Intraspecific variability of leaf form and function across habitat types.

Trait-based ecology has already revealed main independent axes of trait variation defining trait spaces that summarize plant adaptive strategies, but often ignoring intraspecific trait variability (ITV). By using empirical ITV-level data for two independent dimensions of leaf form and function and 167 species across five habitat types (coastal dunes, forests, grasslands, heathlands, wetlands) in the Italian peninsula, we found that ITV: (i) rotated the axes of trait variation that define the trait space; (ii) increased the variance explained by these axes and (iii) affected the functional structure of the target trait space. However, the magnitude of these effects was rather small and depended on the trait and habitat type. Our results reinforce the idea that ITV is context-dependent, calling for careful extrapolations of ITV patterns across traits and spatial scales. Importantly, our study provides a framework that can be used to start integrating ITV into trait space analyses.

Fast-slow traits predict competition network structure and its response to resources and enemies.

Plants interact in complex networks but how network structure depends on resources, natural enemies and species resource-use strategy remains poorly understood. Here, we quantified competition networks among 18 plants varying in fast-slow strategy, by testing how increased nutrient availability and reduced foliar pathogens affected intra- and inter-specific interactions. Our results show that nitrogen and pathogens altered several aspects of network structure, often in unexpected ways due to fast and slow growing species responding differently. Nitrogen addition increased competition asymmetry in slow growing networks, as expected, but decreased it in fast growing networks. Pathogen reduction made networks more even and less skewed because pathogens targeted weaker competitors. Surprisingly, pathogens and nitrogen dampened each other's effect. Our results show that plant growth strategy is key to understand how competition respond to resources and enemies, a prediction from classic theories which has rarely been tested by linking functional traits to competition networks.

Nitrogen availability and plant functional composition modify biodiversity-multifunctionality relationships.

Biodiversity typically increases multiple ecosystem functions simultaneously (multifunctionality) but variation in the strength and direction of biodiversity effects between studies suggests context dependency. To determine how different factors modulate the diversity effect on multifunctionality, we established a large grassland experiment manipulating plant species richness, resource addition, functional composition (exploitative vs. conservative species), functional diversity and enemy abundance. We measured ten above- and belowground functions and calculated ecosystem multifunctionality. Species richness and functional diversity both increased multifunctionality, but their effects were context dependent. Richness increased multifunctionality when communities were assembled with fast-growing species. This was because slow species were more redundant in their functional effects, whereas different fast species promoted different functions. Functional diversity also increased multifunctionality but this effect was dampened by nitrogen enrichment and enemy presence. Our study suggests that a shift towards fast-growing communities will not only alter ecosystem functioning but also the strength of biodiversity-functioning relationships.

Rice Big Grain1 improves grain yield in ectopically expressing rice and heterologously expressing tobacco plants.

Functional genomics through transgenesis has provided faster and more reliable methods for identifying, characterizing, and utilizing genes or quantitative trait loci linked to agronomic traits to target yield. The present study explored the role of Big Grain1 (BG1) gene of rice (Oryza sativa L.) in yield improvement of crop plants. We aimed to identify the genetic variation of OsBG1 in various indica rice cultivars by studying the allelic polymorphism of the gene, while also investigating the gene's potential to increase crop yield through the transgenic approach. Our study reports the presence of an extra 393 bp sequence having two 6 bp enhancer elements in the 3' regulatory sequence of OsBG1 in the large-grain cultivar IR64 but not in the small-grain cultivar Badshahbhog. A single copy of the OsBG1 gene in both the cultivars and a 4.1-fold higher expression of OsBG1 in IR64 than in Badshahbhog imply that the grain size is positively correlated with the level of OsBG1 expression in rice. The ectopic expression of OsBG1 under the endosperm-specific glutelin C promoter in Badshahbhog enhanced the flag leaf length, panicle weight, and panicle length by an average of 33.2%, 33.7%, and 30.5%, respectively. The length of anthers, spikelet fertility, and grain yield per plant increased in transgenic rice lines by an average of 27.5%, 8.3%, and 54.4%, respectively. Heterologous expression of OsBG1 under the constitutive 2xCaMV35S promoter improved the number of seed pods per plant and seed yield per plant in transgenic tobacco lines by an average of 2.2-fold and 2.6-fold, respectively. Improving crop yield is crucial to ensure food security and socio-economic stability, and identifying suitable genetic factor is the essential step towards this endeavor. Our findings suggest that the OsBG1 gene is a promising candidate for improving the grain yield of monocot and dicot plant systems by molecular breeding and genetic engineering.

Leaf functional traits and resource use strategies facilitate the spread of invasive plant Parthenium hysterophorus across an elevational gradient in western Himalayas.

Parthenium hysterophorus L. (Asteraceae) is a highly prevalent invasive species in subtropical regions across the world. It has recently been seen to shift from low (subtropical) to high (sub-temperate) elevations. Nevertheless, there is a dearth of research investigating the adaptive responses and the significance of leaf functional traits in promoting the expansion to high elevations. The current study investigated the variations and trade-offs among 14 leaf traits (structural, photosynthetic, and nutrient content) of P. hysterophorus across different elevations in the western Himalayas, India. Plots measuring 20 × 40 m were established at different elevations (700 m, 1100 m, 1400 m, and 1800 m) to collect leaf trait data for P. hysterophorus. Along the elevational gradient, significant variations were noticed in leaf morphological parameters, leaf nutrient content, and leaf photosynthetic parameters. Significant increases were observed in the specific leaf area, leaf thickness, and chlorophyll a, total chlorophyll and carotenoid content, as well as leaf nitrogen and phosphorus content with elevation. On the other hand, there were reductions in the amount of chlorophyll b, photosynthetic efficiency, leaf dry matter content, leaf mass per area, and leaf water content. The trait-trait relationships between leaf water content and dry weight and between leaf area and dry weight were stronger at higher elevations. The results show that leaf trait variability and trait-trait correlations are very important for sustaining plant fitness and growth rates in low-temperature, high-irradiance, resource-limited environments at relatively high elevations. To summarise, the findings suggest that P. hysterophorus can expand its range to higher elevations by broadening its functional niche through changes in leaf traits and resource utilisation strategies.

In situ short-term responses of Amazonian understory plants to elevated CO2.

The response of plants to increasing atmospheric CO2 depends on the ecological context where the plants are found. Several experiments with elevated CO2 (eCO2) have been done worldwide, but the Amazonian forest understory has been neglected. As the central Amazon is limited by light and phosphorus, understanding how understory responds to eCO2 is important for foreseeing how the forest will function in the future. In the understory of a natural forest in the Central Amazon, we installed four open-top chambers as control replicates and another four under eCO2 (+250 ppm above ambient levels). Under eCO2, we observed increases in carbon assimilation rate (67%), maximum electron transport rate (19%), quantum yield (56%), and water use efficiency (78%). We also detected an increase in leaf area (51%) and stem diameter increment (65%). Central Amazon understory responded positively to eCO2 by increasing their ability to capture and use light and the extra primary productivity was allocated to supporting more leaf and conducting tissues. The increment in leaf area while maintaining transpiration rates suggests that the understory will increase its contribution to evapotranspiration. Therefore, this forest might be less resistant in the future to extreme drought, as no reduction in transpiration rates were detected.

Plant traits mediate foliar uptake of deposited nitrogen by mature woody plants.

Increased atmospheric nitrogen (N) deposition significantly disturbs ecosystem N cycle. Although foliar interception and uptake of N deposition can provide an important alternative N supply to forest ecosystems, the mechanisms regulating foliar N uptake from wet deposition are not fully understood. Here, we selected 19 woody species with a wide range of plant traits from different functional groups and conducted a 15N isotope labelling experiment through brushing 15NH4 + and 15NO3 - solution on canopy leaves. Our findings demonstrate that leaves can directly absorb N from wet deposition within a few hours. The average leaf 15N recoveries were 10% and 28% under 15NH4 + and 15NO3 - treatments across species, respectively, while twig N recoveries were only 1%-7% of leaf N recoveries. Differences in foliar N uptake efficiency among species were closely associated with leaf traits but were little influenced by meteorological conditions or soil nutrient status. Specifically, plants with higher leaf N concentration, larger specific leaf area and lower wax concentration exhibited higher leaf N recovery. Our results indicated that tree canopies could directly absorb N from atmospheric deposition. We highlight the critical role of leaf traits in determining canopy foliar N uptake, which may consequently influence plant competition under elevated N deposition.

G × E × M analysis to define allometric rules between leaves and stems in wheat.

Allometric rules provide insights into the structure-function relationships across species and scales and are commonly used in ecology. The fields of agronomy, plant phenotyping and modeling also need simplifications such as allometric rules to reconcile data at different temporal and spatial levels (organs/canopy). This paper explores the variations in relationships for wheat regarding (i) the distribution of crop green area between leaves and stems, and (ii) the allocation of above-ground biomass between leaves and stems during the vegetative period, using a large dataset covering different years, countries, genotypes and management practices. Our results show that the relationship between leaf and stem area was linear, genotype-specific, and sensitive to radiation. The relationship between leaf and stem biomass depended on genotype and nitrogen fertilization. The mass per area, associating area and biomass for both leaf and stem, varied strongly by developmental stage and was significantly affected by environment and genotype. These allometric rules were evaluated with satisfactory performance, and their potential use is discussed with regard to current phenotyping techniques and plant/crop models. Our results enable the definition of models and minimum datasets required for characterizing diversity panels and making predictions in various G × E × M contexts.

Scaling of leaf area with biomass in trees reconsidered: constant metabolically active sapwood volume per unit leaf area with height growth.

Hypoallometric (slope<1) scaling between metabolic rate and body mass is often regarded as near-universal across organisms. However, there are compelling reasons to question hypoallometric scaling in woody plants, where metabolic rate is directly proportional to leaf area. This leaf area must provide carbon to the volume of the metabolically active sapwood (VMASW). Within populations of a species, variants in which VMASW increases per unit leaf area with height growth (e.g. ⅔ or ¾ scaling) would have proportionally less carbon for growth and reproduction as they grow taller. Therefore, selection should favor individuals in which, as they grow taller, leaf area scales isometrically with shoot VMASW (slope=1). Using tetrazolium staining, we measured total VMASW and total leaf area (LAtot) across 22 individuals of Ricinus communis and confirmed that leaf area scales isometrically with VMASW, and that VMASW is much smaller than total sapwood volume. With the potential of the LAtot-VMASW relationship to shape factors as diverse as the crown area-stem diameter relationship, conduit diameter scaling, reproductive output, and drought-induced mortality, our work indicates that the notion that sapwood increases per unit leaf area with height growth requires revision.

Comparison between computer recognition and manual measurement methods for the estimation of leaf area.

BACKGROUND AND AIMS: Leaf area (A) is a crucial indicator of the photosynthetic capacity of plants. The Montgomery equation (ME), which hypothesizes that A is proportional to the product of leaf length (L) and width (W), is a valid tool for non-destructively measuring A for many broadleaved plants. At present, the methods used to compute L and W for the ME can be broadly divided into two kinds: using computer recognition and measuring manually. However, the potential difference in the prediction accuracy using either method has not been thoroughly examined in previous studies. METHODS: In the present study, we measured 540 Alangium chinense leaves, 489 Liquidambar formosana leaves and 215 Liriodendron × sinoamericanum leaves, utilizing computer recognition and manual measurement methods to determine L and W. The ME was used to fit the data determined by the two methods, and the goodness of fits were compared. The prediction errors of A were analysed by examining the correlations with two leaf symmetry indices (areal ratio of the left side to the right side, and standardized index for bilateral asymmetry), as well as the leaf shape complexity index (the leaf dissection index). KEY RESULTS: The results indicate that there is a neglectable difference in the estimation of A between the two methods. This further validates that the ME is an effective method for estimating A in broadleaved tree species, including those with lobes. Additionally, leaf shape complexity significantly influenced the estimation of A. CONCLUSIONS: These results show that the use of computer recognition and manual measurement in the field are both effective and feasible, although the influence of leaf shape complexity should be considered when applying the ME to estimate A in the future.

Divergent structural leaf trait spectra in succulent versus non-succulent plant taxa.

BACKGROUND AND SCOPE: Plant functional traits are the result of natural selection to optimize carbon gain, leading to a broad spectrum of traits across environmental gradients. Among plant traits, leaf water storage capacity is paramount for plant drought resistance. We explored whether leaf-succulent taxa follow trait correlations similar to those of non-leaf-succulent taxa to evaluate whether both are similarly constrained by relationships between leaf water storage and climate. METHODS: We tested the relationships among three leaf traits related to water storage capacity and resource use strategies in 132 species comprising three primary leaf types: succulent, sclerophyllous, and leaves with rapid returns on water investment, referred to as fast return. Correlation coefficients among specific leaf area (SLA), water mass per unit of area (WMA), and saturated water content (SWC) were tested, along with relationships between leaf trait spectra and aridity determined from species occurrence records. RESULTS: Both SWC and WMA at a given SLA were ~10-fold higher in succulent leaves than in non-succulent leaves. While SWC actually increased with SLA in non-succulent leaves, no relationship was detected between SWC and SLA in succulent leaves, although WMA decreased with SLA in all leaf types. A principal component analysis (PCA) revealed that succulent taxa occupied a widely different mean trait space than either fast-return (P < 0.0001) or sclerophyllous (P < 0.0001) taxa along the first PCA axis, which explained 63 % of mean trait expression among species. However, aridity only explained 12 % of the variation in PCA1 values. This study is among the first to establish a structural leaf trait spectrum in succulent leaf taxa and quantify contrasts in leaf water storage among leaf types relative to specific leaf area. CONCLUSIONS: Trait coordination in succulent leaf taxa may not follow patterns similar to those of widely studied non-succulent taxa.

The simultaneous prediction of yield and maturity date for wheat-maize by combining satellite images with crop model.

BACKGROUND: The simultaneous prediction of yield and maturity date has an important impact on ensuring food security. However, few studies have focused on simultaneous prediction of yield and maturity date for wheat-maize in the North China Plain (NCP). In this study, we developed the prediction model of maturity date and yield (PMMY) for wheat-maize using multi-source satellite images, an Agricultural Production Systems sIMulator (APSIM) model and a random forest (RF) algorithm. RESULTS: The results showed that the PMMY model using peak leaf area index (LAI) and accumulated evapotranspiration (ET) has the optimal performance in the prediction of maturity date and yield. The accuracy of the PMMY model using peak LAI and accumulated ET was higher than that of the PMMY model using only peak LAI or accumulated ET. In a single year, the PMMY model had good performance in the prediction of maturity date and yield. The latitude variation in spatial distribution of maturity date for WM was obvious. The spatial heterogeneity for yield of wheat-maize was not prominent. Compared with 2001-2005, the maturity date of the two crops in 2016-2020 advanced 1-2 days, while yield increased 659-706 kg ha-1. The increase in minimum temperature was the main meteorological factor for advance in the maturity date for wheat-maize. Precipitation was mainly positively correlated with maize yield, while the increase in minimum temperature and solar radiation was crucial to the increase in yield. CONCLUSION: The simultaneous prediction of yield and maturity can be used to guide agricultural production and ensure food security. © 2024 Society of Chemical Industry.

Estimation of winter wheat LAI based on color indices and texture features of RGB images taken by UAV.

BACKGROUND: Leaf area index (LAI) is an important indicator for assessing plant growth and development, and is also closely related to photosynthesis in plants. The realization of rapid accurate estimation of crop LAI plays an important role in guiding farmland production. In study, the UAV-RGB technology was used to estimate LAI based on 65 winter wheat varieties at different fertility periods, the wheat varieties including farm varieties, main cultivars, new lines, core germplasm and foreign varieties. Color indices (CIs) and texture features were extracted from RGB images to determine their quantitative link to LAI. RESULTS: The results revealed that among the extracted image features, LAI exhibited a significant positive correlation with CIs (r = 0.801), whereas there was a significant negative correlation with texture features (r = -0.783). Furthermore, the visible atmospheric resistance index, the green-red vegetation index, the modified green-red vegetation index in the CIs, and the mean in the texture features demonstrated a strong correlation with the LAI with r > 0.8. With reference to the model input variables, the backpropagation neural network (BPNN) model of LAI based on the CIs and texture features (R2 = 0.730, RMSE = 0.691, RPD = 1.927) outperformed other models constructed by individual variables. CONCLUSION: This study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer-level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry.

Potassium deficiency reduces grapevine transpiration through decreased leaf area and stomatal conductance.

Plants require potassium (K) to support growth and regulate hydraulics. Yet, K's effects on transpiration are still speculated. We hypothesized that K deficiency would limit grapevine water uptake by limiting canopy size and stomatal conductance (gs). Hence, we constructed large (2 m3) lysimeters and recorded vine transpiration for three years (2020-2022) under three fertilization application rates (8, 20, or 58 mg K L-1 in irrigation). Maximal K availability supported transpiration up to 75 L day-1, whereas K-deficient vines transpired only 60 L day-1 in midsummer. Limited vine growth and canopy size mainly accounted for reduced transpiration under low K conditions. Hence, considering K demand in addition to supply, we compared K deficiency effects on vines bearing 20 or 50 fruit clusters and found that reduced gs further limited transpiration when yields were high. Although fruits were strong K sinks, high yields did not alter K uptake because lower vegetative growth countered the additional K demands. Potassium deficiency leads to lower transpiration and productivity. Yet, internal mineral allocation compensates for fruit K uptake and masks biochemical indices or physiological proxies for K deficiency. Thus, decision support tools should integrate mineral availability, seasonal growth, and yield projections to determine grapevine water demands.

The combined effect of diffuse radiation and leaf wetness on functional traits and transpiration efficiency on a cloud forest species.

Cloud forests are unique biomes that thrive in foggy environments for a substantial part of the season. Fog in cloud forests plays two critical roles: it reduces incoming radiation and creates a humid environment, leading to the wetting of the canopy. This paper aims to investigate the combined effect of both radiation and wetness on Myrica faya Wilbur-a cloud forest species present in subtropical regions-both directly in plants and through simulations. Experiments consisted of a controlled environment with two levels of radiation and leaf wetness: low radiation/wet conditions, and high radiation/no-wetness; and three treatments: continuous low radiation and wetness, continuous high radiation and no wetness and alternate high low radiation and alternate wetness. The results revealed that a combination of low radiation and leaf wetness significantly improves leaf stomata conductance and increases the specific leaf area (SLA). Changes in SLA were driven by leaf size changes. However, the minimum leaf conductance (gmin) did not respond to any of the treatments. The simulations focused on exploring the impact of radiation and canopy wetness on transpiration efficiency (TE), i.e. the ratio between photosynthesis (An) and transpiration (Tc). The simulations demonstrated that TE increased exponentially as the canopy was gradually wetted, regardless of the radiation environment. This increase in TE results from Tc approaching zero while An maintains positive values. Overall, this study provides an integrated understanding of how fog alters M. faya functioning and, potentially, other cloud forest tree species.

Biochar-based nanoparticles mitigated arsenic toxicity and improved physiological performance of basil via enhancing cation exchange capacity and ferric chelate reductase activity.

The modified biochars have positive effects in reducing heavy metal toxicity for plants. However, the mechanism and extent of these effects on mitigating arsenic toxicity and plant performance are not clear. Thus, a pot experiment was conducted as factorial to evaluate the potential of fresh and enriched biochars with potassium and magnesium nano-sulfates [potassium-enriched biochar (K-BC), magnesium-enriched biochar (Mg-BC) in individual and combined forms] on reducing arsenic toxicity (non-contamination, 50, and 100 mg NaAsO2 kg-1 soil) in basil plants. Biochar-related treatments reduced plant arsenic absorption rate (up to 24%), arsenic content of root (up to 38%) and shoot (up to 21%) and root tonoplast H+-ATPase activity (up to 30%). The fresh and particularly enriched biochars improved soil properties (pH, CEC, and available iron content), ferric chelate reductase activity, iron, potassium and magnesium contents of plant tissues, chlorophyll content index, photochemical efficiency of photosystem II, relative electron transport rate, leaf area, and basil growth (shoot and root dry weight). These results revealed that enriched biochars are useful soil amendments for improving physiological performance of plants via reducing heavy metal toxicity and enhancing cation exchange capacity, nutrient availability and ferric chelate reductase activity. Therefore, soil amendment by enriched biochars could be a sustainable solution for enhancing plant productivity in contaminated soils via mitigating environmental impacts. This is an environmentally friendly method for using the natural wastes to overcome the adverse effects of soil pollutants on medicinal plants.

Nutrients from spawning salmon influence leaf area, tissue density, and nitrogen-15 in riparian plant leaves.

Nutrient subsidies have significant impacts on ecosystems by connecting disjunct habitats, often through long-distance animal migrations. Salmon migrations on the North Pacific coasts provide these kinds of nutrient subsidies from senescent fish at the end of their life cycle, which can have significant ecological effects on terrestrial species. This can include impacts on individuals, populations, and communities, where shifts in community composition towards plant species that indicate nitrogen-rich soils have been documented. We investigated the effects of variation in salmon spawning density on the leaf traits of four common riparian plant species on the central coast of British Columbia, Canada. We found that all plant species had higher foliar salmon-derived nitrogen on streams with a higher spawning density. Three of the four species had larger leaves, and one species also had higher leaf mass per area on streams with more salmon. However, we found no differences in leaf greenness or foliar percent nitrogen among our study streams. These results demonstrate that nutrient subsidies from spawning salmon can have significant impacts on the ecology, morphology, and physiology of riparian plants, which lends support to a mechanism by which certain plants are more common on productive salmon streams.

Ability of Different Growth Indicators to Detect Salt Tolerance of Advanced Spring Wheat Lines Grown in Real Field Conditions.

Plant growth indicators (GIs) are important for evaluating how different genotypes respond to normal and stress conditions separately. They consider both the morphological and physiological components of plants between two successive growth stages. Despite their significance, GIs are not commonly used as screening criteria for detecting salt tolerance of genotypes. In this study, 36 recombinant inbred lines (RILs) along with four genotypes differing in their salt tolerance were grown under normal and 150 mM NaCl in a two-year field trial. The performance and salt tolerance of these germplasms were assessed through various GIs. The analysis of variance showed highly significant variation between salinity levels, genotypes, and their interaction for all GIs and other traits in each year and combined data for two years, with a few exceptions. All traits and GIs were significantly reduced by salinity stress, except for relative growth rate (RGR), net assimilation rate (NAR), and specific leaf weight (SLW), which increased under salinity conditions. Traits and GIs were more correlated with each other under salinity than under normal conditions. Principal component analysis organized traits and GIs into three main groups under both conditions, with RGR, NAR, and specific leaf area (SLA) closely associated with grain yield (GY) and harvest index, while leaf area duration (LAD) was closely associated with green leaf area (GLA), plant dry weight (PDW), and leaf area index (LAI). A hierarchical clustering heatmap based on GIs and traits organized germplasms into three and four groups under normal and salinity conditions, respectively. Based on the values of traits and GIs for each group, the germplasms varied from high- to low-performing groups under normal conditions and from salt-tolerant to salt-sensitive groups under salinity conditions. RGR, NAR, and LAD were important factors determining genotypic variation in GY of high- and low-performing groups, while all GIs, except leaf area duration (LAR), were major factors describing genotypic variation in GY of salt-tolerant and salt-sensitive groups. In conclusion, different GIs that reveal the relationship between the morphological and physiological components of genotypes could serve as valuable selection criteria for evaluating the performance of genotypes under normal conditions and their salt tolerance under salinity stress conditions.

Creating a new yellow and blue combination transparent film for Panax ginseng C.A. Meyer growth based on orthogonal designs.

Panax ginseng C.A. Meyer originates from old-growth forest environments, where the light intensity and spectrum reaching the forest bed are influenced by the canopy and humidity. In farmlands, suitable light intensity for cultivation is achieved by controlling the light transmission rate using shading nets, while light quality is regulated by a cover of yellow or blue transparent film. Such films have a light quality distinct from that produced by old-growth forests. Herein, a large composite film was developed by alternating small pieces of yellow and blue transparent film. An orthogonal array was used to evaluate the influence of the small transparent film area (STFA), yellow transparent film (YTF) number, and blue transparent film (BTF) number on the associated changes in ginseng in a range of fluorescence-, photosynthesis-, morphology-, and crop quality-related factors. Our results showed that light intensity was influenced primarily by STFA, which caused an overall decrease, while the light quality ratio was affected primarily by YTF number, which increased the proportion of red light and decreased that of blue light, with corresponding influence on different growth parameters. Based on these observations, an improved yellow and blue combination transparent film (YBCTF) with the following characteristics was established: STFA: 15 × 15 cm, YTF: two pieces, and BTF: three pieces. The improved YBCTF facilitated efficient light energy use by the plants, and led to an increase in leaf area, the per leaf photosynthetic rate, dry root weight, and the per root single ginsenoside yield. The findings present a relatively low-cost approach for optimising the light environment of ginseng cultivated in farmland and other crops in large-scale agricultural settings.

Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches.

Precise and timely leaf area index (LAI) estimation for winter wheat is crucial for precision agriculture. The emergence of high-resolution unmanned aerial vehicle (UAV) data and machine learning techniques offers a revolutionary approach for fine-scale estimation of wheat LAI at the low cost. While machine learning has proven valuable for LAI estimation, there are still model limitations and variations that impede accurate and efficient LAI inversion. This study explores the potential of classical machine learning models and deep learning model for estimating winter wheat LAI using multispectral images acquired by drones. Initially, the texture features and vegetation indices served as inputs for the partial least squares regression (PLSR) model and random forest (RF) model. Then, the ground-measured LAI data were combined to invert winter wheat LAI. In contrast, this study also employed a convolutional neural network (CNN) model that solely utilizes the cropped original image for LAI estimation. The results show that vegetation indices outperform the texture features in terms of correlation analysis with LAI and estimation accuracy. However, the highest accuracy is achieved by combining both vegetation indices and texture features to invert LAI in both conventional machine learning methods. Among the three models, the CNN approach yielded the highest LAI estimation accuracy (R 2 = 0.83), followed by the RF model (R 2 = 0.82), with the PLSR model exhibited the lowest accuracy (R 2 = 0.78). The spatial distribution and values of the estimated results for the RF and CNN models are similar, whereas the PLSR model differs significantly from the first two models. This study achieves rapid and accurate winter wheat LAI estimation using classical machine learning and deep learning methods. The findings can serve as a reference for real-time wheat growth monitoring and field management practices.