Haloxyfop-P-methyl (HPM) is widely used for grass weed control in oilseed rape production. However, whether its residues affect subsequent seed germination and the underlying mechanisms remains unclear. In this study, seeds harvested from HPM-treated fields exhibited a significantly reduced germination rate of 36.5%, compared with 100% in the control group, accompanied by abnormal cell morphology. Residue analysis revealed an HPM concentration of 3.36 mg kg⁻¹ in the treated seeds, exceeding the Chinese maximum residue limit (MRL) of 3 mg kg⁻¹ (GB 2763). Given that herbicides often induce oxidative stress, we measured a range of antioxidant markers (H₂O₂, MDA, SOD, POD, GSH, GSSG, AsA, and DHA) during germination (12 h and 24 h) but detected no significant changes, which did not support oxidative stress as the primary mechanism during this phase. Relative electrical conductivity measurements further indicated that membrane integrity was already compromised in dry seeds, suggesting that oxidative injury—if it occurred—may have taken place during seed development rather than during germination. To elucidate the molecular basis, we performed transcriptomic, metabolomic, and lipidomic analyses, which revealed significant downregulation of fatty acid biosynthesis genes and marked alterations in the lipidome profile. Collectively, our multi-stage investigation demonstrates that HPM residue accumulation is associated with impaired seed germination, primarily through metabolic disruption of lipid reserves rather than oxidative damage during germination, highlighting a non-target phytotoxic effect of this herbicide on the crop itself. These findings provide a theoretical basis for understanding the potential toxicity of persistent HPM residues in rapeseed, with implications for pesticide environmental risk assessment and the safe production of rapeseed.

Accurate estimation of canopy water content (OWC) is essential for precision irrigation, crop growth monitoring, and yield prediction. Unmanned aerial vehicle (UAV)-based multispectral remote sensing provides a rapid and non-destructive approach for monitoring crop water status; however, the selection of effective spectral features and appropriate machine learning algorithms for robust OWC estimation remains insufficiently investigated, particularly across multiple growing seasons. This study evaluated the potential of UAV multispectral imagery for estimating oilseed rape canopy water content using two feature selection strategies and four representative machine learning algorithms. Field experiments were conducted during two consecutive growing seasons (2023–2024 and 2024–2025). Different sowing dates, nitrogen application rates, and planting densities were used to create a broad range of canopy water conditions. UAV multispectral images were acquired at ten representative growth stages during the reproductive period, from stem elongation to physiological maturity. Fourteen vegetation indices (VIs) were extracted from the multispectral imagery. Pearson correlation analysis and principal component analysis (PCA) were used to select informative features. These features were then used to develop multiple linear regression (MLR), partial least squares (PLS), support vector machine (SVM), and random forest (RF) models. Model performance was evaluated using each single-year dataset and the combined two-year dataset to assess robustness under different seasonal conditions. The RF model consistently achieved the highest prediction accuracy. The correlation-based RF model developed from the combined two-year dataset produced the best performance. It achieved an R² of 0.966, an RMSE of 1.734%, and an RRMSE of 2.360% for the training dataset. For the independent testing dataset, the corresponding values were 0.901, 2.794%, and 3.830%, respectively. The PCA-based models showed similar performance and effectively reduced feature redundancy. However, they did not consistently outperform the correlation-based models. These results indicate that combining UAV multispectral imagery with appropriate feature selection and machine learning algorithms can accurately estimate oilseed rape canopy water content under field conditions. Integrating data from multiple growing seasons further improves model robustness and provides a practical basis for UAV-assisted crop water monitoring and precision agricultural management.