**1. Introduction**

During the growing season, wheat is exposed to numerous biotic and abiotic stresses. The factors causing abiotic stress are intense solar radiation, low or high temperature, excess or shortage of water, strong winds, etc. Biotic stress includes pests or diseases [1,2]. The plant's response to stress depends on many factors, including the applied variety, age, and developmental stage of plant. Numerous developmental, morphological, and physiological adaptations enable the passive avoidance of stress. The active interaction of the plant and stressor cause defense responses that prevent or tolerate changes.

One of the most important biogenic stressors is the infestation of crops by pathogenic fungi. This results in crop losses, the level of which depends on the variety, meteorological conditions, and cultivation technology. Potential losses can be minimized using appropriate agrotechnical measures and fungicide protection [3]. The most effective method for limiting the effects of disease infestation is the use of varieties containing resistance genes for individual diseases. In such cases, during plant cultivation, the accumulation of various combinations of many genes are applied [4].

A completely different strategy should be adopted in the case of Fusarium, because of the specificity of both the pathogen and the mechanism of pathogenesis. Resistance to Fusarium is a quantitative trait, associated with the presence of many quantitative trait loci (QTL) [5–9]. Almost every wheat chromosome has been identified with this type of QTL [10]. One of the most effective R genes that fight Fusarium is Fhb1 from the Sumai 3 wheat variety. Unfortunately, this variety has many unfavorable agronomic traits strongly linked to the Fhb1 gene. Growers are therefore looking for other sources to fight this disease.

Pathogenic fungi cause not only quantitative, but also qualitative losses in the wheat yield as well as a reduction in grain quality parameters (thousand grain weight, falling number, sedimentation number, and total protein content) [11–16]. Quality losses are not only the result of the development of pathogen mycelium, but also the effect of secondary metabolite production by fungi called mycotoxins. *Fusarium* fungi, mainly *F. culmorum* and *F. graminearum*, produce deoxynivalenol (DON) and zearalenone, which are highly toxic to humans and animals [17]. In 2005, the European Union introduced a standard where the maximum allowable DON concentration was set at 1250 µg/kg of unprocessed wheat grain (1.25 ppm (parts per million)).

In addition to genetic studies on wheat resistance to Fusarium, research has also been conducted on the biochemical aspects of plant responses to a massive pathogen attack. Based on the literature sources [18,19] and our own [20,21] research, it was found that antioxidative processes based on the significantly increased biosynthesis of the low-molecular antioxidants of the plant have a significant impact on pathogenesis, and constitute the first line of defense against pathogens. Based on the concentration of the selected phenolic acids, including ferulic acid, it is possible to assess the degree of disease risk at an early stage of pathogenesis [20,22]. Taking into account all of the current information on the mechanism of wheat resistance to Fusarium [20,23,24], as well as the results of chemical analyzes from three years of field experiments on wheat, it was decided to use modern methods of data analysis to determine the relationship between the concentration of ferulic acid; the concentration of deoxynivalenol and nivalenol (NIV), depending on weather conditions; and the infestation degree by fungal diseases.

Recently, increasing interest in microbiological forecasting is being observed, which has been mainly used for the examination of bacterial pathogens in the context of food. Modern computer technologies are used in other interdisciplinary research areas. Many new methods, such as artificial neural network (ANN), fuzzy logic, and genetic algorithms, which are part of artificial intelligence methods, are being used for multidimensional data analysis. Recent years show that there has been a significant increase in the use of nonlinear data modeling methods in agriculture. Particularly important analysis results are observed when using artificial neural networks, the results of which are often compared with classical statistical methods, e.g., multiple regression. As a result of the possibility of learning and generalizing data, the use of ANN gives better results than statistical methods. Neural modeling methods are used in classification, identification, and prediction, therefore, their potential is significant for practical application in broadly understood agriculture [25–39].

One of the most interesting uses of ANN is crop yield prediction. Forecasting of winter rapeseed and winter wheat yield has been applied in many works [40–45]. Because of the fact that plant yield is affected by many factors, such as meteorological conditions, fertilization level, and soil cultivation method, the use of modern data analysis techniques brings even more accurate results. In addition, because of the unique properties of data processing, ANN can simultaneously analyze empirical data in a quantitative and qualitative form. Such data are not possible to achieve using classical statistical

methods, as they are usually limited only to the quantitative interpretation of the analyzed data. Therefore, the purpose of the work is to conduct pilot studies on the basis of which neural models will be produced that examine the impact of the variety and weather conditions on the concentration of ferulic acid, and to link its content with the concentration of deoxynivalenol and nivalenol. methods, as they are usually limited only to the quantitative interpretation of the analyzed data. Therefore, the purpose of the work is to conduct pilot studies on the basis of which neural models will be produced that examine the impact of the variety and weather conditions on the concentration of ferulic acid, and to link its content with the concentration of deoxynivalenol and nivalenol.

*Agriculture* **2020**, *10*, x FOR PEER REVIEW 3 of 12

in a quantitative and qualitative form. Such data are not possible to achieve using classical statistical

### **2. Materials and Methods 2. Materials and Methods**

### *2.1. Site Location 2.1. Site Location*

The field experiment was conducted during the 2011–2013 growing seasons in Poland at the Mikulice Breeding and Production Facility belonging to the Małopolska Plant Breeding Station (50◦00026.700 N 22◦26025.200 E; Figure 1). The field experiment was conducted during the 2011–2013 growing seasons in Poland at the Mikulice Breeding and Production Facility belonging to the Małopolska Plant Breeding Station (50°00′26.7′′ N 22°26′25.2′′ E; Figure 1.)

**Figure 1.** Location of the study site. **Figure 1.** Location of the study site.

The plant material was 23 winter wheat genotypes (Table S1) with different Fusarium wilt resistance, as follows: 13 wheat lines obtained from Polish plant breeding companies; seven genotypes from six European countries; and three from the Department of Genetics and Plant Breeding, the Poznań University of Life Sciences [20]. The experiment had the following two factors: the first was the variant of protection and the second was the wheat genotype. All of the wheat genotypes were sown onto 3 m2 plots in three replications in three different variants: The plant material was 23 winter wheat genotypes (Table S1) with different Fusarium wilt resistance, as follows: 13 wheat lines obtained from Polish plant breeding companies; seven genotypes from six European countries; and three from the Department of Genetics and Plant Breeding, the Pozna ´n University of Life Sciences [20]. The experiment had the following two factors: the first was the variant of protection and the second was the wheat genotype. All of the wheat genotypes were sown onto 3 m<sup>2</sup> plots in three replications in three different variants:


No fungicides were applied to the experimental plots of K and I. No fungicides were applied to the experimental plots of K and I.

The genotypes were sown on 29 September 2010, 27 September 2011, and 28 September 2012, and harvested on 5 August 2011, 2 August 2012, and 7 August 2013. The genotypes were sown on 29 September 2010, 27 September 2011, and 28 September 2012, and harvested on 5 August 2011, 2 August 2012, and 7 August 2013.

The inoculum used in the inoculation was produced from three isolates obtained from *F. graminearum* and three isolates obtained from *F. culmorum*. The inoculum used in the inoculation was produced from three isolates obtained from *F. graminearum* and three isolates obtained from *F. culmorum*.

After the appearance of first symptoms, the severity of the Fusarium wilt was assessed. The disease index (DI) was calculated from the following formula [46,47]: After the appearance of first symptoms, the severity of the Fusarium wilt was assessed. The disease index (DI) was calculated from the following formula [46,47]:

$$\text{DI} = \frac{\text{number of infected plants}}{\text{total number of plants}} \times 100\tag{1}$$

The average monthly temperatures and monthly precipitation, measured according to the (World Meteorological Organization) WMO guidelines for the years 2011–2013, were obtained from The average monthly temperatures and monthly precipitation, measured according to the (World Meteorological Organization) WMO guidelines for the years 2011–2013, were obtained from a Vantage

Vue 6357 UE 9 meteorological station (Davis Instruments) located approximately 400 m from the experimental field (Table S2).

After harvest, the plant height was measured and chemical analyzes were carried out (Table 1).


VAR: experiment variant; VOW: winter wheat variety. FERUANN analytical models (ferulic acid content), DONANN (deoxynivalenol content) and NIVANN (nivalenol content).

## *2.2. Determination of Ferulic Acid*

The ferulic acid in the samples was analyzed after alkaline and acidic hydrolysis [20]. Analysis was performed using an Aquity H class (Ultra Performance Liquid Chromatography) UPLC system equipped with a Waters Acquity PDA detector (Waters Corporation, Parsippany, NJ, USA). Chromatographic separation was performed on an Acquity UPLC® BEH C18 column (100 mm <sup>×</sup> 2.1 mm, particle size 1.7 µm; Waters CorporationWaters, Dublin, Ireland). The elution was done with a gradient using the following mobile phase composition: (A) acetonitryl with 0.1% formic acid and (B) 1% aqueous formic acid mixture (pH = 2). The concentrations of phenolic compounds were determined using an internal standard at wavelengths of λ = 320 nm and 280 nm. Compounds were identified based on a comparison of the retention time of the analyzed peak with the retention time of

the standard, and by adding a specific amount of the standard to the analyzed samples. The detection level was 1 µg/g. The retention time for ferulic acid was 17.50 min.
