Zirui Yu

Leaf senescence, a pivotal process in plants, directly influences both crop yield and nutritional quality. Foxtail millet (Setaria italica) is a C₄ model crop renowned for its exceptional nutritional value and stress tolerance characteristics. However, there is a lack of research on the identification of senescence-associated genes (SAGs) and the underlying molecular regulatory mechanisms governing this process. In this study, a dark-induced senescence (DIS) experimental system was applied to investigate the extensive physiological and transcriptomic changes in two foxtail millet varieties with different degrees of leaf senescence. The physiological and biochemical indices revealed that the light senescence (LS) variety exhibited a delayed senescence phenotype, whereas the severe senescence (SS) variety exhibited an accelerated senescence phenotype. The most evident differences in gene expression profiles between these two varieties during DIS included photosynthesis, chlorophyll, and lipid metabolism. Comparative transcriptome analysis further revealed a significant up-regulation of genes related to polysaccharide and calcium ion binding, nitrogen utilization, defense response, and malate metabolism in LS. In contrast, the expression of genes associated with redox homeostasis, carbohydrate metabolism, lipid homeostasis, and hormone signaling was significantly altered in SS. Through WGCNA and RT-qPCR analyses, we identified three SAGs that exhibit potential negative regulation towards dark-induced leaf senescence in foxtail millet. This study establishes the foundation for a further comprehensive examination of the regulatory network governing leaf senescence and provides potential genetic resources for manipulating senescence in foxtail millet.

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.