A clumped canopy architecture can increase crop yields while reducing N2O emissions.

Understanding yield improvement in horticultural systems depends on elucidating how multiple plant traits operate in concert to sustain productivity. Mulberry (Morus alba L.) provides a suitable model for examining such whole-plant integration. Under cold-region field conditions, a modern high-yield cultivar (‘Nongsang 14’) was compared with a traditional cultivar (‘Lusang 1’). Measurements encompassed canopy architecture, biomass allocation between roots and shoots, leaf economic traits, and gas-exchange parameters, allowing trait coordination to be evaluated across structural and physiological dimensions. Multivariate profiling—Principal Component Analysis (PCA) and correlation networks—was used to characterise phenotypic integration. The modern cultivar’s superior productivity emerged as a coordinated “acquisitive” trait syndrome. This strategy couples a larger canopy (higher LAI) and nitrogen-rich foliage (higher LNC) with greater stomatal conductance (Gs), operating together with reduced root-to-shoot allocation. These features form a tightly connected network where structural investment and physiological upregulation are synchronised to maximise carbon gain. These findings provide a whole-plant framework for interpreting high productivity, offering guidance for breeding programmes that target trait integration rather than single-trait optimisation.

Wood apple is a hardy yet underutilized fruit tree of the Indian subcontinent, valued for its nutritional, medicinal, and ecological significance. Despite its potential as a climate-resilient fruit species, the determinants of yield variability remain poorly characterized. This study aimed to quantify how morphometric descriptors of canopy architecture, floral, and fruit traits explain yield variation across 62 wood apple genotypes. By integrating multivariate statistics with explainable machine-learning models (Random Forest + SHAP), we provide the first data-driven framework for identifying trait combinations that govern productivity in this underutilized tree species. The approach offers a novel, interpretable path toward ideotype selection and precision orchard design. Extensive morphometric variability was observed across the 62 genotypes for vegetative, foliar, floral, fruit and seed traits, indicating a broad genetic base. Yield per tree ranged widely from 35 to 127kg, with a mean of 75kg tree⁻¹. Principal Component Analysis (PCA) showed that canopy architecture, branch traits, and leaf-fruit attributes collectively explained 31.1% of the total variation. Correlation analysis revealed positive associations of yield with tree shape, pulp colour, and fruit-bearing tendency, whereas ornamental fruit traits and excessive spine density were negatively related. The optimized Random Forest (RF) model achieved strong predictive performance on the test dataset (R² = 0.84; RMSE = 9.45kg; MAE = 7.12kg), significantly outperforming Multiple Linear Regression (R² = 0.62), Support Vector Regression (R² = 0.76), and the Deep Learning (MLP) model (R² = 0.71). RF identified tree shape (16%), open flower colour (11.3%), and pulp colour (9.0%) as the most influential predictors of yield. SHAP analysis further clarified the non-linear and interactive effects among traits, highlighting the combined influence of canopy vigour, reproductive efficiency, and fruit-quality attributes on productivity. Hierarchical clustering grouped the genotypes into three clusters, with Cluster 2 characterized by compact canopies, superior reproductive traits, and desirable pulp features showing the highest and most stable yield (mean 84.6kg tree⁻¹). Cluster 0 displayed moderate-to-high yields (79.7kg tree⁻¹) but with greater variability, while Cluster 1 comprised the lowest-yielding genotypes (70.4kg tree⁻¹). These findings confirm that productivity in wood apple is jointly regulated by architectural and reproductive traits through coordinated source-sink dynamics. Wood apple yield is governed by an integrated suite of architectural and reproductive traits, rather than single descriptors. Genotypes with compact canopies, regular bearing habit, and consumer-preferred pulp characteristics emerge as promising ideotypes for high productivity and orchard efficiency. By combining Random Forest and SHAP, this study demonstrates the practical value of explainable machine-learning tools in identifying actionable trait combinations and providing a robust, trait-based framework to support data-driven breeding and climate-smart orchard design in underutilized perennial fruit crops.

Modern soybean (Glycine max) varieties have higher than optimal leaf area index (LAI), which could divert resources from reproductive growth. Altering leaf shape could be a simple strategy to reduce LAI. To test this, we developed 204 near-isogenic lines differing in leaf morphology by introgressing the ln allele that confers the narrow-leaf trait from two donor parents (PI 612713A and PI 547745) into the elite, broad-leaf cultivar LD11-2170. We evaluated the lines across two locations and two row spacings (38-cm and 76-cm) to assess how reduced investment in leaf area influences canopy architecture, crop physiology, and yield. Narrow-leaf lines showed 13% lower peak LAI and 3% lower digital biomass compared to broad-leaf counterparts yet maintained yield parity (5,756 vs. 5.801 kg ha-1, p = 0.43) across environmental conditions. Photosynthetic capacity remained largely unchanged, with narrow-leaf lines showing modest increases in electron transport rate and leaf mass per area. Narrow-leaf lines achieved similar canopy closure timing despite lower LAI, suggesting architectural compensation mechanisms. The most striking difference appeared in seed packaging, with 34% of pods containing four seeds in narrow-leaf lines compared to only 1.8% in broad-leaf lines. There was a nonlinear relationship between peak LAI and yield, with optimal LAI values of 9-11 varying by environment. These findings show that the single-gene GmJAG1-controlled narrow-leaf trait offers a tractable strategy for reducing LAI and maintaining high productivity. This could reduce the metabolic costs associated with excessive canopy development and support sustainable agriculture under increasing climate variability.

A stable QTL for petiole length, qPL6, was mapped in a RIL population over three consecutive growing seasons, with two candidate genes identified through integrated RNA-seq analysis. Petiole length is a critical determinant of canopy architecture in soybean (Glycine max (L.) Merr.), directly modulating yield potential through its effects on photosynthetic efficiency. Consequently, identifying genes controlling petiole length is essential for developing an ideal plant architecture adapted to high-density planting, ultimately increasing yield per unit area. Here we identified a stable quantitative trait locus (QTL) for petiole length on chromosome 6, designated qPL6, across three consecutive growing seasons, which explained 6.13-19.42% of the phenotypic variance. By comparing the longitudinal anatomical structures of petioles from Qi Huang No34 (QH34) and Ji Dou No17 (JD17), we determined that the difference in petiole length was attributed to variations in parenchyma cell lengths. Through RNA-seq analysis of two near-isogenic lines (NILs), we identified 90 differential expressed genes (DEGs) common to the upper, middle and lower petioles. These DEGs were significantly enriched in GO terms related to hormone signaling pathways and cell wall organization. By integrating analysis of sequence variations with transcriptional profiles, we selected two candidate genes, Glyma.06G258000 and Glyma.06G260800, both implicated in the auxin-responsive pathway. Glyma.06G258000 showed differential expression in the petiole, pulvinus and leaf, and carried a 626-bp InDel located 737bp upstream of its coding region. Glyma.06G260800 contained two SNPs in its second exon that induced two nonsynonymous mutations. The novel QTL and candidate genes identified in this study offer valuable genetic resources for soybean molecular breeding aimed at optimizing plant architecture and increasing yield.

Monitoring tree cover and canopy architecture is important for sustainable forest management, biodiversity assessments, and climate adaptation planning. However, most current methods rely on large labeled datasets or specific sensors, which limit scalability and adaptability. This study hypothesizes that a zero-shot explainable multi-agent system can successfully detect and segment trees from standard RGB satellite imagery without having to retrain on task-specific data. A new framework is proposed that combines YOLO11m for tree detection and SAM2 for crown segmentation. The system utilizes a combination of vegetation, edge, and color-based agents that work in concert under an IoU based fusion strategy to increase robustness under varying brightness, shadows, and canopy overlap. Explainability includes Grad-CAM, SHAP, and LIME-based agents to visualize model attention to establish user trust. Experiments were conducted on a dataset of 2400 high-resolution satellite imagery (0.5–1.5 m). Moreover, the framework produced a 97.3% overall accuracy score, 97.6% precision score, 97.0% recall score, and 0.92 IoU, processing each image in under 10 seconds. The results of this study demonstrate that the `multi-agent zero-shot' method achieves high accuracy, fast inference, and transparent predictions for real-time vegetation monitoring, deforestation evaluations, and the urban canopy.

Objective: To review the application of goniometry and goniophotometry as innovative tools for advancing precision agriculture. The primary objective is to synthesize how these techniques contribute to optimizing light interception, enhancing crop yield, and improving land-use efficiency by providing precise data on plant architecture and light dynamics within cultivation systems. Design/methodology/approach: The research employs a systematic review methodology, analyzing existing scientific literature and case studies where goniometry and goniophotometry have been applied in agricultural contexts. Goniometry is used for measuring the angular dispositions of plant elements like leaves and stems, while goniophotometry characterizes light distribution and incidence angles. The approach focuses on integrating data from both techniques to inform adjustments in crop canopy architecture and artificial lighting systems. Results: The review demonstrates that the integration of goniometric and goniophotometric data enables the determination of optimal light incidence angles. This facilitates strategic adjustments to planting layouts and canopy management, leading to significant enhancements in photosynthetic efficiency. Consequently, studies report improvements in overall crop yield and a more efficient use of available land and light resources in diverse cultivation environments, from greenhouses to open fields. Limitations of the study/implications: The primary limitations discussed involve the technical complexity and cost associated with the specialized equipment required for these measurements. Furthermore, the practical implementation of findings can be constrained by the need for specialized knowledge to interpret data and integrate it into existing farm management systems, potentially limiting accessibility for widespread adoption. Findings/Conclusions: Goniometry and goniophotometry are powerful, though underutilized, tools that provide a scientific basis for optimizing agricultural systems. Their application offers significant advantages for enhancing photosynthetic performance and spatial planting efficiency. While challenges related to cost and complexity exist, the potential of these techniques to contribute to more sustainable and productive precision agriculture is substantial, warranting further research and development of user-friendly applications.

A novel point cloud completion model for three-dimensional reconstruction of complex, dynamic population-level crop canopy architecture.

Beyond dwarfism: Green Revolution gene Rht-D1b orchestrates tiller angle and canopy architecture in wheat

ABSTRACTAlpine grasslands of the Tibetan Plateau rank among Earth's ecologically critical yet vulnerable ecosystems. These ecosystems sustain pastoral livelihoods and preserve nomadic cultural traditions. However, climate warming combined with intensified anthropogenic pressures is accelerating shrub encroachment in these ecosystems. For centuries, Tibetan pastoralists have counteracted this process through targeted interventions: manual uprooting of dominant shrubs, controlled patch burning during dormant seasons, and rotational grazing systems coupled with strategic herbivore deployment to suppress woody seedlings. Despite their shrub management efficacy, the scientific rationale underlying Tibetan traditional ecological knowledge—the role of shrub‐herbaceous interaction variability in driving encroachment dynamics—remains understudied. We quantified shrub encroachment patterns across alpine meadows of the Zoige Plateau. Shrub encroachment dynamics were monitored across the elevation gradient (3400–3900 m), spanning the core elevational range of shrub encroachment. To identify shrub‐herbaceous interactions in driving encroachment dynamics, we conducted a manipulative experiment with four shrub treatments: shrub removal, canopy restriction, root exclusion, and ambient control. Our analysis identified that elevation imposed a strong linear constraint on short‐term encroachment rates (RSEIThree = −0.06E + 0.28, R2 = 0.83, p < 0.001; E (elevation, km)). No significant elevational effect was observed on long‐term encroachment rates (RSEIOutset; p > 0.05). Canopy competition (RIICanopy = 0.06E − 0.41, R2 = 0.81, p < 0.001) mainly explained 60.6% of the variation in encroachment dynamics, while root competition (RIIRoot = −0.05E + 0.05, R2 = 0.76, p < 0.001) accounted for 2.8%. Our findings highlight that canopy‐mediated suppression of herbaceous plants is the primary pathway driving encroachment, while root‐driven processes act as secondary mechanisms dependent on canopy architecture. Tibetan traditional methods directly disrupt key ecological drivers of shrub encroachment. By targeting shrub canopy removal, reducing herbaceous communities' light limitation, and weakening shrub competitive advantages that drive shrub encroachment, we promote the recovery of understory herbaceous communities.

ABSTRACT Aim Polar and alpine plants live at the edge of their physiological limits. Thus, relatively small changes in climate can have disproportionate effects on biological and ecological processes. Antarctic mosses display highly variable micro‐topography (canopy architecture) over centimetre scales that correspond with spatial patterns in moss health. We aimed to assess the influence of centimetre‐scale micro‐topography on biologically relevant canopy microclimates across Antarctic moss beds. Location Trans‐Antarctic. Time Period 2018–2023. Major Taxa Studied Moss communities (bryophytes). Methods Spatially explicit microclimate data were measured (canopy temperature and water content) at different micro‐topographic positions (micro‐ridges and valleys, and various micro‐slopes and aspects) within 1 m 2 plots of continuous moss cover in Maritime and East Antarctica. Solar radiation was modelled at 1 cm 2 resolution. Results (1) Moss canopies varied by up to 2.24°C in mean and 15°C in maximum temperature within plots, with centimetre‐scale micro‐topography consistently shaping microclimate conditions. (2) Micro‐topographic position, seasonal solar dynamics and processes such as radiative trapping jointly influence the spatial structure of moss temperatures over centimetre scales. (3) East Antarctic mosses show a greater ability to warm above ambient air temperature compared to Maritime Antarctic mosses and may be especially at risk of exceeding upper temperature thresholds. Main Conclusions This study considers the effect of centimetre‐scale moss micro‐topography on moss canopy microclimates and more broadly offers novel insights into the spatial structure and variation of ground‐level climate over scales typically overlooked by in situ measurements. We discuss centimetre‐scale microclimate variation in terms of moss physiology and observed declines in the health of East Antarctic mosses which visibly map to the micro‐topography. These findings are especially relevant for regions across the globe with short‐stature vegetation, like bio‐crusts, and alpine and polar fellfields. Recognising climate variation at micro‐topographic scales is crucial for understanding ecophysiology and plant–climate interactions.

Desertification erodes arable land and human habitats. Vegetation restoration represents a critical process for improving the quality of sandy land by enhancing soil structure and nutrient cycling. This study aims to investigation how vegetation restoration affects soil physicochemical properties and soil quality. Five vegetated land types were selected (Pinus sylvestris var. mongholica Litv., PS; Amygdalus pedunculata Pall., AP; Salix psammophila, SP; Amorpha fruticosa L., AF; Artemisia desertorum Spreng., AD). Bare sandy land (BS) served as the control. The physicochemical properties of 270 soil samples from three vertical depth intervals (0–10, 10–20, and 20–30 cm) were analyzed. The findings demonstrated that vegetation restoration markedly improved the proportion of finer soil particles (clay and silt) and organic carbon, while the variations in total phosphorus, ammonia nitrogen, and nitrate nitrogen were not significant. To better understand the variations in soil quality in different vegetated lands, a soil quality index (SQI) was developed that considers multiple soil physical and chemical indicator selections and scoring methods. The SQI based on the minimum dataset and linear scoring method better differentiated the soil quality for sandy lands and showed higher values for SP among all five vegetated lands and BS. Improvements in soil quality were closely related to vegetation properties (density and coverage) and litter characteristics (thickness, water content, and total phosphorus content). Restoration strategies for sandy lands should focus more strongly on species selection, taking into account interspecific variations in litter production, physicochemical properties, canopy architecture, and planting density to more effectively improve soil quality.

Remote sensing, which allows us to track the health and condition of the vegetation, is one component of precision agriculture. Numerous studies have examined the applications of remote sensing in agriculture; reviews consolidate this research and examine diverse scientific methodologies. Using imagery obtained by remote sensing platforms like satellites, aircraft, and unmanned aerial vehicles, this project attempts to compile the current vegetation indices utilized in viticulture. Spectroscopy, multispectral and hyper spectral imaging, thermography, electrical resistivity, laser imaging detection and ranging, computer vision, and chlorophyll fluorescence are among the sensing technologies that we describe in this paper. These technologies vary in spatial resolution, data acquisition cost, and suitability for specific vineyard management goals. We also discuss the platforms on which these technologies are typically mounted or embedded for either proximal or remote monitoring. One of the main goals of employing these technologies is to gather and provide data and information to winemakers and grape farmers so they may use it to make better decisions and manage their vineyards and property. Topics covered include crop forecasting, yield and fruit composition, vineyard sampling, targeted management, vegetative growth, canopy architecture, nutrient and water status, pests and diseases, soil and topography, and the present and future use of these technologies in vineyards. These technologies' underlying principles are also explained. The technologies have a lot of potential for farmers, but field-scale acceptance and use will require user-friendly devices and software as well as reasonable prices.

IntroductionA high-yielding rice variety (HYV), Kitagenki, in the Hokkaido region has a high yield potential owing to its large sink capacity, high source ability, and grain-filling ability. However, the detailed mechanisms underlying high biomass productivity, a major component of source ability, and stable high yields remain elusive. Thus, we aimed to elucidate the canopy morphological and physiological traits that improve the biomass productivity of Kitagenki and how they contribute to a stably high yield.MethodsWe conducted field experiments over 8 years using three rice varieties (Nanatsuboshi: standard-yielding variety, Kita-aoba, and Kitagenki: HYV) with three replicates.Results and discussionKitagenki stably produced higher gross hulled grain yield than Kita-aoba by 4.9–14.9% (8.9% on average) because of higher filled-grain percentage. During 0–20 days after the full-heading stage (DAH), Kitagenki revealed a markedly higher crop growth rate by 29.7% than Kita-aoba because of a higher net assimilation rate while maintaining leaf area index. During these stages, Kitagenki showed a better canopy architecture, characterized by substantially higher leaf inclination angles of the upper two leaves and narrower leaf blades, which facilitated better light interception inside the canopy and higher 13C assimilation of the third and whole leaves than in Kita-aoba. At the single-leaf level, Kitagenki showed a higher photosynthetic rate in the third leaf and higher stomatal conductance. Consequently, adequate carbohydrate supply during the early grain-filling stages in Kitagenki enabled faster translocation into the inferior spikelet, resulting in a higher grain-filling ability than that in Kita-aoba. This further contributed to the higher grain yield per cumulative solar radiation during 0–40 DAH in Kitagenki than in Kita-aoba under fluctuating air temperature. These findings indicate that superior canopy architecture, better light interception inside the canopy, and higher carbon assimilation of lower leaves contribute to high biomass productivity during the early grain-filling stage, leading to high grain-filling ability and a stable high yield in Kitagenki compared to Kita-aoba. These results provide key canopy morphological and physiological traits for breeding future HYV that can break the yield ceiling in cold regions.

Leaf area index (LAI) is a critical indicator of canopy architecture and physiological performance, serving as a key parameter for crop growth monitoring and management. Although UAV multispectral imagery provides rich spectral and spatial information, the limitations of single texture features for LAI estimation still require further exploration. To address this issue, this study developed a multi-source feature fusion framework that integrates vegetation indices (VIs), texture features (TFs), and texture indices (TIs) within a stacked ensemble approach combining Partial Least Squares Regression (PLSR) with Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT) algorithms to estimate maize LAI.A field experiment was conducted under three planting densities (42,000, 63,000, and 84,000 plants ha−1) and four nitrogen rates (0, 80, 160, 240 kg N ha−1) to assess the potential of UAV-based multispectral imagery for maize LAI estimation. The results show that when using partial least squares regression (PLSR) combined with RF, SVM and GBDT to estimate maize LAI, the R2 values are 0.653, 0.697 and 0.634, and the RMSE is 0.650, 0.608 and 0.668, respectively, when only vegetation indices (VIs) is used as input. After texture features (TFs) incorporation, the R2 increases to 0.717, 0.794, and 0.801, and the RMSE decreases to 0.587, 0.500, and 0.492. Further inclusion of the texture indices (TIs) raises the R2 to 0.789, 0.804, and 0.844, with RMSE of 0.506, 0.489, and 0.436, respectively. Independent test set validation under contrasting conditions confirmed that our multi-model fusion framework (PLSR+GBDT) with multi-source feature fusion (VIs+TFs+TIs) effectively estimated LAI, achieving an R2 of 0.859 and 0.794. These results demonstrate that multi-source feature integration via machine learning enables robust and accurate estimation of maize LAI, providing a valuable tool for precision agriculture and crop growth monitoring.

Early prediction of common bean (Phaseolus vulgaris L.) yield is essential for improving productivity in tropical agricultural systems. In this study, we integrated canopy structural metrics obtained with the Tracing Radiation and Architecture of Canopies (TRAC) system, unmanned aerial vehicle (UAV)-based multispectral measurements (normalized difference vegetation index—NDVI, projected canopy area), and phenological variables collected from stages R6 to R8 under non-limiting nitrogen conditions. Exploratory analyses (correlation, variance inflation factors—VIF), dimensionality reduction (principal component analysis—PCA), and regularized regression (Elastic Net/LASSO), combined with bootstrap stability selection, were applied to identify a parsimonious subset of robust predictors. The final model, composed of six variables, explained approximately 72% of the variability in plant-level grain yield, with acceptable errors (RMSE ≈ 10.67 g; MAE ≈ 7.91 g). The results demonstrate that combining early vigor, radiation interception, and canopy architecture provides complementary information beyond simple spectral indices. This non-destructive framework delivers an efficient model for early yield estimation and supports site-specific management decisions in common bean with high spatial resolution. By enhancing input-use efficiency and reducing waste, this approach contributes to sustainable development and aligns with the global Sustainable Development Goals (SDGs) for climate-resilient agriculture.

LED spectra and defoliation independently shape canopy architecture and cannabinoid yield in indoor Cannabis cultivation

The use of UAVs to detect litchi in natural environments is imperative for rapid litchi yield estimation and automated harvesting systems. However, UAV-based lychee fruit detection bottlenecks arise from complex canopy architecture and leaf occlusion. This study proposed a collaborative optimization strategy integrating agronomic technique with deep learning. Three leaf thinning intensities (0, 6, and 12 compound leaves) were applied at the early stage of fruit to systematically evaluate their effects on fruit growth, canopy structure, and detection performance. Results indicated that moderate defoliation (six leaves) significantly enhanced canopy openness and light penetration without adversely impacting on yield and fruit quality. Subsequent UAV-based detection under moderate versus no defoliation treatment revealed that the YOLOv8-based model achieved significant performance gains: mean average precision (mAP) increased from 0.818 to 0.884, and the F1-score improved from 0.796 to 0.842. The study contributes a novel collaborative optimization strategy that effectively mitigates occlusion issues in fruit detection. This approach demonstrates that agronomic techniques can be strategically used to enhance AI perception, offering a significant step forward in the integration of agricultural machinery and agronomy for intelligent orchard systems.

Efficient pesticide application remains a critical resource-management challenge in modern agriculture, where limited spray penetration reduces treatment efficacy, wastes chemical inputs, and increases environmental losses. This study quantified the effect of air-assisted spraying (AAS) on droplet deposition in two contrasting field crops, oilseed rape and wheat. Field trials were conducted using a sprayer equipped with an adjustable airflow module, and spray coverage was measured with water-sensitive papers at multiple canopy heights and orientations. In oilseed rape, AAS improved deposition on front-facing and top surfaces in the lower canopy, for example, increasing top-surface coverage at 90 cm from 53.4% to 65.5% at 6 km∙h−1, indicating more uniform distribution and enhanced penetration. In wheat, which typically exhibits a more open canopy structure compared to oilseed rape, AAS effects were smaller and less consistent, with the greatest gain on front-facing lower surfaces (from 13.3% to 21.9% at 7 km∙h−1). Although drift was not measured in this experiment, previous studies using the same sprayer prototype demonstrated measurable reductions, supporting the environmental relevance of improved deposition. These results highlight the role of canopy architecture in determining AAS performance and underscore the technology’s potential to reduce pesticide inputs, minimize off-target losses, and improve the resource efficiency of crop protection in line with EU Farm to Fork objectives.

Canopy architecture plays a crucial role in regulating plant growth, phenology and yield by influencing the microclimatic environment, light interception and resource use efficiency. This review systematically explores the interplay between canopy structure and plant phenological stages, particularly in flower crops. Structural traits such as leaf orientation, plant height, branching pattern and spatial distribution of foliage significantly determine the distribution of light within the canopy, affecting photosynthesis and developmental timing. Variations in canopy architecture have been shown to influence flowering induction, fruit set and yield potential by altering the temperature and light quality perceived by the plant. The review also highlights the hormonal control of canopy traits, with auxins, gibberellins and cytokinins modulating shoot elongation, leaf expansion and apical dominance. Furthermore, it examines practical approaches for canopy manipulation, including spacing, pruning and training systems, aimed at maximizing productivity and enhancing crop performance under varied environmental conditions. The integration of remote sensing tools and canopy modelling techniques is emphasized for real-time monitoring and optimization of canopy performance. Understanding the relationship between canopy design and phenology provides critical insights for breeding programs and precision agriculture strategies. This synthesis underscores the importance of tailored canopy management to achieve sustainable yield improvements and better adaptation to climate variability in flower crops.