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.

Physiological traits, crop growth, and grain quality of quinoa in response to deficit irrigation and planting methods.

Climate change has become a concern, emphasizing the need for the development of crops tolerant to drought. Therefore, this study is designed to explore the physiological characteristics of quinoa that enable it to thrive under drought and other extreme stress conditions by investigating the combined effects of irrigation water levels (100%, 75%, and 50% of quinoa's water requirements, WR as I1, I2 and I3) and different planting methods (basin, on-ridge, and in-furrow as P1, P2 and P3) on quinoa's physiological traits and gas exchange. Results showed that quinoa's yield is lowest with on-ridge planting and highest in the in-furrow planting method. Notably, the seed protein concentrations in I2 and I3 did not significantly differ but they were 25% higher than those obtained in I1, which highlighted the possibility of using a more effective irrigation method without compromising the seed quality. On the other hand, protein yield (PY) was lowest in P2 (mean of I1 and I2 as 257 kg ha-1) and highest in P3 (mean of I1 and I2 as 394 kg ha-1, 53% higher). Interestingly, PY values were not significantly different in I1 and I2, but they were lower significantly in I3 by 28%, 27% and 20% in P1, P2, and P3, respectively. Essential plant characteristics including plant height, stem diameter, and panicle number were 6.1-16.7%, 6.4-24.5%, and 18.4-36.5% lower, respectively, in I2 and I3 than those in I1. The highest Leaf Area Index (LAI) value (5.34) was recorded in the in-furrow planting and I1, while the lowest value was observed in the on-ridge planting method and I3 (3.47). In I3, leaf temperature increased by an average of 2.5-3 oC, particularly during the anthesis stage. The results also showed that at a similar leaf water potential (LWP) higher yield and dry matter were obtained in the in-furrow planting compared to those obtained in the basin and on-ridge planting methods. The highest stomatal conductance (gs) value was observed within the in-furrow planting method and full irrigation (I1P3), while the lowest values were obtained in the on-ridge and 50%WR (I3P2). Finally, photosynthesis rate (An) reduction with diminishing LWP was mild, providing insights into quinoa's adaptability to drought. In conclusion, considering the thorough evaluation of all the measured parameters, the study suggests using the in-furrow planting method with a 75%WR as the best approach for growing quinoa in arid and semi-arid regions to enhance production and resource efficiency.

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.

A moving target: trade-offs between maximizing carbon and minimizing hydraulic stress for plants in a changing climate.

Observational evidence indicates that tree leaf area may acclimate in response to changes in water availability to alleviate hydraulic stress. However, the underlying mechanisms driving leaf area changes and consequences of different leaf area allocation strategies remain unknown. Here, we use a trait-based hydraulically enabled tree model with two endmember leaf area allocation strategies, aimed at either maximizing carbon gain or moderating hydraulic stress. We examined the impacts of these strategies on future plant stress and productivity. Allocating leaf area to maximize carbon gain increased productivity with high CO2, but systematically increased hydraulic stress. Following an allocation strategy to avoid increased future hydraulic stress missed out on 26% of the potential future net primary productivity in some geographies. Both endmember leaf area allocation strategies resulted in leaf area decreases under future climate scenarios, contrary to Earth system model (ESM) predictions. Leaf area acclimation to avoid increased hydraulic stress (and potentially the risk of accelerated mortality) was possible, but led to reduced carbon gain. Accounting for plant hydraulic effects on canopy acclimation in ESMs could limit or reverse current projections of future increases in leaf area, with consequences for the carbon and water cycles, and surface energy budgets.

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.

Growth, Morphology and Respiratory Cost Responses to Salinity in the Mangrove Plant Rhizophora Stylosa Depend on Growth Temperature.

Mangrove plants, which have evolved to inhabit tidal flats, may adjust their physiological and morphological traits to optimize their growth in saline habitats. Furthermore, the confined distribution of mangroves within warm regions suggests that warm temperature is advantageous to their growth in saline environments. We analyzed growth, morphology and respiratory responses to moderate salinity and temperature in a mangrove species, Rhizophora stylosa. The growth of R. stylosa was accelerated in moderate salinity compared with its growth in fresh water. Under warm conditions, the increased growth is accompanied by increased specific leaf area (SLA) and specific root length. Low temperature resulted in a low relative growth rate due to a low leaf area ratio and small SLA, regardless of salinity. Salinity lowered the ratio of the amounts of alternative oxidase to cytochrome c oxidase in the mitochondrial respiratory chain in leaves. Salinity enhanced the leaf respiration rate for maintenance, but under warm conditions this enhancement was compensated by a low leaf respiration rate for growth. In contrast, salinity enhanced overall leaf respiration rates at low temperature. Our results indicate that under moderate saline conditions R. stylosa leaves require warm temperatures to grow with a high rate of resource acquisition without enhancing respiratory cost.

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.

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.

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.

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.

Responses of oak seedlings to increased herbivory and drought: a possible trade-off?

BACKGROUND AND AIMS: Anthropogenic disturbances are causing a co-occurring increase in biotic (ungulate herbivory) and abiotic (drought) stressors, threatening plant reproduction in oak-dominated ecosystems. However, we wonder whether herbivory could compensate for the adverse impact of drought by reducing evapotranspiration. Thus, we investigate the isolated and joint effects of herbivory and drought on oak seedlings of two contrasting Mediterranean species that differ in leaf habit and drought resistance. METHODS: California oak seedlings from the evergreen, and more drought-resistant, Quercus agrifolia and the deciduous Q. lobata (n=387) were assigned to a fully crossed factorial design with herbivory and drought as stress factors. Seedlings were assigned in a greenhouse to 3-4 clipping levels simulating herbivory and 3-4 watering levels, depending on the species. We measured survival, growth, and leaf attributes (chlorophyll, secondary metabolites, leaf area and weight) once a month (May-Sep) and harvested above- and below-ground biomass at the end of the growing season. KEY RESULTS: For both oak species, simulated herbivory enhanced seedling survival during severe drought or delayed its adverse effects, probably due to reduced transpiration resulting from herbivory-induced leaf area reduction and compensatory root growth. Seedlings from the deciduous, and less drought-resistant species, benefitted from herbivory at lower levels of water stress, suggesting different response across species. We also found complex interactions between herbivory and drought on their impact on leaf attributes. In contrast to chlorophyll content which was not affected by herbivory, anthocyanins increased with herbivory - although water stress reduced differences in anthocyanins due to herbivory. CONCLUSIONS: Herbivory seems to facilitate Mediterranean oak seedlings to withstand summer drought, potentially alleviating a key bottleneck in the oak recruitment process. Our study highlights the need to consider ontogenetic stages and species-specific traits in understanding complex relationships between herbivory and drought stressors for the persistence and restoration of multi-species oak savannas.

Estimation of Leaf Area Index across Biomes and Growth Stages Combining Multiple Vegetation Indices.

The leaf area index (LAI) is a key indicator of vegetation canopy structure and growth status, crucial for global ecological environment research. The Moderate Resolution Spectral Imager-II (MERSI-II) aboard Fengyun-3D (FY-3D) covers the globe twice daily, providing a reliable data source for large-scale and high-frequency LAI estimation. VI-based LAI estimation is effective, but species and growth status impacts on the sensitivity of the VI-LAI relationship are rarely considered, especially for MERSI-II. This study analyzed the VI-LAI relationship for eight biomes in China with contrasting leaf structures and canopy architectures. The LAI was estimated by adaptively combining multiple VIs and validated using MODIS, GLASS, and ground measurements. Results show that (1) species and growth stages significantly affect VI-LAI sensitivity. For example, the EVI is optimal for broadleaf crops in winter, while the RDVI is best for evergreen needleleaf forests in summer. (2) Combining vegetation indices can significantly optimize sensitivity. The accuracy of multi-VI-based LAI retrieval is notably higher than using a single VI for the entire year. (3) MERSI-II shows good spatial-temporal consistency with MODIS and GLASS and is more sensitive to vegetation growth fluctuation. Direct validation with ground-truth data also demonstrates that the uncertainty of retrievals is acceptable (R2 = 0.808, RMSE = 0.642).

Optimizing native vegetation establishment in urban soils: Assessing the impacts of organic amendments on specific growth parameters.

Following soil disturbances, establishing healthy roadside vegetation can reduce surface water runoff, improve soil quality, decrease erosion, and enhance landscape aesthetics. This study explores the use of organic soil amendments (OAs) as alternatives to conventional vegetation growth approaches, aiming to provide optimal compost mixing ratios for poor soils, and clarify guidelines for OAs' use in roadside projects. Three sandy loam soils and one loam soil were chosen for the study. Organic amendments included yard waste (Y), food waste (F), turkey litter and green waste-based (T) composts, and wood-derived biochar (B). Treatment applications targeted specific increases in the organic matter (OM) percentage of the soils. A selection of seven native species (grasses and forbs) in a total of 156 pots (4 control soils + 4 soils x 4 OAs x 3 application rates, all prepared in triplicates) was used for the pot study experiment. A significant correlation between electrical conductivity (soluble salts) in soil-OA blends and corresponding percent green coverage (%GC) was found. High salts from the T compost either delayed or curtailed growth. Notably, 3 out of the 4 soils amended with biochar exhibited rapid vegetation coverage during initial growth stages compared to other soil-OA blends but reduced the nitrogen (N) uptake and leaf area in black-eyed Susan (BES) plants. In contrast, N uptake was higher in the BES plants emerging from composts T, F, and Y compared to biochar. It is recommended to minimize concentrated manure-based (e.g., turkey litter) composts for roadside projects as an OM source, and alternatively, enriching wood-based biochar with nutrients when used as a soil amendment. Within the current study, composts such as F and Y were well-suited to establish healthy and long-lasting vegetation.

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.

PhenoBot: an automated system for leaf area analysis using deep learning.

MAIN CONCLUSION: A low-cost dynamic image capturing and analysis pipeline using color-based deep learning segmentation was developed for direct leaf area estimation of multiple crop types in a commercial environment. Crop yield is largely driven by intercepted radiation of the leaf canopy, making the leaf area index (LAI) a critical parameter for estimating yields. The growth rate of leaves at different growth stages determines the overall LAI, which is used by crop growth models (CGM) for simulating yield. Consequently, precise phenotyping of the leaves can help elucidate phenological processes relating to resource capturing. A stable system for acquiring images and a strong data processing backend play a vital role in reducing throughput time and increasing accuracy of calculations, compared to manual analysis. However, most available solutions are not dynamic, as they use color-based segmentation, which fails to capture leaves with varying shades and shapes. We have developed a system that uses a low-cost setup to acquire images and an automated pipeline to manage the data storage on the device and in the cloud. The system is powered by virtual machines that run multiple custom-trained deep learning models to segment out leaves, calculate leaf area (LA) for the whole set and at the individual leaf level, overlays important information on the images, and appends them on a compatible file used for CGMs with very high accuracy. The pipeline is dynamic and can be used for multiple crops. The use of open-source hardware, platforms, and algorithms makes this system affordable and reproducible.