The aboveground dry biomass (AGDB) of winter wheat can reflect the growth and development of winter wheat. The rapid monitoring of AGDB by using hyperspectral technology is of great significance for obtaining the growth and development status of winter wheat in real time and promoting yield increase. This study analyzed the changes of AGDB based on a winter wheat irrigation experiment. At the same time, the AGDB and canopy hyperspectral reflectance of winter wheat were obtained. The effect of spectral preprocessing algorithms such as reciprocal logarithm (Lg), multiple scattering correction (MSC), standardized normal variate (SNV), first derivative (FD), and second derivative (SD); sample division methods such as the concentration gradient method (CG), the Kennard–Stone method (KS), and the sample subset partition based on the joint X–Y distances method (SPXY); sample division ratios such as 1:1 (Ratio1), 3:2 (Ratio2), 2:1 (Ratio3), 5:2 (Ratio4), and 3:1 (Ratio5); dimension reduction algorithms such as uninformative variable elimination (UVE); and modeling algorithms such as partial least-squares regression (PLSR), stepwise multiple linear regression (SMLR), artificial neural network (ANN), and support vector machine (SVM) on the hyperspectral monitoring model of winter wheat AGDB was studied. The results showed that irrigation can improve the AGDB and canopy spectral reflectance of winter wheat. The spectral preprocessing algorithm can change the original spectral curve and improve the correlation between the original spectrum and the AGDB of winter wheat and screen out the bands of 1400 nm, 1479 nm, 1083 nm, 741 nm, 797 nm, and 486 nm, which have a high correlation with AGDB. The calibration sets and validation sets divided by different sample division methods and sample division ratios have different data-distribution characteristics. The UVE method can obviously eliminate some bands in the full-spectrum band. SVM is the best modeling algorithm. According to the universality of data, the better sample division method, sample division ratio, and modeling algorithm are SPXY, Ratio4, and SVM, respectively. Combined with the original spectrum and by using UVE to screen bands, a model with stable performance and high accuracy can be obtained. According to the particularity of data, the best model in this study is FD-CG-Ratio4-Full-SVM, for which the R²_c, RMSE_c, R²_v, RMSE_v, and RPD are 0.9487, 0.1663 kg·m⁻², 0.7335, 0.3600 kg·m⁻², and 1.9226, respectively, which can realize hyperspectral monitoring of winter wheat AGDB. This study can provide a reference for the rational irrigation of winter wheat in the field and provide a theoretical basis for monitoring the AGDB of winter wheat by using hyperspectral remote sensing technology.

The kidney bean is an important cash crop whose growth and yield are severely affected by brown spot disease. Traditional target detection models cannot effectively screen out key features, resulting in model overfitting and weak generalization ability. In this study, a Bi-Directional Feature Pyramid Network (BiFPN) and Squeeze and Excitation (SE) module were added to a YOLOv5 model to improve the multi-scale feature fusion and key feature extraction abilities of the improved model. The results show that the BiFPN and SE modules show higher heat in the target location region and pay less attention to irrelevant environmental information in the non-target region. The detection Precision, Recall, and mean average Precision (mAP@0.5) of the improved YOLOv5 model are 94.7%, 88.2%, and 92.5%, respectively, which are 4.9% higher in Precision, 0.5% higher in Recall, and 25.6% higher in the mean average Precision compared to the original YOLOv5 model. Compared with the YOLOv5-SE, YOLOv5-BiFPN, FasterR-CNN, and EfficientDet models, detection Precision improved by 1.8%, 3.0%, 9.4%, and 9.5%, respectively. Moreover, the rate of missed and wrong detection in the improved YOLOv5 model is only 8.16%. Therefore, the YOLOv5-SE-BiFPN model can more effectively detect the brown spot area of kidney beans.

Aboveground biomass (AGB) is a key parameter reflecting crop growth which plays a vital role in agricultural management and ecosystem assessment. Real-time and non-destructive biomass monitoring is essential for accurate field management and crop yield prediction. This study utilizes a multi-sensor-equipped unmanned aerial vehicle (UAV) to collect remote sensing data during critical growth stages of millet, including spectral, textural, thermal, and point cloud information. The use of RGB point cloud data facilitated plant height extraction, enabling subsequent analysis to discern correlations between spectral parameters, textural indices, canopy temperatures, plant height, and biomass. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models were constructed to evaluate the capability of different features and integrated multi-source features in estimating the AGB. Findings demonstrated a strong correlation between the plant height derived from point cloud data and the directly measured plant height, with the most accurate estimation of millet plant height achieving an R² of 0.873 and RMSE of 7.511 cm. Spectral parameters, canopy temperature, and plant height showed a high correlation with the AGB, and the correlation with the AGB was significantly improved after texture features were linearly transformed. Among single-factor features, the RF model based on textural indices showcased the highest accuracy in estimating the AGB (R² = 0.698, RMSE = 0.323 kg m⁻², and RPD = 1.821). When integrating two features, the RF model incorporating textural indices and canopy temperature data demonstrated optimal performance (R² = 0.801, RMSE = 0.253 kg m⁻², and RPD = 2.244). When the three features were fused, the RF model constructed by fusing spectral parameters, texture indices, and canopy temperature data was the best (R² = 0.869, RMSE = 0.217 kg m⁻², and RPD = 2.766). The RF model based on spectral parameters, texture indices, canopy temperature, and plant height had the highest accuracy (R² = 0.877, RMSE = 0.207 kg m⁻², and RPD = 2.847). In this study, the complementary and synergistic effects of multi-source remote sensing data were leveraged to enhance the accuracy and stability of the biomass estimation model.

(1) Background: With the increase in people’s consumption of processed oat products, the production of selenium (Se)-enriched oat has become a possibility to supplement the human body with Se. Therefore, the effects of various factors on the Se-enriched ability and yield of different oat varieties were comprehensively studied. (2) Methods: cv.“Pinyan 5” and cv.“Bayou 18” were applied at the stem-elongation stage and heading stage in the Jinzhong (JZ), and cv.“Bayou 1” and cv.“Jinyan 18” were applied at the heading stage and flowering stage in the northwestern Shanxi (JXB) with different doses of Na₂SeO₃ (0, 5.48, 10.96, 21.92, 43.84, 65.76, 98.64, 0, 5.48, 10.96, 21.92, 43.84, 65.76, 98.64, 147.96 g hm⁻²) by soil application and foliar spraying. (3) Results: The grain Se content and yield of oat were affected by the variety, Se application dose, stage and method of Se supplementation. Additionally, the Se content in oat grain was positively correlated with the Se application dose while the yield of oat first increased and then decreased with the Se application dose. (4) Conclusions: In the JZ and JXB, 21.92 g hm⁻² and 43.84 g hm⁻² Se was sprayed on the leaves of cv.“Bayou 18” and cv.“Bayou 1” at the heading stage, respectively, was the most effective Se biofortification program.

Common oat (Avena sativa L.) is one of the important minor grain crops in China, and drought stress severely affects its yield and quality. To investigate the drought resistance characteristics of oat seedlings, this study used Baiyan 2, an oat cultivar at the three-leaf stage, as the experimental material. Drought stress was simulated using polyethylene glycol (PEG) to treat the seedlings. The photosynthetic parameters and physicochemical indices of the treatment groups at 6 h and 12 h were measured and compared with the control group at 0 h. The results showed that drought stress did not significantly change chlorophyll content, but it significantly reduced net photosynthetic rate and other photosynthetic parameters while significantly increasing proline content. Transcriptome analysis was conducted using seedlings from both the control and treatment groups, comparing the two treatment groups with the control group using Tbtool software (v2.136). This analysis identified 344 differentially expressed genes. Enrichment analysis of these differentially expressed genes revealed significant enrichment in physiological pathways such as photosynthesis and ion transport. Ten differentially expressed genes related to the physiological process of photosynthetic carbon assimilation were identified, all of which were downregulated. Additionally, seven differentially expressed genes were related to ion transport. Through gene co-expression analysis combined with promoter region structure analysis, 11 transcription factors (from MYB, AP2/ERF, C2C2-dof) were found to regulate the expression of 10 genes related to photosynthetic carbon assimilation. Additionally, five transcription factors regulate the expression of two malate transporter protein-related genes (from LOB, zf-HD, C2C2-Dof, etc.), five transcription factors regulate the expression of two metal ion transporter protein-related genes (from MYB, zf-HD, C2C2-Dof), five transcription factors regulate the expression of two chloride channel protein-related genes (from MYB, bZIP, AP2/ERF), and two transcription factors regulate the expression of one Annexin-related gene (from NAC, MYB). This study provides a theoretical foundation for further research on the molecular regulation of guard cells and offers a molecular basis for enhancing drought resistance in oats.