Leaf Inclination Research Articles

Vegetation monitoring in mining area using canopy reflectance and landsat8 oli image

The aim of this study is to find out the vegetation monitoring in mining areas which is of great significance in grasping the ecological environment status of mining areas. As the first target of spectral reflection, the vegetation canopy directly affects the results of spectral reception. Leaf Angle Distribution (LAD) is a key canopy structural parameter, which directly determines the amount of solar radiation intercepted by the canopy. Taking the relationship between the effect of leaf inclination angle on vegetation canopy reflectance and NDVI as an entry point, the study monitored and analyzed the vegetation in Dexing mining area, China, from 2013 to 2018 by NDVI, based on which the Copper Stress Vegetation Index (CSVI), which is applicable to the narrow wavelength band of remote sensing imagery, was evaluated whether it could be applied to the wide wavelength band. The results show that the NDVI value of the horizontal distribution is significantly higher than that of other leaf inclination distributions, and the NDVI in the direction of the horizontal distribution is 0.82, while the NDVI in the direction of the hi-straight distribution is 0.75; the NDVI of the mining area decreases gradually with the increase of the area of the mining area, and the correlation coefficients of the two groups of CSVI based on the wide and narrow bands are not large, which is not suitable for the vegetation monitoring by using the Landsat8 OLI imagery. Wide band is less applicable for vegetation monitoring. Bangladesh J. Bot. 53(3): 835-847, 2024 (September) Special

Leaf angle, light interception and chlorophyll contents of bean and soybean grown in integrated cropping systems

ABSTRACT This study aimed to evaluate the dynamics of solar radiation and the morphological and physiological responses of bean and soybean plants grown in two integrated cultivation systems and under full sun. Two experiments were conducted in southern Brazil, one with beans (in-season) and the other with soybeans (off-season). The experimental design used was randomised blocks in a bifactorial 2 × 2 arrangement with an additional treatment, characterised by two forest arrangements (6 × 6 m and 12 × 12 m); two forest species (Peltophorum dubium and Eucalyptus urograndis); and full sun, with three repetitions. The transmissivity of solar radiation, intercepted solar radiation, leaf inclination angle, light extinction coefficient (k), and chlorophyll content (a, b, total, and a/b ratio) were measured. The dynamics of solar radiation were modified by the arrangement of trees and the canopy characteristics of the forest species. Shading significantly influenced the levels of chlorophyll a, b, and total chlorophyll, while no variation was observed in the angle of inclination of the leaves. It is recommended to cultivate beans intercropped with the forest species E. urograndis in the 12 × 12 intercropped arrangement, while for soybean cultivation, intercropping with the two forest species studied in the 12 × 12 intercropped arrangement is recommended.

3D Morphological Feature Quantification and Analysis of Corn Leaves.

Marked variations in the 3-dimensional (3D) shape of corn leaves can be discerned as a function of various influences, including genetics, environmental factors, and the management of cultivation processes. However, the causes of these variations remain unclear, primarily due to the absence of quantitative methods to describe the 3D spatial morphology of leaves. To address this issue, this study acquired 3D digitized data of ear-position leaves from 478 corn inbred lines during the grain-filling stage. We propose quantitative calculation methods for 13 3D leaf shape features, such as the leaf length, 3D leaf area, leaf inclination angle, blade-included angle, blade self-twisting, blade planarity, and margin amplitude. Correlation analysis, cluster analysis, and heritability analysis were conducted among the 13 leaf traits. Leaf morphology differences among subpopulations of the inbred lines were also analyzed. The results revealed that the 3D leaf traits are capable of revealing the morphological differences among different leaf surfaces, and the genetic analysis revealed that 84.62% of the 3D phenotypic traits of ear-position leaves had a heritability greater than 0.3. However, the majority of 3D leaf shape traits were strongly affected by environmental conditions. Overall, this study quantitatively investigated 3D leaf shape in corn, providing a reliable basis for further research on the genetic regulation of corn leaf morphology and advancing the understanding of the complex interplay among crop genetics, phenotypes, and the environment.

Influence of leaf inclination angle and tillering on population transpiration, soil evaporation, and yield in winter wheat near-isogenic lines.

Leaf inclination angle (LIA) and tillering impact the winter wheat (Triticum aestivum L.) population canopy structure. Understanding their effects on water use (WU) parameters and yield can guide water-saving strategies through population control. In this study, six near-isogenic lines (NILs) and their parents were selected as materials. These special materials were characterized by varying tillering at the current sowing density, a similar genetic background, and, particularly, a gradient in mean flag leaf LIA. The investigation focused on the jointing to early grain-filling stage, the peak water requirement period of wheat crops. Population-scale transpiration (PT) and evaporation from the soil surface (E) were partitioned from total evapotranspiration (ET) by the means of micro-lysimeters. The results showed decreased PT, E, and ET with increased population density (PD) within a narrow density range derived from varying tillering across genotypes. Significant correlations existed between PD and ET, E, and PT, especially in the wettest 2017-2018 growing season. Within such narrow PD range, all the correlations between WU parameters and PD were negative, although some correlations were not statistically significant, thereby suggesting the population structure's predominant impact. No significant correlation existed between LIA and both ET and PT within the LIA range of 35°-65°. However, significant correlations occurred between LIA and E in two growing seasons. Genotypes with similar LIA but different PD produced varied ET; while with similar PD, the four pairs of genotypes with different LIA each consumed similar ET, thus highlighting PD's more crucial role in regulating ET. The yield increased with higher LIA, and showed a significant correlation, emphasizing the LIA's significant effect on yield. However, no correlation was observed with PD, indicating the minor effect of tillering at the current sowing density. Therefore these results might offer valuable insights for breeding water-saving cultivars and optimizing population structures for effective field water conservation.

Multi-view 3D reconstruction of seedling using 2D image contour

3D reconstruction of seedling can provide comprehensive and quantitative spatial structure information, offering an effective digital tool for breeding research. However, accurate and efficient reconstruction of seedling is still a challenging work due to limited performance of depth sensor for seedling with small-size stem and unavoidable error for multi-view point cloud registration. Therefore, in this paper, we propose an accurate multi-view 3D reconstruction method for seedling using 2D image contour to constrain 3D point cloud. The rotation axis is calibrated and optimised by minimising point-to-contour distance between 2D image contour and projected exterior points from 3D point cloud. Then, to remove outliers and noise, we introduce the seedling mask of 2D image to constrained and delete projected outlier points of 3D model from corresponding view. Furthermore, we propose a residual-guided method to recognise missing region for 3D model and complete 3D model of small-size stem. Finally, we can obtain an accurate 3D model of seedling. The reconstruction accuracy is evaluated by average distance between projected contour of 3D model and 2D image contour of all views (0.3185 mm). Then, the phenotypic parameters were calculated from 3D model and the results are close to manual measurements (Plant height: R2 = 0.98, RMSE = 2.3 mm, rRMSE = 1.52%; Petioles inclination angle: R2 = 0.99, RMSE = 0.73°, rRMSE = 1.41%; Leaf area: R2 = 0.66, RMSE = 1.05 cm2, rRMSE = 7.63%; Leaf inclination angle: R2 = 0.99, RMSE = 1.01°, rRMSE = 1.72%; Stem diameter: R2 = 0.95, RMSE = 0.12 mm, rRMSE = 5.43%). Breeders can improve the selection of more resilient varieties and cultivars to different growing conditions starting from the dynamic analysis of their phenotype.

The extent of multiallelic, co-editing of LIGULELESS1 in highly polyploid sugarcane tunes leaf inclination angle and enables selection of the ideotype for biomass yield.

Sugarcane (Saccharum spp. hybrid) is a prime feedstock for commercial production of biofuel and table sugar. Optimizing canopy architecture for improved light capture has great potential for elevating biomass yield. LIGULELESS1 (LG1) is involved in leaf ligule and auricle development in grasses. Here, we report CRISPR/Cas9-mediated co-mutagenesis of up to 40 copies/alleles of the putative LG1 in highly polyploid sugarcane (2n = 100-120, x = 10-12). Next generation sequencing revealed co-editing frequencies of 7.4%-100% of the LG1 reads in 16 of the 78 transgenic lines. LG1 mutations resulted in a tuneable leaf angle phenotype that became more upright as co-editing frequency increased. Three lines with loss of function frequencies of ~12%, ~53% and ~95% of lg1 were selected following a randomized greenhouse trial and grown in replicated, multi-row field plots. The co-edited LG1 mutations were stably maintained in vegetative progenies and the extent of co-editing remained constant in field tested lines L26 and L35. Next generation sequencing confirmed the absence of potential off targets. The leaf inclination angle corresponded to light transmission into the canopy and tiller number. Line L35 displaying loss of function in ~12% of the lg1 NGS reads exhibited an 18% increase in dry biomass yield supported by a 56% decrease in leaf inclination angle, a 31% increase in tiller number, and a 25% increase in internode number. The scalable co-editing of LG1 in highly polyploid sugarcane allows fine-tuning of leaf inclination angle, enabling the selection of the ideotype for biomass yield.

New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data

Leaf area index (LAI) serves as a pivotal parameter in crop monitoring, significantly impacting agricultural applications. Empirical models are one of the commonly used methods for estimating LAI, they are often dependent on vegetation indices (VIs), predominantly derived from low-to-moderate spatial resolution satellite sensors. A critical limitation of these VIs is their tendency to saturate at elevated LAI values. Additionally, the interplay between chlorophyll content (Cab), LAI, as well as average leaf inclination angle (ALA), particularly in reflectance spectra from red to red-edge regions, has been underexplored in past research. Based on Sentinel-2 satellite, with three red-edge bands and enhanced spatio-temporal resolution, this study introduced a new three red-edge vegetation index (VI3RE), comprising NDVI3RE and CI3RE, which leverages the unique spectral characteristics of these bands and related differential response to LAI and Cab variations. We investigated VI3RE’s efficacy through a threefold approach: firstly, by conducting sensitivity analyses using noise equivalent (NE)ΔLAI ((NE) ΔLAI=1.37 and 1.59 for NDVI3RE and CI3RE; (NE)ΔLAI = 1.75 – 3.25 for other VIs) and extended Fourier amplitude sensitivity test (EFAST) (the contribution of LAI: FOI > 30 % and TOI > 40 % for VI3RE; FOI ∼ 17 % − 28 % and TOI ∼ 25 % − 34 % for other VIs, except for NDVI with TOI ∼ 50 %), we demonstrated VI3RE’s heightened sensitivity to LAI and its improved capability for seasonal LAI estimation compared to other VIs. Secondly, by comprehensively considering the simplicity, interpretability, AIC and R2 values of various models, the linear regression model was selected for analyzing the relationships between LAI and various VIs. We established that VI3RE, particularly NDVI3RE, exhibits a robust correlation with LAI, thereby enhancing LAI estimation accuracy (R2 = 0.72, RMSE = 1.39). Lastly, we applied the LAI-VI regression models to generate crop LAI imageries throughout the growing season, subsequently validating by field measured LAI data. The results affirmed VI3RE’s superior performance in seasonal LAI estimation of crops, notably during peak growth and sowing phases. We conclude that the newly formulated VI3RE offers a universal, highly precise, and reliable model for LAI prediction across various crop types and phenological stages. However, the broader application of VI3RE may be limited, due to the three red-edge bands are not commonly found in most satellite platforms. In addition, some other aspects are not considered in this study, such as phenological information, meteorological factors, sampling strategies, etc., which should be meticulously considered in the future studies.

BcBZR1 Regulates Leaf Inclination Angle in Non-Heading Chinese Cabbage (Brassica campestris ssp. chinensis Makino)

Brassinosteroids (BRs) play critical roles in plant growth by promoting cell elongation and division, leading to increased leaf inclination angles. BRASSINAZOLE RESISTANT 1 (BZR1) and BRI1-EMS-SUPPRESSOR 1 (BES1) act as transcription factors in the brassinosteroid signaling pathway and are involved in several physiological activities regulated by BRs. In this study, we identified and cloned BcBZR1 from the heitacai non-heading Chinese cabbage (NHCC) cultivar. The sequence analysis showed that the coding sequence length of BcBZR1 is 996 bp, encoding 331 amino acid residues. Subcellular localization assays showed that BcBZR1 is localized in the nucleus and cytoplasm and that BcBZR1 protein is transported to the nucleus after receiving BR signals. Compared with Col-0, the leaf inclination angle was smaller in BcBZR1-OX. The EBR treatment experiment indicated that BRs regulate the differential expression of paclobutrazol resistance1 (PRE1) and ILI1 binding bHLH1 (IBH1) in the adaxial and abaxial cells of the petiole through BZR1, thus regulating the leaf inclination angle. The bimolecular fluorescence complementation (BiFC) assay indicated that BcBZR1 interacts with C-repeat Binding Factor2 (BcCBF2) and CBF3. Taken together, our findings not only validate the function of BcBZR1 in leaf inclination angle distribution in non-heading Chinese cabbage, but also contribute to the mechanism of leaf inclination angle regulation in this species under cold stress.

Switching action modes of miR408-5p mediates auxin signaling in rice

MicroRNAs (miRNAs) play fundamental roles in many developmental and physiological processes in eukaryotes. MiRNAs in plants generally regulate their targets via either mRNA cleavage or translation repression; however, which approach plays a major role and whether these two function modes can shift remains elusive. Here, we identify a miRNA, miR408-5p that regulates AUXIN/INDOLE ACETIC ACID 30 (IAA30), a critical repressor in the auxin pathway via switching action modes in rice. We find that miR408-5p usually inhibits IAA30 protein translation, but in a high auxin environment, it promotes the decay of IAA30 mRNA when it is overproduced. We further demonstrate that IDEAL PLANT ARCHITECTURE1 (IPA1), an SPL transcription factor regulated by miR156, mediates leaf inclination through association with miR408-5p precursor promoter. We finally show that the miR156-IPA1-miR408-5p-IAA30 module could be controlled by miR393, which silences auxin receptors. Together, our results define an alternative auxin transduction signaling pathway in rice that involves the switching of function modes by miR408-5p, which contributes to a better understanding of the action machinery as well as the cooperative network of miRNAs in plants.

Transcription factor OsWRKY72 controls rice leaf angle by regulating LAZY1-mediated shoot gravitropism.

Leaf angle is a major trait of ideal architecture, which is considered to influence rice (Oryza sativa) cultivation and grain yield. Although a few mutants with altered rice leaf inclination angles have been reported, the underlying molecular mechanism remains unclear. In this study, we showed that a WRKY transcription factor gene, OsWRKY72, was highly expressed in the leaf sheath and lamina joint. Phenotypic analyses showed that oswrky72 mutants had smaller leaf angles than the wild type, while OsWRKY72 overexpression lines exhibited an increased leaf angle. This observation suggests that OsWRKY72 functions as a positive regulator, promoting the enlargement of the leaf angle. Our bioinformatics analysis identified LAZY1 as the downstream gene of OsWRKY72. Electrophoretic mobility shift assays and dual-luciferase analysis revealed that OsWRKY72 directly inhibited LAZY1 by binding to its promoter. Moreover, knocking out OsWRKY72 enhanced shoot gravitropism, which contrasted with the phenotype of lazy1 plants. These results imply that OsWRKY72 regulates the leaf angle through gravitropism by reducing the expression of LAZY1. In addition, OsWRKY72 could directly regulate the expression of other leaf angle-related genes such as FLOWERING LOCUS T-LIKE 12 (OsFTL12) and WALL-ASSOCIATED KINASE 11 (OsWAK11). Our study indicates that OsWRKY72 contributes positively to the expansion of the leaf angle by interfering with shoot gravitropism in rice.

Quantifying water-use efficiency in plant canopies with varying leaf angle and density distribution.

Variation in architectural traits related to the spatial and angular distribution of leaf area can have considerable impacts on canopy-scale fluxes contributing to water-use efficiency (WUE). These architectural traits are frequent targets for crop improvement and for improving the understanding and predictions of net ecosystem carbon and water fluxes. A three-dimensional, leaf-resolving model along with a range of virtually generated hypothetical canopies were used to quantify interactions between canopy structure and WUE by examining its response to variation of leaf inclination independent of leaf azimuth, canopy heterogeneity, vegetation density and physiological parameters. Overall, increasing leaf area index (LAI), increasing the daily-averaged fraction of leaf area projected in the sun direction (Gavg) via the leaf inclination or azimuth distribution and increasing homogeneity had a similar effect on canopy-scale daily fluxes contributing to WUE. Increasing any of these parameters tended to increase daily light interception, increase daily net photosynthesis at low LAI and decrease it at high LAI, increase daily transpiration and decrease WUE. Isolated spherical crowns could decrease photosynthesis by ~60 % but increase daily WUE ≤130 % relative to a homogeneous canopy with equivalent leaf area density. There was no observed optimum in daily canopy WUE as LAI, leaf angle distribution or heterogeneity was varied. However, when the canopy was dense, a more vertical leaf angle distribution could increase both photosynthesis and WUE simultaneously. Variation in leaf angle and density distributions can have a substantial impact on canopy-level carbon and water fluxes, with potential trade-offs between the two. These traits might therefore be viable target traits for increasing or maintaining crop productivity while using less water, and for improvement of simplified models. Increasing canopy density or decreasing canopy heterogeneity increases the impact of leaf angle on WUE and its dependent processes.

Accurate solution of the SAIL model by leaf inclination angle calculation based on laser point clouds

ABSTRACT The canopy radiative transfer model enables quantitative inversion of vegetation biophysical parameters by rapidly simulating spectral information. However, structural input parameters, such as the leaf inclination angle distribution (LAD), are often set to empirical values in classic models, leading to simulation errors. Laser point cloud technology has developed rapidly in recent years, and the 3D structure of vegetation can be detailed using laser sensors and processing software, providing the necessary conditions for accurate solutions of radiative transfer models of the canopy. However, the mechanisms for improving existing models of canopy radiative transfer by introducing laser point clouds have not yet been theoretically analyzed in detail. In this study, a precise solution was proposed by incorporating the accurate LAD obtained from laser point clouds into the intermediate function of the traditional Scattering by Arbitrarily Inclined Leaves (SAIL) model, which is a widely used multiangle radiative transfer model. The calculation of the leaf inclination angle and the derivation of the improved model were detailed in this paper. Experiments from different observation angles and bands compared the similarity between the actual measured spectra and the simulated spectra for both the traditional SAIL model and the proposed model with accurate structural parameters. The overall similarity of the proposed model with respect to the conventional SAIL model was improved, indicating that introducing laser point clouds improved the simulation accuracy. The proposed model achieved more significant improvements in the blue 450 nm and red 670 nm bands. Moreover, the similarity improvements differed between different angles. Finally, we analyzed the sensitivity of the structural parameters of the SAIL model and the proposed model using local and global sensitivity analysis methods.

HvGSK1.1 Controls Salt Tolerance and Yield through the Brassinosteroid Signaling Pathway in Barley.

Brassinosteroids (BRs) are a class of plant steroid hormones that are essential for plant growth and development. BRs control important agronomic traits and responses to abiotic stresses. Through the signaling pathway, BRs control the expression of thousands of genes, resulting in a variety of biological responses. The key effectors of the BR pathway are two transcription factors (TFs): BRASSINAZOLE RESISTANT 1 (BZR1) and BRI1-EMSSUPPRESSOR 1 (BES1). Both TFs are phosphorylated and inactivated by the Glycogen synthase kinase 3 BRASSINOSTEROID INSENSITIVE2 (BIN2), which acts as a negative regulator of the BR pathway. In our study, we describe the functional characteristics of HvGSK1.1, which is one of the GSK3/SHAGGY-like orthologs in barley. We generated mutant lines of HvGSK1.1 using CRISPR/Cas9 genome editing technology. Next Generation Sequencing (NGS) of the edited region of the HvGSK1.1 showed a wide variety of mutations. Most of the changes (frameshift, premature stop codon, and translation termination) resulted in the knock-out of the target gene. The molecular and phenotypic characteristics of the mutant lines showed that the knock-out mutation of HvGSK1.1 improved plant growth performance under salt stress conditions and increased the thousand kernel weight of the plants grown under normal conditions. The inactivation of HvGSK1.1 enhanced BR-dependent signaling, as indicated by the results of the leaf inclination assay in the edited lines. The plant traits under investigation are consistent with those known to be regulated by BRs. These results, together with studies of other GSK3 gene members in other plant species, suggest that targeted editing of these genes may be useful in creating plants with improved agricultural traits.

Leaf properties estimation enhancement over heterogeneous vegetation by correcting for terrestrial laser scanning beam divergence effect

The need for an accurate description of forest structure is increased as it is a key for monitoring carbon storage, energy, and mass exchange, and different ecosystem functions. The leaf area index (LAI), the leaf area density (ul), and the leaf inclination distribution function (LIDF) are regarded as important and critical keys allowing the characterization of the forest structure. The dependency between these properties and the heterogeneity of the forest makes their estimation challenging and delicate. In this context, despite the expanding body of works reporting forest structure properties retrieval using infinitesimal footprint size terrestrial LiDAR system (TLS) data, there seem to be few works evaluating the influence of the TLS beam divergence effect (i.e. its size increase with distance from the sensor), on the estimation accuracy, even though the influence is not negligible. In this study, we aim to jointly estimate LAI/ul and LIDF using non-infinitesimal footprint size TLS data based on forward/inverse radiative transfer (RT) modeling. Our approach assumes a uniform distribution of azimuth angles enabling us to attain valuable insights into inclination angles and leaf area density while simplifying our computational efforts. The forward modeling allows an understanding of the interaction between vegetation structure properties and TLS light beams, while the inversion allows retrieving the properties given the TLS data. The inversion technique is based on the maximum likelihood estimator (MLE). Our problem turns out to be a non-linear and high-dimensional cost function to be optimized. To tackle this complexity, the global optimization technique, the shuffled complex evolution (SCE-UA), is adapted to find the globally optimal solution. Our developed approach is validated with simulated and actual forest TLS point clouds acquired from Estonian Pine, Birch, and Spruce stands. Our findings using simulated data prove that our estimates are in agreement with the actual ul and LIDF values with (MAEul∈0.00120.0043) and (RMSEul∈0.01370.0224). In the actual case, RMSELAI for Pine, Birch and Spruce stands are 0.1066, 0.014, and 0.070, respectively. In all cases, our approach outperforms state-of-the-art techniques.

Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.

Plant sensors are commonly used in agricultural production, landscaping, and other fields to monitor plant growth and environmental parameters. As an important basic parameter in plant monitoring, leaf inclination angle (LIA) not only influences light absorption and pesticide loss but also contributes to genetic analysis and other plant phenotypic data collection. The measurements of LIA provide a basis for crop research as well as agricultural management, such as water loss, pesticide absorption, and illumination radiation. On the one hand, existing efficient solutions, represented by light detection and ranging (LiDAR), can provide the average leaf angle distribution of a plot. On the other hand, the labor-intensive schemes represented by hand measurements can show high accuracy. However, the existing methods suffer from low automation and weak leaf-plant correlation, limiting the application of individual plant leaf phenotypes. To improve the efficiency of LIA measurement and provide the correlation between leaf and plant, we design an image-phenotype-based noninvasive and efficient optical sensor measurement system, which combines multi-processes implemented via computer vision technologies and RGB images collected by physical sensing devices. Specifically, we utilize object detection to associate leaves with plants and adopt 3-dimensional reconstruction techniques to recover the spatial information of leaves in computational space. Then, we propose a spatial continuity-based segmentation algorithm combined with a graphical operation to implement the extraction of leaf key points. Finally, we seek the connection between the computational space and the actual physical space and put forward a method of leaf transformation to realize the localization and recovery of the LIA in physical space. Overall, our solution is characterized by noninvasiveness, full-process automation, and strong leaf-plant correlation, which enables efficient measurements at low cost. In this study, we validate Auto-LIA for practicality and compare the accuracy with the best solution that is acquired with an expensive and invasive LiDAR device. Our solution demonstrates its competitiveness and usability at a much lower equipment cost, with an accuracy of only 2. 5° less than that of the widely used LiDAR. As an intelligent processing system for plant sensor signals, Auto-LIA provides fully automated measurement of LIA, improving the monitoring of plant physiological information for plant protection. We make our code and data publicly available at http://autolia.samlab.cn.

Maximizing the Radiation Use Efficiency by Matching the Leaf Area and Leaf Nitrogen Vertical Distributions in a Maize Canopy: A Simulation Study.

The radiation use efficiency (RUE) is one of the most important functional traits determining crop productivity. The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in boosting the RUE from both experimental and computational evidence. However, previous simulation studies have primarily assumed that the leaf area is uniformly distributed along the canopy depth, rarely considering the optimization of the leaf area distribution, especially for C4 crops. The present study hypothesizes that the RUE may be maximized by matching the leaf area and leaf nitrogen vertical distributions in the canopy. To test this hypothesis, various virtual maize canopies were generated by combining the leaf inclination angle, vertical leaf area distribution, and vertical leaf nitrogen distribution and were further evaluated by an improved multilayer canopy photosynthesis model. We found that a greater fraction of leaf nitrogen is preferentially allocated to canopy layers with greater leaf areas to maximize the RUE. The coordination of light and nitrogen emerged as a property from the simulations to maximize the RUE in most scenarios, particularly in dense canopies. This study not only facilitates explicit and precise profiling of ideotypes for maximizing the RUE but also represents a primary step toward high-throughput phenotyping and screening of the RUE for massive numbers of inbred lines and cultivars.

Rice ILI atypical bHLH transcription factors antagonize OsbHLH157/OsbHLH158 during brassinosteroid signaling.

Brassinosteroids (BRs) are a group of steroid hormones that play crucial roles in plant growth and development. Atypical bHLH transcription factors that lack the basic region for DNA binding have been implicated in BR signaling. However, the underlying mechanisms of atypical bHLHs in regulation of rice (Oryza sativa) BR signaling are still largely unknown. Here, we describe a systematic characterization of INCREASED LEAF INCLINATION (ILI) subfamily atypical bHLH transcription factors in rice. A total of 8 members, ILI1 to ILI8, with substantial sequence similarity were retrieved. Knockout and overexpression analyses demonstrated that these ILIs play unequally redundant and indispensable roles in BR-mediated growth and development in rice, with a more prominent role for ILI4 and ILI5. The ili3/4/5/8 quadruple and ili1/3/4/7/8 quintuple mutants displayed tremendous BR-related defects with severe dwarfism, erect leaves, and sterility. Biochemical analysis showed that ILIs interact with OsbHLH157 and OsbHLH158, which are also atypical bHLHs and have no obvious transcriptional activity. Overexpression of OsbHLH157 and OsbHLH158 led to drastic BR-defective growth, whereas the osbhlh157 osbhlh158 double mutant developed a typical BR-enhanced phenotype, indicating that OsbHLH157 and OsbHLH158 play a major negative role in rice BR signaling. Further transcriptome analyses revealed opposite effects of ILIs and OsbHLH157/OsbHLH158 in regulation of downstream gene expression, supporting the antagonism of ILIs and OsbHLH157/OsbHLH158 in maintaining the balance of BR signaling. Our results provide insights into the mechanism of BR signaling and plant architecture formation in rice.

Identification of QTLs Conferring Rice Leaf Inclination Angle and Analysis of Candidate Genes

Identification of QTLs Conferring Rice Leaf Inclination Angle and Analysis of Candidate Genes

Comparative effects of drought stress on leaf gas exchange, foliar ABA and leaf orientation in four grapevine cultivars grown in Northern Italy.

Drought tolerance varies greatly across Vitis vinifera cultivars, depending on physiological responses and structural and morphological adaptations. In this study, responses to water stress were examined in three extensively cultivated varieties from Northern Italy. Over the course of two seasons, mature potted vines were subjected to a 12 or 13-day period of water restriction. Vine water relations were investigated using measures of water potential, gas exchanges, and leaf ABA content. Leaf angle response to increasing water stress was analysed in the four cultivars as a mechanism that improves stress tolerance. Different physiological responses were observed among cultivars, suggesting a near-isohydric water-use strategy for Moscato and a near-anisohydric one for Garganega, Glera and Merlot. Results of leaf ABA analysis highlighted a variability among the studied varieties, indicating higher contents and lower sensitivity to ABA for the anisohydric ones. In all varieties, a similar increase in midday leaf inclination was observed in response to decreasing stem water potentials, indicating that leaf angle adjustments may represent a common adaptive response to drought. These findings increase the understanding of the leaf physiological and structural mechanisms that contribute to water stress tolerance in grapevine, supporting a more efficient cultivar selection to cope with the expected changes in Mediterranean climate.

A method for detecting tomato canopies’ phenotypic traits based on improved skeleton extraction algorithm

Automatic acquisition of tomato canopies’ phenotypic traits is essential for tomato varieties’ selection and scientific cultivation. Due to the infinite growth characteristics of tomato, its organ development is stochastic and the canopies' internal structure of is also complex. These make it challenging to obtain organs’ detailed phenotypic traits. Thus, this work proposed a method for detecting tomato canopies’ phenotypic traits based on improved skeleton extraction algorithm (ISEA). Firstly, after collecting tomato canopies' point cloud data from multiple perspectives, this work reconstructed its three-dimensional (3D) model accurately. Secondly, the least squares method was used to simplify the spatial contraction model of the Laplace Skeleton Extraction algorithm to obtain the tomatoes’ skeleton point set. On this basic, Greedy algorithm was used to optimise the Edge Collapse algorithm to extract more accurate and reliable skeleton structure. Then, in conjunction with the canopy growth characteristics of the crop and the Intrinsic Shape Signatures (ISS) principle, the simplified skeleton structure was subjected to local principal component analysis (PCA), which achieved the separation of tomatoes’ main stem and leaves. Finally, a modelling algorithm based on Delaunay triangulation was applied to construct the separated organs’ model to calculate phenotypic traits such as stem diameter, leaf area index and average leaf inclination. The calculated results were also compared with the measured values at different growth stages for performance evaluation. The average precision, average recall, average accuracy and micro F1 score of ISEA were 0.9144, 0.7751, 0.7243 and 0.8306, respectively. The overall R2 between calculated values and measured values for stem diameter, leaf area index and average leaf inclination were 0.9638, 0.9067, and 0.9428, and the root mean square errors (RMSE) were 0.3922, 0.0029, and 0.0186, respectively. As a result, the proposed method can extract a simple skeleton from the complex tomato canopy and calculate phenotypic traits accurately. It also provided solid technical support for studying the interaction about “genotype-phenotype-envirotype” in tomato.