Optimizing root system architecture (RSA) is essential for plants because of its critical role in acquiring water and nutrients from the soil. However, the subterranean nature of roots complicates the measurement of RSA traits. Recently developed rhizobox methods allow for the rapid acquisition of root images. Nevertheless, effective and precise approaches for extracting RSA features from these images remain underdeveloped. Deep learning (DL) technology can enhance image segmentation and facilitate RSA trait extraction. However, comprehensive pipelines that integrate DL technologies into image-based root phenotyping techniques are still scarce, hampering their implementation. To address this challenge, we present a reproducible pipeline (faCRSA) for automated RSA traits analysis, consisting of three modules: (1) the RSA traits extraction module functions to segment soil-root images and calculate RSA traits. A lightweight convolutional neural network (CNN) named RootSeg was proposed for efficient and accurate segmentation; (2) the data storage module, which stores image and text data from other modules; and (3) the web application module, which allows researchers to analyze data online in a user-friendly manner. The correlation coefficients (R²) of total root length, root surface area, and root volume calculated from faCRSA and manually measured results were 0.96^**, 0.97^**, and 0.93^**, respectively, with root mean square errors (RMSE) of 8.13 cm, 1.68 cm², and 0.05 cm³, processed at a rate of 9.74 s per image, indicating satisfying accuracy. faCRSA has also demonstrated satisfactory performance in dynamically monitoring root system changes under various stress conditions, such as drought or waterlogging. The detailed code and deployable package of faCRSA are provided for researchers with the potential to replace manual and semi-automated methods.

The selection and promotion of high-yielding and nitrogen-efficient wheat varieties can reduce nitrogen fertilizer application while ensuring wheat yield and quality and contribute to the sustainable development of agriculture; thus, the mining and localization of nitrogen use efficiency (NUE) genes is particularly important, but the localization of NUE genes requires a large amount of phenotypic data support. In view of this, we propose the use of low-altitude aerial photography to acquire field images at a large scale, generate 3-dimensional (3D) point clouds and multispectral images of wheat plots, propose a wheat 3D plot segmentation dataset, quantify the plot canopy height via combination with PointNet++, and generate 4 nitrogen utilization-related vegetation indices via index calculations. Six height-related and 24 vegetation-index-related dynamic digital phenotypes were extracted from the digital phenotypes collected at different time points and fitted to generate dynamic curves. We applied height-derived dynamic numerical phenotypes to genome-wide association studies of 160 wheat cultivars (660,000 single-nucleotide polymorphisms) and found that we were able to locate reliable loci associated with height and NUE, some of which were consistent with published studies. Finally, dynamic phenotypes derived from plant indices can also be applied to genome-wide association studies and ultimately locate NUE- and growth-related loci. In conclusion, we believe that our work demonstrates valuable advances in 3D digital dynamic phenotyping for locating genes for NUE in wheat and provides breeders with accurate phenotypic data for the selection and breeding of nitrogen-efficient wheat varieties.

Crop uniformity is a comprehensive indicator used to describe crop growth and is important for assessing crop yield and biomass potential. However, there is still a lack of continuous monitoring of uniformity throughout the growing season to explain their effects on yield and biomass. Therefore, this paper proposed a wheat uniformity quantification method based on unmanned aerial vehicle imaging technology to monitor and analyze the dynamic changes in wheat uniformity. The leaf area index (LAI), soil plant analysis development (SPAD), and fractional vegetation cover were estimated from hyperspectral images, while plant height was estimated by a point cloud model from RGB images. Based on these 4 agronomic parameters, a total of 20 uniformity indices covering multiple growing stages were calculated. The changing trends in the uniformity indices were consistent with the results of visual interpretation. The uniformity indices strongly correlated with yield and biomass were selected to construct multiple linear regression models for estimating yield and biomass. The results showed that Pielou’s index of LAI had the strongest correlation with yield and biomass, with correlation coefficients of −0.760 and −0.801, respectively. The accuracies of the yield (coefficient of determination [R²] = 0.616, root mean square error [RMSE] = 1.189 Mg/ha) and biomass estimation model (R² = 0.798, RMSE = 1.952 Mg/ha) using uniformity indices were better than those of the models using the mean values of the 4 agronomic parameters. Therefore, the proposed uniformity monitoring method can be used to effectively evaluate the temporal and spatial variations in wheat uniformity and can provide new insights into the prediction of yield and biomass.

Frequent drought events severely restrict global crop productivity, especially those occurring in the reproductive stages. Moderate drought priming during the earlier growth stages is a promising strategy for allowing plants to resist recurrent severe drought stress. However, the underlying mechanisms remain unclear. Here, we subjected wheat plants to drought priming during the vegetative growth stage and to severe drought stress at 10 days after anthesis. We then collected leaf samples at the ends of the drought priming and recovery periods, and at the end of drought stress for transcriptome sequencing in combination with phenotypic and physiological analyses. The drought-primed wheat plants maintained a lower plant temperature, with higher stomatal openness and photosynthesis, thereby resulting in much lower 1,000-grain weight and grain yield losses under the later drought stress than the non-primed plants. Interestingly, 416 genes, including 27 transcription factors (e.g., MYB, NAC, HSF), seemed to be closely related to the improved drought tolerance as indicated by the dynamic transcriptome analysis. Moreover, the candidate genes showed six temporal expression patterns and were significantly enriched in several stress response related pathways, such as plant hormone signal transduction, starch and sucrose metabolism, arginine and proline metabolism, inositol phosphate metabolism, and wax synthesis. These findings provide new insights into the physiological and molecular mechanisms of the long-term effects of early drought priming that can effectively improve drought tolerance in wheat, and may provide potential approaches for addressing the challenges of increasing abiotic stresses and securing food safety under global warming scenarios.

Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics (spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hyper-temporal, and large-volume light detection and ranging (LiDAR) and multispectral data to (i) identify the best machine learning method and prediction stage for wheat yield estimation, (ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and (iii) elucidate the contribution of time-series data fusion and 3D spatial information to yield estimation. Wheat yield could be accurately (R² = 0.891) and timely (approximately-two months before harvest) estimated from fused LiDAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits (such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits (such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3D points than from canopy surface points and from integrated multi-stage (especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and time-series information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.

Drought priming is a promising approach to improve tolerance to further drought in wheat. The root apex plays important roles in drought however, its contribution to drought priming remains unknown. To provide mechanistic insights into this process, the transcriptomes and proteomes at three different zones along the root axis under drought stress were analyzed. Physiological assessment of root growth indicated that priming augmented roots growth in response to drought and also the levels of protective proline and glycine betaine. Scanning across the proximal to the distal zones of the root apex indicated increases the transcription of genes involved in primary and secondary metabolism. Conversely, genes related to translation, transcription, folding, sorting and degradation, replication and repair were increased in the apex compared to the proximal zone. A single drought episode suppressed their expression but prior drought priming served to maintain expression with recurrent drought stress. The differentially primed responses genes were mainly involved in the pathways related to plant hormone signaling, stress defense and cell wall modification. The prediction of regulatory hubs using Cytoscape implicated signaling components such as the ABA receptor PYL4 as influencing antioxidant status and the cell cycle. Based our integrative transcriptomic-proteomic assessments we present a model for drought priming protected plant hormone signaling transduction pathways to drive the cell cycle and cell wall loosening to confer beneficial effects on roots to counter the effects of drought. This model provides a theoretical basis for improvement of drought tolerance in wheat, via an increased understanding of drought priming induced drought tolerance.

Drought stress is a limiting factor for wheat production and food security. Drought priming has been shown to increase drought tolerance in wheat. However, the underlying mechanisms are unknown. In the present study, the genes encoding the biosynthesis and metabolism of abscisic acid (ABA) and jasmonic acid (JA), as well as genes involved in the ABA and JA signaling pathways were up-regulated by drought priming. Endogenous concentrations of JA and ABA increased following drought priming. The interplay between JA and ABA in plant responses to drought priming was further investigated using inhibitors of ABA and JA biosynthesis. Application of fluridone (FLU) or nordihydroguaiaretic acid (NDGA) to primed plants resulted in lower chlorophyll-fluorescence parameters and activities of superoxide dismutase and glutathione reductase, and higher cell membrane damage, compared to primed plants (PD) under drought stress. NDGA + ABA, but not FLU + JA, restored priming-induced tolerance, as indicated by a finding of no significant difference from PD under drought stress. Under drought priming, NDGA induced the suppression of ABA accumulation, while FLU did not affect JA accumulation. These results were consistent with the expression of genes involved in the biosynthesis of ABA and JA. They suggest that ABA and JA are required for priming-induced drought tolerance in wheat, with JA acting upstream of ABA.