Powdery mildew severely affects wheat growth and yield; therefore, its effective monitoring is essential for the prevention and control of the disease and global food security. In the present study, a spectroradiometer and thermal infrared cameras were used to obtain hyperspectral signature and thermal infrared images data, and thermal infrared temperature parameters (TP) and texture features (TF) were extracted from the thermal infrared images and RGB images of wheat with powdery mildew, during the wheat flowering and filling periods. Based on the ten vegetation indices from the hyperspectral data (VI), TF and TP were integrated, and partial least square regression, random forest regression (RFR), and support vector machine regression (SVR) algorithms were used to construct a prediction model for a wheat powdery mildew disease index. According to the results, the prediction accuracy of RFR was higher than in other models, under both single data source modeling and multi-source data modeling; among the three data sources, VI was the most suitable for powdery mildew monitoring, followed by TP, and finally TF. The RFR model had stable performance in multi-source data fusion modeling (VI&TP&TF), and had the optimal estimation performance with 0.872 and 0.862 of R² for calibration and validation, respectively. The application of multi-source data collaborative modeling could improve the accuracy of remote sensing monitoring of wheat powdery mildew, and facilitate the achievement of high-precision remote sensing monitoring of crop disease status.

The increasing frequency of low-temperature events in spring, driven by climate change, poses a serious threat to wheat production in Northern China. Understanding how low-temperature stress affects wheat yield and its components under varying moisture conditions, and exploring the role of irrigation before exposure to low temperatures, is crucial for food security and mitigating agricultural losses. In this study, four wheat cultivars—semi-spring (YZ4110, LK198) and semi-winter (ZM366, FDC21)—were tested across two years under different conditions of soil moisture (irrigation before low-temperature exposure (IBLT) and non-irrigation (NI)) and low temperatures (−2 °C, −4 °C, −6 °C, −8 °C, and −10 °C). The IBLT treatment effectively reduced leaf wilt, stem breakage, and spikelet desiccation. Low-temperature stress adversely impacted the yield per plant—including both original and regenerated yields—and yield components across all wheat varieties. Furthermore, a negative correlation was found between regenerated and original yields. Semi-spring varieties showed greater yield reduction than semi-winter varieties, with a more pronounced impact under NI compared to IBLT. This suggests that the compensatory regenerative yield is more significant in semi-spring varieties and under NI conditions. As low-temperature stress intensified, the primary determinant of yield loss shifted from grain number per spike (GNPS) to spike number per plant (SNPP) beyond a specific temperature threshold. Under NI, this threshold was −6 °C, while it was −8 °C under IBLT. Low-temperature stress led to variability in fruiting rate across different spike positions, with semi-spring varieties and NI conditions showing the most substantial reductions. Sensitivity to low temperatures varied across spikelet positions: Apical spikelets were the most sensitive, followed by basal, while central spikelets showed the largest reduction in grain number as stress levels increased, significantly contributing to reduced overall grain yield. Irrigation, variety, and low temperature had variable impacts on physiological indices in wheat. Structural equation modeling (SEM) analysis revealed that irrigation significantly enhanced wheat’s response to cold tolerance indicators—such as superoxide dismutase (SOD), proline (Pro), and peroxidase (POD)—while reducing malondialdehyde (MDA) levels. Irrigation also improved photosynthesis (Pn), chlorophyll fluorescence (Fv/Fm), and leaf water content (LWC), thereby mitigating the adverse effects of low-temperature stress and supporting grain development in the central spike positions. In summary, IBLT effectively mitigates yield losses due to low-temperature freeze injuries, with distinct yield component contributions under varying stress conditions. Furthermore, this study clarifies the spatial distribution of grain responses across different spike positions under low temperatures, providing insights into the physiological mechanisms by which irrigation mitigates grain loss. These findings provide a theoretical and scientific basis for effective agricultural practices to counter spring freeze damage and predict wheat yield under low-temperature stress.

Wheat yellow mosaic disease is a low-temperature and soil-borne disease. Crop infection by the yellow mosaic virus can lead to severe yield and economic losses. It is easily confused with nitrogen deficiency based on the plant’s morphological characteristics. Timely disease detection and crop management in the field require the precise identification of crop stress types. However, the detection of crop stress is often underappreciated. Wheat nitrogen deficiency and yellow mosaic disease were investigated in the field and wheat physiological and biochemical experiments were conducted to collect agronomic indicators, four years of reflectance spectral data at green-up and jointing were collected, and then studies for the detection of nitrogen deficiency and yellow mosaic disease stresses were carried out. The continuous removal (CR), first-order derivative (FD), standard normal variate (SNV), and spectral separation of soil and vegetation (3SV) preprocessing methods and 96 spectral indices were evaluated. The threshold method and variance inflation factor (TVIF) were used as feature selection methods combined with machine learning to develop a crop stress detection method. The results show that the most sensitive wavelengths are found in the 725–1000 nm region, while the sensitivity of the spectrum in the 400–725 nm region is lower. The PRI_670,570, B, and RARSa spectral indices can detect nitrogen deficiency and yellow leaf disease stress, and the OA and Kappa values are 93.87% and 0.873, respectively, for PRI_670,570, which is the best index. A 3SV-TVIF-SVM stress detection method was then proposed, using OA and Kappa values of 96.97% and 0.931, respectively, for field data validation. The results of the study can provide technical support and a theoretical basis for the accurate control of yellow mosaic disease and nitrogen fertilizer management in the field.

Optimizing the interaction between planting density and nitrogen (N) application rate is critical for simultaneously improving grain yield and nitrogen use efficiency (NUE) in winter wheat (Triticum aestivum L.). However, the underlying regulatory mechanism remains poorly understood in the fluvo-aquic soil region of the southern Huang–Huai–Hai Plain. This study aimed to elucidate the physiological mechanism by which planting density and nitrogen application interactively regulate source–sink coordination to achieve synergistic high grain yield and high NUE, and to screen the optimal local cultivation combination for winter wheat in southeastern Henan. A two-year consecutive field experiment was conducted from 2018 to 2020 in Shangshui, Henan, using a split-plot design. Three planting densities (D1: 225 × 10⁴ plants ha⁻¹; D2: 375 × 10⁴ plants ha⁻¹; D3: 525 × 10⁴ plants ha⁻¹) and five N rates (N0: 0; N1: 180; N2: 240; N3: 300; N4: 360 kg N ha⁻¹) were established. Results demonstrated that planting density, N rate, and their interaction significantly regulated grain yield, NUE, and dry matter and N allocation, with consistent trends across both years. Increasing density enhanced total biomass and N accumulation, but dry matter and N partitioning to grains declined when density exceeded 375 × 10⁴ plants ha⁻¹. Grain yield exhibited a quadratic response to N rate; the optimal N rate for maximum yield decreased from 296.33 kg ha⁻¹ at low density (D1) to 237.50–245.38 kg ha⁻¹ at medium and high densities. The combination of 240 kg N ha⁻¹ and 375 × 10⁴ plants ha⁻¹ (D2N2) produced the highest average grain yield (8875.35 kg ha⁻¹), with simultaneous improvements in spike number and kernels per spike as well as superior dry matter and N partitioning to grains. This combination also maintained high nitrogen recovery efficiency (NRE) and nitrogen agronomic efficiency (NAE). Correlation analysis revealed that grain yield and NUE were significantly positively correlated with dry matter accumulation, N accumulation, and their partitioning proportions to grains. Overall, D2N2 achieved simultaneous high yield and high NUE by coordinately optimizing dry matter and N partitioning to grains. We therefore recommend reducing N fertilizer to approximately 240 kg ha⁻¹ combined with a moderate planting density of 375 × 10⁴ plants ha⁻¹ as the preferred strategy for sustainable and intensive winter wheat production in the fluvo-aquic soil region of southeastern Henan and adjacent areas.