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• Leaf angle and leaf area index together influence canopy light interception and canopy photosynthesis. However, so far, there is no effective method to identify the optimal combination of these two parameters for canopy photosynthesis. • In this study, first a robust high-throughput method for accurate segmentation of maize organs based on 3D point clouds data was developed, then the segmented plant organs were used to generate new 3D point clouds for the canopy of altered architectures. With this, we simulated the synergistic effect of leaf area and leaf angle on canopy photosynthesis. • The results show that, compared to the traditional parameters describing the canopy photosynthesis including leaf area index, facet angle and canopy coverage, a new parameter – the canopy occupation volume (COV) – can better explain the variations of canopy photosynthetic capacity. Specifically, COV can explain > 79% variations of canopy photosynthesis generated by changing leaf angle and > 84% variations of canopy photosynthesis generated by changing leaf area. • As COV can be calculated in a high-throughput manner based on the canopy point clouds, it can be used to evaluate canopy architecture in breeding and agronomic research.

Summary Light interception and radiation‐use efficiency (RUE) are essential components of plant performance. Their genetic dissections require novel high‐throughput phenotyping methods. We have developed a suite of methods to evaluate the spatial distribution of incident light, as experienced by hundreds of plants in a glasshouse, by simulating sunbeam trajectories through glasshouse structures every day of the year; the amount of light intercepted by maize (Zea mays) plants via a functional–structural model using three‐dimensional (3D) reconstructions of each plant placed in a virtual scene reproducing the canopy in the glasshouse; and RUE, as the ratio of plant biomass to intercepted light. The spatial variation of direct and diffuse incident light in the glasshouse (up to 24%) was correctly predicted at the single‐plant scale. Light interception largely varied between maize lines that differed in leaf angles (nearly stable between experiments) and area (highly variable between experiments). Estimated RUEs varied between maize lines, but were similar in two experiments with contrasting incident light. They closely correlated with measured gas exchanges. The methods proposed here identified reproducible traits that might be used in further field studies, thereby opening up the way for large‐scale genetic analyses of the components of plant performance.

• Phenology is an important indicator of environmental variation and climate change impacts on tree responses. In conifers, monitoring phenology of photosynthesis through remote sensing has been unreliable, because needle foliage varies little throughout the year. This is challenging for modelling ecosystem carbon uptake and monitoring phenology for enhanced breeding (genomic selection) and forest health. • Here, we demonstrate that drone-based carotenoid-sensitive spectral indices, such as the Chl/carotenoid index (CCI), can be used to track phenology in conifers by taking advantage of the close relationship between seasonally changing carotenoid levels and the variation of photosynthetic activity. • Physiological ground measurements, including photosynthetic pigments and maximum quantum yield of Chl fluorescence, indicated that CCI tracked the variation of photosynthetic activity better than other vegetation indices for 30 white spruce seedlings measured over 1 yr. A machine-learning approach, using CCI derived from drone-based multispectral imagery, was used to model phenology of photosynthesis for the entire pedigree population (6000 seedlings). • This high-throughput drone-based phenotyping approach is suitable for studying climate change impacts and environmental variation on the physiological status of thousands of field-grown conifers at unprecedented speed and scale.

Droughts cause severe crop losses worldwide and climate change is projected to increase their prevalence in the future. Similar to the situation for many crops, the reference plant Arabidopsis thaliana (Ath) is considered drought-sensitive, whereas, as we demonstrate, its close relatives Arabidopsis lyrata (Aly) and Eutrema salsugineum (Esa) are drought-resistant. To understand the molecular basis for this plasticity we conducted a deep phenotypic, biochemical and transcriptomic comparison using developmentally matched plants. We demonstrate that Aly responds most sensitively to decreasing water availability with early growth reduction, metabolic adaptations and signaling network rewiring. By contrast, Esa is in a constantly prepared mode as evidenced by high basal proline levels, ABA signaling transcripts and late growth responses. The stress-sensitive Ath responds later than Aly and earlier than Esa, although its responses tend to be more extreme. All species detect water scarcity with similar sensitivity; response differences are encoded in downstream signaling and response networks. Moreover, several signaling genes expressed at higher basal levels in both Aly and Esa have been shown to increase water-use efficiency and drought resistance when overexpressed in Ath. Our data demonstrate contrasting strategies of closely related Brassicaceae to achieve drought resistance.

Summary The dissection of genetic architecture for rice root system is largely dependent on phenotyping techniques, and high‐throughput root phenotyping poses a great challenge. In this study, we established a cost‐effective root phenotyping platform capable of analysing 1680 root samples within 2 h. To efficiently process a large number of root images, we developed the root phenotyping toolbox (RPT) with an enhanced SegFormer algorithm and used it for root segmentation and root phenotypic traits. Based on this root phenotyping platform and RPT, we screened 18 candidate (quantitative trait loci) QTL regions from 219 rice recombinant inbred lines under drought stress and validated the drought‐resistant functions of gene OsIAA8 identified from these QTL regions. This study confirmed that RPT exhibited a great application potential for processing images with various sources and for mining stress‐resistance genes of rice cultivars. Our developed root phenotyping platform and RPT software significantly improved high‐throughput root phenotyping efficiency, allowing for large‐scale root trait analysis, which will promote the genetic architecture improvement of drought‐resistant cultivars and crop breeding research in the future.

Summary A major challenge of plant biology is to unravel the genetic basis of complex traits. We took advantage of recent technical advances in high‐throughput phenotyping in conjunction with genome‐wide association studies to elucidate genotype–phenotype relationships at high temporal resolution. A diverse Brassica napus population from a commercial breeding programme was analysed by automated non‐invasive phenotyping. Time‐resolved data for early growth‐related traits, including estimated biovolume, projected leaf area, early plant height and colour uniformity, were established and complemented by fresh and dry weight biomass. Genome‐wide SNP array data provided the framework for genome‐wide association analyses. Using time point data and relative growth rates, multiple robust main effect marker–trait associations for biomass and related traits were detected. Candidate genes involved in meristem development, cell wall modification and transcriptional regulation were detected. Our results demonstrate that early plant growth is a highly complex trait governed by several medium and many small effect loci, most of which act only during short phases. These observations highlight the importance of taking the temporal patterns of QTL/allele actions into account and emphasize the need for detailed time‐resolved analyses to effectively unravel the complex and stage‐specific contributions of genes affecting growth processes that operate at different developmental phases.

Summary Rapeseed is the second most important oil crop species and is widely cultivated worldwide. However, overcoming the ‘phenotyping bottleneck’ has remained a significant challenge. A clear goal of high‐throughput phenotyping is to bridge the gap between genomics and phenomics. In addition, it is important to explore the dynamic genetic architecture underlying rapeseed plant growth and its contribution to final yield. In this work, a high‐throughput phenotyping facility was used to dynamically screen a rapeseed intervarietal substitution line population during two growing seasons. We developed an automatic image analysis pipeline to quantify 43 dynamic traits across multiple developmental stages, with 12 time points. The time‐resolved i‐traits could be extracted to reflect shoot growth and predict the final yield of rapeseed. Broad phenotypic variation and high heritability were observed for these i‐traits across all developmental stages. A total of 337 and 599 QTLs were identified, with 33.5% and 36.1% consistent QTLs for each trait across all 12 time points in the two growing seasons, respectively. Moreover, the QTLs responsible for yield indicators colocalized with those of final yield, potentially providing a new mechanism of yield regulation. Our results indicate that high‐throughput phenotyping can provide novel insights into the dynamic genetic architecture of rapeseed growth and final yield, which would be useful for future genetic improvements in rapeseed.

Bioenergy will be one component of a suite of alternatives to fossil fuels. Effective conversion of biomass to energy will require the careful pairing of advanced conversion technologies with biomass feedstocks optimized for the purpose. Lignocellulosic biomass can be converted to useful energy products via two distinct pathways: enzymatic or thermochemical conversion. The thermochemical pathways are reviewed and potential biotechnology or breeding targets to improve feedstocks for pyrolysis, gasification, and combustion are identified. Biomass traits influencing the effectiveness of the thermochemical process (cell wall composition, mineral and moisture content) differ from those important for enzymatic conversion and so properties are discussed in the language of biologists (biochemical analysis) as well as that of engineers (proximate and ultimate analysis). We discuss the genetic control, potential environmental influence, and consequences of modification of these traits. Improving feedstocks for thermochemical conversion can be accomplished by the optimization of lignin levels, and the reduction of ash and moisture content. We suggest that ultimate analysis and associated properties such as H:C, O:C, and heating value might be more amenable than traditional biochemical analysis to the high-throughput necessary for the phenotyping of large plant populations. Expanding our knowledge of these biomass traits will play a critical role in the utilization of biomass for energy production globally, and add to our understanding of how plants tailor their composition with their environment.

The description of biological objects, such as seeds, mainly relies on manual measurements of few characteristics, and on visual classification of structures, both of which can be subjective, error prone and time‐consuming. Image analysis tools offer means to address these shortcomings, but we currently lack a method capable of automatically handling seeds from different taxa with varying morphological attributes and obtaining interpretable results. Here, we provide a simple image acquisition and processing protocol and introduce Traitor, an open‐source software available as a command‐line interface (CLI), which automates the extraction of seed morphological traits from images. The workflow for trait extraction consists of scanning seeds against a high‐contrast background, correcting image colours, and analysing images with the software. Traitor is capable of processing hundreds of images of varied taxa simultaneously with just three commands, and without a need for training, manual fine‐tuning or thresholding. The software automatically detects each object in the image and extracts size measurements, traditional morphometric descriptors widely used by scientists and practitioners, standardised shape coordinates, and colorimetric measurements. The method was tested on a dataset comprising of 91,667 images of seeds from 1228 taxa. Traitor's extracted average length and width values closely matched the average manual measurements obtained from the same collection (concordance correlation coefficient of 0.98). Further, we used a large image dataset to demonstrate how Traitor's output can be used to obtain representative seed colours for taxa, determine the phylogenetic signal of seed colour, and build objective classification categories for shape with high levels of visual interpretability. Our approach increases productivity and allows for large‐scale analyses that would otherwise be unfeasible. Traitor enables the acquisition of data that are readily comparable across different taxa, opening new avenues to explore functional relevance of morphological traits and to advance on new tools for seed identification.

Plant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA). Encompassing a spectrum from molecular biology to ecosystem‐level studies, it employs high‐throughput phenotyping (HTP) approaches to quickly evaluate characteristics and enhance the yields of crops in smart plant facilities. HTP uses environmental parameters for accuracy, such as software sensors, as well as hyperspectral imaging for pigment data, thermal imaging for water content, and fluorescence imaging for photosynthesis rates. They provide information on growth kinetics, physiological and biochemical characteristics, and genotype–environment interaction. Artificial intelligence (AI) and machine learning (ML) are used on a large volume of phenotypic data to predict growth rates, determine the optimal time to water plants, or detect diseases, nutrient deficiencies, or pests at an early stage. The lighting used in smart plant factories is adjusted based on the specific growth phase of the plants, such as using different light intensities, spectrums, and durations for germination, vegetative growth, and flowering stages, hydroponics as the method of providing nutrients, and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) for improving certain characteristics, such as resistance to drought. These systems enhance crop production, yields, adaptability, and input use by optimizing the environment and utilizing precision breeding techniques. Plant phenomics with AI is a combination of several disciplines, promoting the understanding of plant–environment interactions in relation to agriculture problems such as resource use, diseases, and climate change. It affects their capacity to develop crops that capture inputs, minimize chemical application, and are resilient to climate change. Phenomics is cost‐effective, reduces inputs, and contributes to more sustainable agricultural practices, being economically and environmentally sound. Altogether, plant phenomics is central to CEA due to its capacity to capitalize on phenotypic data and genetic potential within agriculture to advance sustainability and food security. Through phenomic research, the next advancements are likely to be even more revolutionary in terms of agricultural practices and food systems worldwide.