Canopy occupation volume as an indicator of canopy photosynthetic capacity

Three-Dimensional Leaf Edge Reconstruction Combining Two- and Three-Dimensional Approaches

Leaves, crucial for plant physiology, exhibit various morphological traits that meet diverse functional needs. Traditional leaf morphology quantification, largely 2-dimensional (2D), has not fully captured the 3-dimensional (3D) aspects of leaf function. Despite improvements in 3D data acquisition, accurately depicting leaf morphologies, particularly at the edges, is difficult. This study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction. Utilizing deep-learning-based instance segmentation for 2D edge detection, structure from motion for estimation of camera positions and orientations, leaf correspondence identification for matching leaves among images, and curve-based 3D reconstruction for estimating 3D curve fragments, the method assembles 3D curve fragments into a leaf edge model through B-spline curve fitting. The method's performances were evaluated on both virtual and actual leaves, and the results indicated that small leaves and high camera noise pose greater challenges to reconstruction. We developed guidelines for setting a reliability threshold for curve fragments, considering factors occlusion, leaf size, the number of images, and camera error; the number of images had a lesser impact on this threshold compared to others. The method was effective for lobed leaves and leaves with fewer than 4 holes. However, challenges still existed when dealing with morphologies exhibiting highly local variations, such as serrations. This nondestructive approach to 3D leaf edge reconstruction marks an advancement in the quantitative analysis of plant morphology. It is a promising way to capture whole-plant architecture by combining 2D and 3D phenotyping approaches adapted to the target anatomical structures.

Modern phenomics to empower holistic crop science, agronomy, and breeding research

Crop phenomics enables collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared to the traditional measurements. Most of modern crop phenomics use different sensors to collect reflective, emitted and fluorescence signals etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

Histological and single-nucleus transcriptome analyses reveal the specialized functions of ligular sclerenchyma cells and key regulators of leaf angle in maize

Leaf angle (LA) is a crucial factor that affects planting density and yield in maize. However, the regulatory mechanisms underlying LA formation remain largely unknown. In this study, we performed a comparative histological analysis of the ligular region across various maize inbred lines and revealed that LA is significantly influenced by a two-step regulatory process involving initial cell elongation followed by subsequent lignification in the ligular adaxial sclerenchyma cells (SCs). Subsequently, we performed both bulk and single-nucleus RNA sequencing, generated a comprehensive transcriptomic atlas of the ligular region, and identified numerous genes enriched in the hypodermal cells that may influence their specialization into SCs. Furthermore, we functionally characterized two genes encoding atypical basic-helix-loop-helix (bHLH) transcription factors, bHLH30 and its homolog bHLH155, which are highly expressed in the elongated adaxial cells. Genetic analyses revealed that bHLH30 and bHLH155 positively regulate LA expansion, and molecular experiments demonstrated their ability to activate the transcription of genes involved in cell elongation and lignification of SCs. These findings highlight the specialized functions of ligular adaxial SCs in LA regulation by restricting further extension of ligular cells and enhancing mechanical strength. The transcriptomic atlas of the ligular region at single-nucleus resolution not only deepens our understanding of LA regulation but also enables identification of numerous potential targets for optimizing plant architecture in modern maize breeding.

Effects of narrow-wide row planting patterns on canopy photosynthetic characteristics, bending resistance and yield of soybean in maize‒soybean intercropping systems

With the improvements in mechanization levels, it is difficult for the traditional intercropping planting patterns to meet the needs of mechanization. In the traditional maize‒soybean intercropping, maize has a shading effect on soybean, which leads to a decrease in soybean photosynthetic capacity and stem bend resistance, resulting in severe lodging, which greatly affects soybean yield. In this study, we investigated the effects of three intercropping ratios (four rows of maize and four rows of soybean; four rows of maize and six rows of soybean; six rows of maize and six rows of soybean) and two planting patterns (narrow-wide row planting pattern of 80-50 cm and uniform-ridges planting pattern of 65 cm) on soybean canopy photosynthesis, stem bending resistance, cellulose, hemicellulose, lignin and related enzyme activities. Compared with the uniform-ridge planting pattern, the narrow-wide row planting pattern significantly increased the LAI, PAR, light transmittance and compound yield by 6.06%, 2.49%, 5.68% and 5.95%, respectively. The stem bending resistance and cellulose, hemicellulose, lignin and PAL, TAL and CAD activities were also significantly increased. Compared with those under the uniform-ridge planting pattern, these values increased by 7.74%, 3.04%, 8.42%, 9.76%, 7.39%, 10.54% and 8.73% respectively. Under the three intercropping ratios, the stem bending resistance, cellulose, hemicellulose, lignin content and PAL, TAL, and CAD activities in the M4S6 treatment were significantly greater than those in the M4S4 and M6S6 treatments. Compared with the M4S4 treatment, these variables increased by 12.05%, 11.09%, 21.56%, 11.91%, 18.46%, 16.1%, and 16.84%, respectively, and compared with the M6S6 treatment, they increased by 2.06%, 2.53%, 2.78%, 2.98%, 8.81%, 4.59%, and 4.36%, respectively. The D-M4S6 treatment significantly improved the lodging resistance of soybean and weakened the negative impact of intercropping on soybean yield. Therefore, based on the planting pattern of narrow-wide row maize‒soybean intercropping planting pattern, four rows of maize and six rows of soybean were more effective at improving the lodging resistance of soybean in the semiarid region of western China.

From leaf to multiscale models of photosynthesis: applications and challenges for crop improvement

To keep up with the growth of human population and to circumvent deleterious effects of global climate change, it is essential to enhance crop yield to achieve higher production. Here we review mathematical models of oxygenic photosynthesis that are extensively used, and discuss in depth a subset that accounts for diverse approaches providing solutions to our objective. These include models (1) to study different ways to enhance photosynthesis, such as fine-tuning antenna size, photoprotection and electron transport; (2) to bioengineer carbon metabolism; and (3) to evaluate the interactions between the process of photosynthesis and the seasonal crop dynamics, or those that have included statistical whole-genome prediction methods to quantify the impact of photosynthesis traits on the improvement of crop yield. We conclude by emphasizing that the results obtained in these studies clearly demonstrate that mathematical modelling is a key tool to examine different approaches to improve photosynthesis for better productivity, while effective multiscale crop models, especially those that also include remote sensing data, are indispensable to verify different strategies to obtain maximized crop yields.

Combining Multiple Plant Attributes to Reveal Differences in Community Structure in Two Distant Deserts in Central Asia

International interest is growing in biodiversity conservation and sustainable use in drylands. Desert ecosystems across arid Central Asia are severely affected by global change. Understanding the changes in a plant community is an essential prerequisite to revealing the community assembly mechanism, vegetation conservation, and management. The knowledge of large-scale spatial variation in plant community structure in different Central Asian deserts is still limited. In this study, we selected the Taukum (TD, Kazakhstan) and the Gurbantunggut (GD, China) deserts as the research area, with similar latitudes despite being nearly 1000 km apart. Thirteen and 15 sampling plots were set up and thoroughly investigated. The differences in community structure depending on multiple plant attributes (individual level: plant height, canopy diameter, and plant volume, and community level: plant density, total cover, and total volume) were systematically studied. TD had a better overall environmental status than GD. A total of 113 species were found, with 68 and 74 in TD and GD, respectively. The number of species and plant attributes was unequally distributed across different families and functional groups between deserts. The values of several plant attributes, such as ephemerals, annuals, dicotyledons, and shrubs with assimilative branches in GD, were significantly lower than those in TD. The Motyka indices of six plant attributes (26.18-38.61%) were higher between the two deserts than the species similarity index (20.4%), indicating a more robust convergence for plant functional attributes. The community structures in the two deserts represented by different plant attribute matrices demonstrated irregular differentiation patterns in ordination diagrams. The most variance in community structure was attributed to soil and climatic factors, while geographic factors had the smallest proportion. Consequently, the community structures of the two distant deserts were both different and similar to an extent. This resulted from the long-term impacts of heterogeneous environments within the same region. Our knowledge is further deepened by understanding the variation in community structure in different deserts on a large spatial scale. This therefore provides valuable insights into conserving regional biodiversity in Central Asia.

The Importance of Using Realistic 3D Canopy Models to Calculate Light Interception in the Field

Quantifying canopy light interception provides insight into the effects of plant spacing, canopy structure, and leaf orientation on radiation distribution. This is essential for increasing crop yield and improving product quality. Canopy light interception can be quantified using 3-dimensional (3D) plant models and optical simulations. However, virtual 3D canopy models (VCMs) have often been used to quantify canopy light interception because realistic 3D canopy models (RCMs) are difficult to obtain in the field. This study aims to compare the differences in light interception between VCMs and RCM. A realistic 3D maize canopy model (RCM) was reconstructed over a large area of the field using an advanced unmanned aerial vehicle cross-circling oblique (CCO) route and the structure from motion-multi-view stereo method. Three types of VCMs (VCM-1, VCM-4, and VCM-8) were then created by replicating 1, 4, and 8 individual realistic plants constructed by CCO in the center of the corresponding RCM. The daily light interception per unit area (DLI), as computed for the 3 VCMs, exhibited marked deviation from the RCM, as evinced by the relative root mean square error (rRMSE) values of 20.22%, 17.38%, and 15.48%, respectively. Although this difference decreased as the number of plants used to replicate the virtual canopy increased, rRMSE of DLI for VCM-8 and RCM still reached 15.48%. It was also found that the difference in light interception between RCMs and VCMs was substantially smaller in the early stage (48 days after sowing [DAS]) than in the late stage (70 DAS). This study highlights the importance of using RCM when calculating light interception in the field, especially in the later growth stages of plants.

Quantifying Contributions of Different Factors to Canopy Photosynthesis in 2 Maize Varieties: Development of a Novel 3D Canopy Modeling Pipeline

Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate (Ac) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in Ac between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in Ac (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with Ac and plant architecture traits were non-linearly related to Ac. This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.

How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques

Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images

The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy. Leaves orientation is an important architectural trait determining maize canopies light interception. Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition. The goal of the present study is 2-fold: firstly, to propose and validate an automatic algorithm (Automatic Leaf Azimuth Estimation from Midrib detection [ALAEM]) based on leaves midrib detection in vertical red green blue (RGB) images to describe leaves orientation at the canopy level; and secondly, to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities (6 and 12 plants.m^-2) and 2 row spacing (0.4 and 0.8 m) over 2 different sites in southern France. The ALAEM algorithm was validated against in situ annotations of leaves orientation, showing a satisfactory agreement (root mean square [RMSE] error = 0.1, R² = 0.35) in the proportion of leaves oriented perpendicular to rows direction across sowing patterns, genotypes, and sites. The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition. In both experiments, a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1 (6 plants.m^-2, 0.4 m row spacing) towards 8 (12 plants.m^-2, 0.8 m row spacing). Significant differences among the 5 cultivars were found, with 2 hybrids exhibiting, systematically, a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity. Differences in leaves orientation were also found between experiments in a squared sowing pattern (6 plants.m^-2, 0.4 m row spacing), indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low.

Disentangling the Heterosis in Biomass Production and Radiation Use Efficiency in Maize: A Phytomer-Based 3D Modelling Approach

Maize (Zea mays L.) benefits from heterosis in-yield formation and photosynthetic efficiency through optimizing canopy structure and improving leaf photosynthesis. However, the role of canopy structure and photosynthetic capacity in determining heterosis in biomass production and radiation use efficiency has not been separately clarified. We developed a quantitative framework based on a phytomer-based three-dimensional canopy photosynthesis model and simulated light capture and canopy photosynthetic production in scenarios with and without heterosis in either canopy structure or leaf photosynthetic capacity. The accumulated above-ground biomass of Jingnongke728 was 39% and 31% higher than its male parent, Jing2416, and female parent, JingMC01, while accumulated photosynthetically active radiation was 23% and 14% higher, correspondingly, leading to an increase of 13% and 17% in radiation use efficiency. The increasing post-silking radiation use efficiency was mainly attributed to leaf photosynthetic improvement, while the dominant contributing factor differs for male and female parents for heterosis in post-silking yield formation. This quantitative framework illustrates the potential to identify the key traits related to yield and radiation use efficiency and helps breeders to make selections for higher yield and photosynthetic efficiency.

Linking genetic markers with an eco-physiological model to pyramid favourable alleles and design wheat ideotypes

Genetic markers can be linked with eco-physiological crop models to accurately predict genotype performance and individual markers' contributions in target environments, exploring interactions between genotype and environment. Here, wheat (Triticum aestivum L.) yield was dissected into seven traits corresponding to cultivar genetic coefficients in an eco-physiological model. Loci for these traits were discovered through the genome-wide association studies (GWAS). The cultivar genetic coefficients were derived from the loci using multiple linear regression or random forest, building a marker-based eco-physiological model. It is then applied to simulate wheat yields and design virtual ideotypes. The results indicated that the loci identified through GWAS explained 46%-75% variations in cultivar genetic coefficients. Using the marker-based model, the normalized root mean square error (nRMSE) between the simulated yield and observed yield was 13.95% by multiple linear regression and 13.62% by random forest. The nRMSE between the simulated and observed maturity dates was 1.24% by multiple linear regression and 1.11% by random forest, respectively. Structural equation modelling indicated that variations in grain yield could be well explained by cultivar genetic coefficients and phenological data. In addition, 24 pleiotropic loci in this study were detected on 15 chromosomes. More significant loci were detected by the model-based dissection method than considering yield per se. Ideotypes were identified by higher yield and more favourable alleles of cultivar genetic traits. This study proposes a genotype-to-phenotype approach and demonstrates novel ideas and tools to support the effective breeding of new cultivars with high yield through pyramiding favourable alleles and designing crop ideotypes.

Characteristics of photosynthesis and vertical canopy architecture of citrus trees under two labor-saving cultivation modes using unmanned aerial vehicle (UAV)-based LiDAR data in citrus orchards

Analyzing and comparing the effects of labor-saving cultivation modes on photosynthesis, as well as studying their vertical canopy architecture, can improve the tree structure of high-quality and high-yield citrus and selection of labor-saving cultivation modes. The photosynthesis of 1080 leaves of two labor-saving cultivation modes (wide-row and narrow-plant mode and fenced mode) comparing with the traditional mode were measured, and nitrogen content of all leaves and photosynthetic nitrogen use efficiency (PNUE) were determined. Unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) data were used to assess the vertical architecture of three citrus cultivation modes. Results showed that for the wide-row and narrow-plant and traditional modes leaf photosynthetic CO₂ assimilation rate, stomatal conductance, and transpiration rate of the upper layer were significantly higher than those of the middle layer, and values of the middle layer were markedly higher than those of the lower layer. In the fenced mode, a significant difference in photosynthetic factors between the upper and middle layers was not observed. A vertical canopy distribution had a more significant effect on PNUE in the traditional mode. Leaves in the fenced mode had distinct photosynthetic advantages and higher PNUE. UAV-based LiDAR data effectively revealed the differences in the vertical canopy architecture of citrus trees by enabling calculating the density and height percentile of the LiDAR point cloud. The point cloud densities of three cultivation modes were significantly different for all LiDAR density slices, especially at higher canopy heights. The labor-saving modes, particularly the fenced mode, had significantly higher height percentile data.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

Three-dimensional reconstruction and phenotype measurement of maize seedlings based on multi-view image sequences

As an important method for crop phenotype quantification, three-dimensional (3D) reconstruction is of critical importance for exploring the phenotypic characteristics of crops. In this study, maize seedlings were subjected to 3D reconstruction based on the imaging technology, and their phenotypic characters were analyzed. In the first stage, a multi-view image sequence was acquired via an RGB camera and video frame extraction method, followed by 3D reconstruction of maize based on structure from motion algorithm. Next, the original point cloud data of maize were preprocessed through Euclidean clustering algorithm, color filtering algorithm and point cloud voxel filtering algorithm to obtain a point cloud model of maize. In the second stage, the phenotypic parameters in the development process of maize seedlings were analyzed, and the maize plant height, leaf length, relative leaf area and leaf width measured through point cloud were compared with the corresponding manually measured values, and the two were highly correlated, with the coefficient of determination (R ²) of 0.991, 0.989, 0.926 and 0.963, respectively. In addition, the errors generated between the two were also analyzed, and results reflected that the proposed method was capable of rapid, accurate and nondestructive extraction. In the third stage, maize stem leaves were segmented and identified through the region growing segmentation algorithm, and the expected segmentation effect was achieved. In general, the proposed method could accurately construct the 3D morphology of maize plants, segment maize leaves, and nondestructively and accurately extract the phenotypic parameters of maize plants, thus providing a data support for the research on maize phenotypes.

High-throughput field crop phenotyping: current status and challenges

In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.