Poor seed germination is a crucial yield-limiting factor when winter wheat is sown under low temperature. The objective of this study was to evaluate the role of ascorbic acid (AsA) in the extenuation of the harmful effects of low temperature at early and reproductive stages of wheat during 2016–2017 (15 November to 15 December). A two-year experiment was conducted using a randomized complete block design with split plot arrangement and with three replicates. Sowing dates (15 November and 15 December) were allotted to the main plot while seed priming (control, hydro-priming, and AsA priming) were allotted to the sub-plot. Results demonstrated that AsA priming significantly boosted different yield characteristics including chlorophyll content, tillers per unit area, number of grains per spike, and 1000-grain weight, contributing higher productivity and biomass during 2016–2017. The results further revealed that AsA could induce the up-regulation of diverse antioxidants (super oxide dismutase (SOD), peroxidase (POD), and catalase (CAT)), thus offsetting the adverse effects of sub-supra optimum temperatures of late sowing wheat. It is therefore concluded in this work that AsA priming enhances stand establishment, yield and yield-related traits, antioxidant enzyme activities, and chlorophyll contents when wheat is sown under low temperature.

Real-time monitoring of cotton above-ground biomass (AGB) is crucial for monitoring crop growth and optimizing management practices. This study evaluated UAV-based RGB and multispectral (MS) imagery for cotton AGB estimation across multiple growth stages under different planting densities and sowing dates in Anyang, China. Spectral features, vegetation indices (VIs), and Gray Level Co-occurrence Matrix (GLCM) texture metrics were extracted and organized into three scenarios: RGB + MS, RGB-only, and MS-only. Recursive feature elimination with cross-validation (RFECV) was applied for feature selection, and six machine learning models were evaluated using both baseline and selected feature sets. Results showed that model performance was strongly influenced by growth stage, sensor configuration, and feature composition. Accuracy was highest at the seedling and squaring stages and decreased at flowering due to canopy complexity and spectral saturation. MS-only and fused features generally performed best at the seedling stage, while RGB-only features were competitive or superior at the squaring stage, highlighting the importance of high-resolution structural information. At flowering, fused RGB–MS features provided the most stable performance, although improvements were limited. RFECV exhibited stage-dependent behavior, improving performance mainly at early growth stages but showing inconsistent benefits later. SHAP analysis revealed a shift from texture-dominated predictors at the seedling stage to balanced feature contributions at squaring and vegetation index (VIs) dominance at flowering. Overall, cotton AGB estimation is a stage-dependent process requiring adaptive sensor and feature selection strategies.