for disease monitoring at the regional scale. According to literature research, the spectral features of winter wheat infected with FHB at the ear level have not been systematically studied. The characteristics of non-imaging hyperspectral techniques are high resolution, a large number of bands with abundant information, being less influenced by the atmosphere and the external environment, and higher signal-to-noise ratio, which is closer to the true spectrum of the ground object [
8]. Therefore, using non-imaging hyperspectral techniques to study the spectral features of FHB at the ear level is of great significance for the subsequent identification of FHB. The main purposes of this paper are: (1) to evaluate the correlation between spectral features and the severity of disease at different angles of FHB-infected ears; (2) to use correlation analysis and independent sample T-tests to extract spectral feature sets that are sensitive to FHB and have significant differences between different severity classes; and (3) to use the FLDA and SVM algorithms to construct an effective model for identifying the severity of wheat infected with FHB in a complex farmland environment. The results can provide a theoretical reference for the monitoring and identification of FHB at the canopy scale or regional scale.
4. Conclusions
The effects of background differences of data collection in the three areas were eliminated by standardizing the spectral data prior to analysis. Correlation analysis and independent sample T-tests were used to verify the feasibility of identifying the disease severity by using the spectral feature sets, which were extracted by the combination of derivative features, continuum removal features, and common vegetation indices at the ear level. The FLDA and SVM models are constructed by using the sensitive spectral feature sets as input variables, and the results show that the accuracy of the two models are ideal, although the overall accuracy of the SVM was slightly better than the FLDA, but the effect is not obvious. The LDA–SVM model combined the advantages of FLDA and SVM, and had a better identification accuracy than the FLDA or SVM models. The identification accuracy for the three angles is 88.6%, 85.7%, and 68.6%, respectively. The results show that: (1) the selected spectral features have great potential in detecting ears infected with FHB, especially the first-order derivative features SDg/SDb and (SDg − SDb)/(SDg + SDb); (2) the LDA–SVM model has a good effect on identifying the severity of FHB in a complex, farmland environment; and (3) of the three angles used in the experiment, the side was the best angle for detecting FHB, followed by the front. However, the identification effect for the erect samples is poor (68.6%), which needs to be improved. The results are of great significance for identifying diseases of different severity at ear level, and provide a theoretical reference for the further study of identification of FHB at the canopy scale or regional scale.
Traditionally, the methods of disease assessment have been visually investigated in the field by experienced farmers or pathologists. Visual rating is unavoidably subjective, and the error range largely depends on the personnel skills of the evaluator. The emergence of hyperspectral measurements has distinct advantages over conventional visual inspection, allowing the information to be collected repeatedly, automatically, and objectively. In this study, a convenient, simple, and effective method was used to establish a model that can accurately identify the disease severity of wheat at the filling and maturity stages, reducing the time and effort related to manual investigation. Furthermore, it is easy to implement. The method can be used to identify FHB-infected wheat at the mild disease stage and guide field management accurately. For example, in the case of FHB infections in wheat, it can identify the area of infection and prevent infections in neighboring areas. This information can help farmers to segregate the harvesting of severely affected areas of fields to avoid toxins entering the food chain. In addition, the model can be used to collect FHB data, and can provide data support and validation for some research studies.