Rapidly developing remote sensing techniques are shedding new light on large-scale crop growth status monitoring, especially in recent applications of unmanned aerial vehicles (UAVs). Many inversion models have been built to estimate crop growth variables. However, the present methods focused on building models for each single crop stage, and the features generally used in the models are vegetation indices (VI) or joint VI with data derived from UAV-based sensors (e.g., texture, RGB color information, or canopy height). It is obvious these models are either limited to a single stage or have an unstable performance across stages. To address these issues, this study selected four key wheat growth parameters for inversion: above-ground biomass (AGB), plant nitrogen accumulation (PNA) and concentration (PNC), and the nitrogen nutrition index (NNI). Crop data and multispectral data were acquired in five wheat growth stages. Then, the band reflectance and VI were obtained from multispectral data, along with the five stages that were recorded as phenology indicators (PIs) according to the stage of Zadok’s scale. These three types of data formed six combinations (C1–C6): C1 used all of the band reflectances, C2 used all VIs, C3 used bands and VIs, C4 used bands and PIs, C5 used VIs and PIs, and C6 used bands, Vis, and PIs. Some of the combinations were integrated with PIs to verify if PIs can improve the model accuracy. Random forest (RF) was used to build models with combinations of different parameters and evaluate the feature importance. The results showed that all models of different combinations have good performance in the modeling of crop parameters, such as R² from 0.6 to 0.79 and NRMSE from 10.51 to 15.83%. Then, the model was optimized to understand the importance of PIs. The results showed that the combinations that integrated PIs showed better estimations and the potential of using PIs to minimize features while still achieving good predictions. Finally, the varied model results were evaluated to analyze their performances in different stages or fertilizer treatments. The results showed the models have good performances at different stages or treatments (R² > 0.6). This paper provides a reference for monitoring and estimating wheat growth parameters based on UAV multispectral imagery and phenology information.

The effective use of available nitrogen (N) to improve crop grain yields provides an important strategy to reduce environmental N pollution and promote sustainable agriculture. However, little is known about the common genetic basis of N use efficiency (NUE) at varying N availability. Two wheat (Triticum aestivum L.) cultivars were grown in the field with high, moderate, and low N supply. Cultivar Zhoumai 27 outperformed Aikang 58 independent of the N supply and showed improved growth, canopy leaf area index, flag leaf surface area, grain number, and yield, and enhanced NUE due to both higher N uptake and utilization efficiency. Further, transcriptome and proteome analyses were performed using flag leaves that provide assimilates for grain growth. The results showed that many genes or proteins that are up- or down-regulated under all N regimes are associated with N and carbon metabolism and transport. This was reinforced by cultivar differences in photosynthesis, assimilate phloem transport, and grain protein/starch yield. Overall, our study establishes that improving NUE at both high and low N supply requires distinct adjustments in leaf metabolism and assimilate partitioning. Identified key genes/proteins may individually or concurrently regulate NUE and are promising targets for maximizing crop NUE irrespective of the N supply.

Glutamine synthetase (GS), the key enzyme in plant nitrogen assimilation, is strictly regulated at multiple levels, but the most relevant reports focus on the mRNA level. Using specific antibodies as probes, the effects of nitrogen on the expression and localization of individual wheat GS (TaGS) isoforms were studied. In addition to TaGS2, TaGS1;1 with high affinity to substrate and TaGS1;3 with high catalytic activity were also localized in mesophyll, and may participate in cytoplasmic assimilation of ammonium (NH₄⁺) released from photorespiration or absorbed by roots; TaGS1;2 was localized in xylem of leaves. In roots, although there were hundreds of times more TaGS1;1 than TaGS1;2 transcripts, the amount of TaGS1;1 subunit was not higher than that of TaGS1;2; NH₄⁺ inhibited TaGS1;1 expression but stimulated TaGS1;3 expression. In root tips, nitrate stimulated TaGS1;1, TaGS1;3, and TaGS2 expression in meristem, while NH₄⁺ promoted tissue differentiation and TaGS1;2 expression in endodermis and vascular tissue. Only TaGS1;2 was located in vascular tissue of leaves and roots, and was activated by glutamine, suggesting a role in nitrogen transport. TaGS1;3 was induced by NH₄⁺ in root endodermis and mesophyll, suggesting a function in relieving NH₄⁺ toxicity. Thus, TaGS isoforms play distinct roles in nitrogen assimilation for their different kinetic properties, tissue locations, and response to nitrogen regimes.

The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R² values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring.

Optimizing canopy light distribution (CLD) has manifested improved light utility and yield without modifying other inputs. Nonetheless, molecular mechanisms working at cellular and organelle level remain to be elucidated. The current study aimed to assess the effect of CLD on photosynthetic performance and yield of wheat, and to investigate into the molecular mechanism underlying the photosynthetically active radiation (PAR)–use efficiency (PUE) at optimized CLD. Wheat was planted in two rows having different spacing [R1 (15 cm) and R2 (25 cm)] to simulate different CLD. Flag and penultimate leaves were subjected to chloroplast proteomics analysis. An increase in row spacing positively affects CLD. A decrease (16.64%) of PAR interception in the upper layer, an increase (19.76%) in the middle layer, improved PUE (12.08%), and increased yield (9.38%) were recorded. The abundance of proteins associated with photosynthetic electron transport, redox state, and carbon-nitrogen assimilation was differentially altered by CLD optimization. In the penultimate leaves, R2 reduced the abundance of photosystem II (PSII) light-harvesting proteins, PSII-subunits, and increased the photosystem I (PSI) light-harvesting proteins, NAD(P)H quinone oxidoreductase (NQO) and enzymes involved in carbon assimilation compared to R1. Additionally, leaf stomatal conductance increased. Altogether, these findings demonstrated that the regulation of chloroplast proteome is intimately linked to light utilization, which provide basis for genetic manipulation of crop species for better adaptation and improvement of cultivation strategies.

Glutamine synthetase (GS), a key enzyme in plant nitrogen metabolism, is closely related to nitrogen remobilization. However, how GS isoforms participate in nitrogen remobilization remains unclear. Here, the spatiotemporal expression of the TaGS gene family after anthesis was investigated, and the results showed that TaGS1;1 was mainly encoded by TaGS1;1-6A, while the other isozymes were mainly encoded by TaGS localized on the A and D subgenomes. TaGS1;2-4A/4D had the highest expression level, especially in rachis and peduncle. Furthermore, immunofluorescence showed TaGS1;2 was located in the phloem of rachis and peduncle. GUS (β-glucuronidase) staining confirmed that ProTaGS1;2-4A/4D::GUS activity was mainly present in the vascular system of leaves, roots, and petal of Arabidopsis. Ureides, an important transport form of nitrogen, were mainly synthesized in flag leaves and transported to grains through the phloem of peduncle and rachis during grain filling. TaAAH, which encodes the enzyme that degrades ureides to release NH₄⁺, had a higher expression in rachis and peduncle and was synchronized with the increase in NH₄⁺ concentration in phloem, indicating that NH₄⁺ in phloem is from ureide degradation. Taking the above into account, TaGS1;2, which is highly expressed in the phloem of peduncle and rachis, may participate in N remobilization by assimilating NH₄⁺ released from ureide degradation.