Leaf Scale Research Articles

A Review of Deep Learning in Multiscale Agricultural Sensing

Population growth, climate change, and the worldwide COVID-19 pandemic are imposing increasing pressure on global agricultural production. The challenge of increasing crop yield while ensuring sustainable development of environmentally friendly agriculture is a common issue throughout the world. Autonomous systems, sensing technologies, and artificial intelligence offer great opportunities to tackle this issue. In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra. Nowadays, the integration of high-performance imagery sensors (e.g., RGB, multispectral, hyperspectral, thermal, and SAR) and unmanned mobile platforms (e.g., satellites, UAVs, and terrestrial agricultural robots) are yielding a huge number of high-resolution farmland images, in which rich crop information is compressed. However, this has been accompanied by challenges, i.e., ways to swiftly and efficiently making full use of these images, and then, to perform fine crop management based on information-supported decision making. In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. More and more agricultural scientists are paying attention to applications of deep learning in image-based farmland observations, such as land mapping, crop classification, biotic/abiotic stress monitoring, and yield prediction. To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing. Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales are the focus of this review. We hope that this work can act as a reference for the global agricultural community regarding DL in PA and can inspire deeper and broader research to promote the evolution of modern agriculture.

Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production.

Sun-induced fluorescence in the far-red region (SIF) is increasingly used as a remote and proximal-sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves. Here, we examined how the 2018 European heatwave (HW) affected the GPP-SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy-scale SIF measurements, GPP estimated from an eddy covariance tower, and active pulse amplitude modulation fluorescence. The HW caused an inversion of the photosynthesis-fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), that is, a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF. Our results show the complex modulation of the NPQ-SIF-GPP relationship at an extreme level of heat stress, which is not completely represented in state-of-the-art coupled radiative transfer and photosynthesis models.

Exploration study of proximate and mineral composition of raw and boiled storage scale leaf samples of Borassus flabellifer L.

Dioecious Palm is known as Borassus flabellifer L. Every part of the plant has been used by the people for various purposes, right from as a source of food, medicine to handicrafts made from palm leaves. The edible part of the seedling is scale leaf or cataphyll which is enclosed by cotyledonous sheath (seed shoot). The present study aimed at analysing the storage scale leaf for understanding its comparative proximate and mineral values of raw and boiled scale leaf for establishing its nutritive use. The crude protein content is lower in the raw scale leaf sample (5.84%) than the boiled scale leaf (6.40%). The boiled sample had higher crude carbohydrate content (16.53%) than the raw scale leaf sample (11.59%), which shows that the boiled scale leaf is a good source of carbohydrates. The crude lipid content of raw scale leaf (1.72%) is significantly lower than the boiled scale leaf (4.39%). Crude fibre content ranged from 1.98% in raw scale leaf to 2.11% in the boiled scale leaf. The moisture content lowered in the boiled scale leaf (57.31%) than the raw scale leaf (58.93%). The calorific value was higher in the boiled scale leaf sample (131.23 cal/100 g) than the raw scale leaf sample (85.2 cal/100 g). The amount of minerals were also analysed and compared between the boiled and raw samples (g/100 g), viz; Magnesium (raw 0.202 and boiled 0.163), Potassium (raw 1.336 and boiled 1.203), Calcium (raw 0.709 and boiled 0.667), Iron (raw 0.101 and boiled 0.106) and Zinc (raw 0.006 and boiled 0.034). Significant differences (p

Onion (Allium cepa L.): Breeding for quality traits and export

Among Allium crops, onion and garlic are exclusively grownand consumed in the form of fresh as well as processedproducts. In India, onion and garlic are integral part of dailydiet in every household, grown under three crop seasonsi.e. kharif, late kharif and rabi. The Rabi crop occupies morethan 50% of production. India is the 2nd largest producer ofonion and garlic after China. Development of varietieshaving attractive bulbs with uniform in size, shape, colourand uniform maturity, high dry matter and long storage life.As well as additional desirable features like intact andattractive skins, thick leaf scales as uniform rings, singlecentred bulb, thin neck, and resistant to premature bolting,diseases and pests. Each of these characteristics isgenetically inherited but can be modified by environmentaland cultural practices. However, several organizationsworked/released many varieties and hybrids as well asproduction technologies, which increases area, production,productivity and export of onion.v

Estimating sunflower canopy conductance under the influence of soil salinity

Canopy conductance (gc) is the key parameter to quantify eco-hydrology processes. Salinity stress is one of the most important abiotic stresses to growth of crops in arid and semi-arid areas. Understanding the influence of salinity on stomatal movement and accurately describing gc under salt stress are of great significance to quantify agro-hydrology processes affected by salt. Therefore, we measured stomata conductance (gs) of sunflower leaves at different growth stages and soil salinity conditions in two consecutive years. At the same time, the evapotranspiration above canopy and the dynamic changes of soil moisture and salinity in root zone were continuously monitored. It was found that under the same meteorological conditions, leaf gs decreased with the increase of soil salt content. Relatively, gs was more sensitive to salt at seedling and maturity stages than at budding and flowering stages. Furthermore, an empirical formula describing the effect of soil salinity on gs was added to the Jarvis multi-factor multiplication gs model, which used leaf area index (LAI) as a parameter to express the salt tolerance of different growth stages. Effective LAI was used to upgrade the model from leaf scale to canopy scale. gc calculated by Penman-Monteith equation using the farmland canopy evapotranspiration measured by the eddy covariance system was used to evaluate the applicability of model. The results showed that the model could simulate gc well on both the half-hour and the daily average scale. Nash-Sutcliffe efficiency coefficient of the daily average scale model was about 0.65, and determination coefficient of the simulation results of different scales in two years were all above 0.66. Our findings could improve the understanding of influence of salinity on stomatal movement in such areas and provided a basis for simulating canopy evapotranspiration and photosynthesis under salt stress.

Implications of Urban Land Management on the Cooling Properties of Urban Trees: Citizen Science and Laboratory Analysis

Trees participate in mitigating the urban heat island phenomenon thanks to their transpiration and shading. This cooling potential is highly dependent on leaf area. Nevertheless, leaf traits potentially vary across different land management practices in urban settings, thereby challenging the models used to estimate thermal budgets. The present study aims to investigate the variability of leaf area traits of linden (Tilia spp.) urban trees, and their effect on simulated tree transpiration. Reconstruction of the leaf area was undertaken at the tree scale at three different urban land management sites from three cities: London and Birmingham (UK) and Chantilly (France). The reconstruction combined allometric measurements at shoot and leaf scales, and a tree-scale 3D digitization with laboratory analysis using field data collected by citizen scientists. The management practices had a significant impact on leaf area, and on tree allometric relationships, which were propagated through the reconstruction process. Relative differences between the management practices ranged between 12% and 48% according to the city where the variable was considered (e.g., leaf area index, total leaf area, or tree transpiration). Trees in managed sites (i.e., individualized leaf crowns, frequent leaf litter removal, and standard thinning/pruning operations) develop a higher leaf area, thus promoting cooling potential. This study shows that the variability of leaf traits, and their responses to different land management, can be studied by comprehensive data collection through citizen science and lab-based modelling. It also highlights the importance of appropriate, well-designed urban planning, where landscaping using urban trees can play an even better role in climate proofing cities.

Landscape conservation and maternal environment affect genetic diversity and the physiological responses of Euterpe edulis (Arecaceae) progenies to light availability

The Brazilian Atlantic Forest (AF) is severely deforested and the remaining forest fragments are generally reduced and disconnected. Euterpe edulis (Arecaceae) is a native AF palm rarely found in forest fragments inserted in more deforested landscapes. We investigate how forest fragments inserted in landscapes with different levels of deforestation influence both the genetic diversity and physiological traits of E. edulis progenies. For this, we evaluate the genetic diversity and physiological variables associated with carbon balance at leaf and whole-plant leaf scales of E. edulis progenies to light availability. We collected fruits from six mother plants, being three from a forest fragment inserted in a more forested landscape (83% of forest cover) and three from a forest fragment inserted in a more deforested landscape (55% of forest cover). The seeds were germinated and, after reaching the juvenile stage, 17 microsatellite markers were used to perform a genetic diversity analysis. The progenies were subjected to 18 light treatments (0.72–22.1 mol photons m-2 day-1) for the analysis of the carbon balance at leaf and whole-plant scales. We found that the origin of the mother plant proved to affect some genetic parameters, since progenies from mother plants of a forest fragment inserted in the more forested landscape had a greater number of private and effective alleles. In addition, progenies from this forest fragment had a great acclimation capacity to the light levels, with higher growth rates and vigor. Our results provide evidence that conservation actions are necessary for this species, including selecting and replanting progenies from the forest fragments inserted in more forested landscapes. It could be a way for alternative genotypes to raise the levels of alleles and thus avoid the collapse of these populations in fragments inserted in deforested landscapes where a low number of adult and juvenile individuals are found.

Leaf- and ecosystem-scale water use efficiency and their controlling factors of a kiwifruit orchard in the humid region of Southwest China

Understanding the characteristics of water use efficiency (WUE) and its controlling factors in the agricultural ecosystem can help us better understand the coupled process between water use and carbon assimilation spatially. The characteristics of the leaf- and ecosystem-level water, carbon and WUE variations as well as their drivers in a kiwifruit orchard were systematically analyzed during the growing seasons of 2018–2020. The results showed that leaf transpiration rate (Tr), net photosynthetic rate (Pn) and instantaneous WUE (WUEi) in exposed leaves were 2.25–2.77, 8.32–9.44 and 2.53–3.77 times higher than those of shaded leaves. Tr and Pn were significantly affected by photosynthetic active radiation (PAR) and stomatal conductance (gs). Air temperature (Ta), leaf water vapor deficit (VPDl) and stomatal conductance (gs) affected WUEi through leaf water consumption, while PAR affected WUEi through leaf photosynthetic process. Averaged evapotranspiration (ET), gross primary productivity (GPP) and ecosystem WUE (eWUE) were 551.30 ± 75.92 kg H2O m-2, 1475.37.59 ± 201.25 g C m-2 and 2.68 ± 0.04 g C kg−1 H2O, respectively. Global total radiation (Rg), Ta and VPD were the dominant climatic factors affecting ET and GPP, while Rg and wind speed (U2) were significantly correlated with eWUE (p < 0.01). WUEu, defined as the ratio of Pn·VPDl0.5 and Tr, could be considered as an effective indicator quantifying the coupled relationship between Tr and Pn·VPDl0.5 at the leaf scale. The coupled relationship between ET and GPP was strengthened after incorporating the effect of VPD on GPP due to reduced time lags among GPP, ET and VPD at the ecosystem scale. Rg, Ta, U2 and VPD contributed to greater effects through ET than that of GPP on eWUE, while the differences between them were reduced after incorporating VPD on GPP, which may be the source of the dependence of iWUE (GPP·VPD/ET) and uWUE (GPP·VPD/ET) on environmental conditions. This study enriches the scarce literature on what drives multi-scale water and carbon in a humid orchard and contributes to improving the understanding of the coupled process of water and carbon incorporating the effect of VPD at leaf and ecosystem scales.

Semi-Automatic Spectral Image Stitching for a Compact Hybrid Linescan Hyperspectral Camera towards Near Field Remote Monitoring of Potato Crop Leaves.

The miniaturization of hyperspectral cameras has opened a new path to capture spectral information. One such camera, called the hybrid linescan camera, requires accurate control of its movement. Contrary to classical linescan cameras, where one line is available for every band in one shot, the latter asks for multiple shots to fill a line with multiple bands. Unfortunately, the reconstruction is corrupted by a parallax effect, which affects each band differently. In this article, we propose a two-step procedure, which first reconstructs an approximate datacube in two different ways, and second, performs a corrective warping on each band based on a multiple homography framework. The second step combines different stitching methods to perform this reconstruction. A complete synthetic and experimental comparison is performed by using geometric indicators of reference points. It appears throughout the course of our experimentation that misalignment is significantly reduced but remains non-negligible at the potato leaf scale.

Intercropping and Nitrogen Fertilization Altered the Patterns of Leaf Senescence in Sorghum

Leaf senescence regulates grain yield. However, the modulation of leaf senescence in sorghum under legume-based intercrop systems and nitrogen (N) fertilization is not known. The objective of the study was to investigate the effect of intercropping two sorghum (Gadam and Serena) and cowpea (K80, M66) varieties and sole cropping systems and different fertilizer N rates (0, 40, and 80 kg·N·ha−1) on the time course of postflowering sorghum leaf senescence and understand how senescence modulates grain yield. The experiment was laid out in a randomized complete block design with a split-plot arrangement with three replications. Leaf senescence was assessed from flowering to maturity at (a) whole-plant level by the visual scoring of green leaves and (b) flag leaf scale by measuring leaf greenness with a SPAD 502 chlorophyll meter. A logistic function in SigmaPlot was fitted to estimate four traits of leaf senescence, including minimum and maximum SPAD (SPADmin, SPADmax), time to loss of 50% SPADmax (EC50), and the rate of senescence. Irrespective of the cowpea variety, intercropping reduced sorghum grain yield by 50%. The addition of N increased yield by 27% but no effect was detected between 40 and 80 kg·N ha−1. Intercropping delayed leaf senescence at the whole plant by 0.2 leaves plant−1 day−1 but reduced SPADmax of the flag by 8 SPAD units and rate of senescence by 4 SPAD units day−1 compared with sole crop system. Fertilizer N delayed leaf senescence ( P ≤ 0.05 ) at whole-plant and flag leaf scales. Cropping System × nitrogen modulated senescence at whole-plant and flag leaf scales and sorghum grain yield but marginally influenced other traits. While EC50 did not correlate with grain yield, faster rates of senescence and leaf greenness were associated with high yield under the sole crop system. Overall, N was the main factor in driving sorghum leaf senescence while the intercropping effect on senescence was nonfunctional. Effects of competition in sorghum-legume intercropping and source-sink relationships on the patterns of leaf senescence deserve further investigation.

Feasibility in Estimating the Dry Leaf Mass and Specific Leaf Area of 50 Bamboo Species Based on Nondestructive Measurements

Specific leaf area (SLA) is a good predictor of aboveground net primary productivity. However, the SLA of bamboo species is generally estimated on the basis of destructive measurements rather than the cost-effective and recyclable nondestructive measurements using easily accessible leaf traits such as leaf length (L) and width (W). Considering the strong empirical relationships between leaf area (LA) and leaf structural parameters of bamboo species that were developed by previous studies, this study explores the feasibility of estimating the leaf dry mass (LDM) and SLA of 50 bamboo species using L and W. The results show that the Montgomery equation and its similar forms precisely estimated LA of the 50 bamboo species at both leaf scale (R2 &gt; 0.96 and MAE% &lt; 4.67%) and the canopy scale (R2 &gt; 0.99 and RMSE &lt; 0.09); the LDM of the 50 bamboo species could also be estimated using L and W at both leaf scale (R2 &gt; 0.52 and MAE% &lt; 26.35%) and the canopy scale (R2 &gt; 0.99 and RMSE &lt; 0.003), and the estimated mean SLA of each of the 50 bamboo species had good agreement with the measured values (R2 &gt; 0.99 and RMSE &lt; 1.88) because of the precisely estimated mean LA and mean LDM at the canopy scale, indicating the feasibility of estimating SLA of the 50 bamboo species at the canopy scale based on nondestructive measurements. However, the empirical relationships used for mean SLA estimations are not suitable for SLA estimations at the leaf scale because of the uncertainties in the estimated LDM at the leaf scale.

Evidence for efficient nonevaporative leaf-to-air heat dissipation in a pine forest under drought conditions.

The drier climates predicted for many regions will result in reduced evaporative cooling, leading to leaf heat stress and enhanced mortality. The extent to which nonevaporative cooling can contribute to plant resilience under these increasingly stressful conditions is not well known at present. Using a novel, high accuracy infrared system for the continuous measurement of leaf temperature in mature trees under field conditions, we assessed leaf-to-air temperature differences (ΔTleaf-air ) of pine needles during drought. On mid-summer days, ΔTleaf-air remained < 3°C, both in trees exposed to summer drought and in those provided with supplemental irrigation, which had a more than 10-fold higher transpiration rate. The nonevaporative cooling in the drought-exposed trees must be facilitated by low resistance to heat transfer, generating a large sensible heat flux, H. ΔTleaf-air was weakly related to variations in the radiation load and mean wind speed in the lower part of the canopy, but was dependent on canopy structure and within-canopy turbulence that enhanced the H. Nonevaporative cooling is demonstrated as an effective cooling mechanism in needle-leaf trees which can be a critical factor in forest resistance to drying climates. The generation of a large H at the leaf scale provides a basis for the development of the previously identified canopy-scale 'convector effect'.

Using leaf-based hyperspectral reflectance for genotype classification within a soybean germplasm collection assessed under different levels of water availability

ABSTRACT Because of the major international issue of intellectual property rights over seed technology, time-efficient methodologies that could characterize and monitor soybean genotypes are increasingly needed as they contribute to royalty collection and better management of seed genetics, leading to more efficient production strategies. This research aims at developing models capable of spectrally classifying soybean genotypes, at leaf scale, under different levels of water availability. Leaf reflectance spectra were collected using the Fieldspec 3 Jr. spectroradiometer, a hyperspectral proximal sensor. Two experiments were conducted at Embrapa Soja (Brazilian Agricultural Research Corporation). Experiment I was carried out in a plant growth chamber, using 12 soybean genotypes with different tolerance levels to drought, submitted to irrigation and water deficit treatments, where 214 spectral samples were collected. Experiment II was conducted in a greenhouse, using 164 soybean genotypes (from 15 different countries) with different responses to drought, submitted to irrigation and water deficit treatments, where 6,046 spectral samples were collected. Principal component analysis (PCA) was used to explore the qualitative spectral differences among the evaluated genotypes within each spectral dataset, and the stepwise procedure was carried out to select the wavelengths that best discriminate the soybean genotypes. Linear discriminant analysis (LDA) was used to obtain a model of classification of each leaf-based reflectance spectrum for each soybean genotype by its spectral behaviour. The acquired leaf reflectance was similar among soybean genotypes. The PCA explained over 96% of the spectral variance in the first three principal components, and the stepwise procedure selected 70 and 410 spectral bands in each dataset. In Experiment I, LDA presented an accuracy of 100%. In Experiment II, the LDA classified 149 genotypes with an accuracy of over 90%. The obtained results demonstrate the great potential of the spectral classification of soybean genotypes at leaf scale, regardless of the water status of the plants.

A random forest model for the classification of wheat and rye leaf rust symptoms based on pure spectra at leaf scale.

The pure spectra acquisition of plant disease symptoms is essential to improving the reliability of remote sensing methods in crop protection. The reflectance values read from the pure spectra can be used as valuable training data for development of algorithms designed for plant disease detection at leaf and canopy scale. The aim of this paper is to identify and distinguish spectrally the leaf rust symptoms caused by two closely related special forms (f. sp.) of Puccinia recondita f. sp. tritici on wheat and Puccinia recondita f. sp. recondita on rye at leaf scale. Spectral measurements were made with FieldSpec 3 spectrometer in the wavelength range of 350-2500nm. The spectrometer was connected to a microscope by optical fiber. Raw spectra of uredinia, chlorotic discoloration, green leaves, senescent inoculated leaves and senescent uninoculated leaves of wheat and rye, all of which obtained for this study, were investigated with a view towards making an automized classification of plant species and their phases. The created Random Forest models were tested separately using pure spectra, and from these vegetation indices were derived as predictors. Three vegetation indices, namely CRI, PRI and GNDVI, appeared to be the most robust in terms of distinguishing uredinia from other symptoms on rye and wheat leaves. PRI, EVI, NDVI705, and GNDVI were the most suitable for distinguishing uredinia, chlorotic discoloration, and green leaf stages on rye. That tusk on wheat leaves can be recognized if seven indices (PRI, MSAWI, SAVI, NDVI, NDVI705, GNDVI and RVI) are used together. For the classification of all disease symptoms for both plant species, the most useful were wavelengths in the VIS range: 431-436, 696-703 and 646-686nm. However, the ranges of SWIR wavelengths (1938, 1955) and NIR wavelengths (1099-1104) also have a high contribution to the discrimination accuracy of the model. In the classification of all disease symptoms, the most important vegetation indices were CRI, OSAVI, and GNDVI. Analysis of the results revealed the advantage of the model based on the selected spectral wavelengths (Hit Rate of 96.6%) in comparison with predictions based on vegetation indices alone (Hit Rate of 91.7%). Both approaches show the highly applicable character of utilizing high quality spectral products such as satellite images in reducing operational costs of crop protection.

Responses of tree leaf gas exchange to elevated CO2 combined with changes in temperature and water availability: A global synthesis

AbstractAimAn elevated CO2 concentration (eCO2) can increase photosynthesis (Asat) and reduce stomatal conductance (gs); hence, it can benefit tree growth directly by increasing carbon availability and/or indirectly by saving water. However, uncertainties remain regarding the direction and magnitude of these effects, particularly in combination with changes in temperature and water availability.LocationGlobal.Time period1986–2020.Major taxa studiedTrees.MethodsWe compiled 3,391 observations from 270 studies and performed a meta‐analysis on: (1) the responses of Asat, gs and intrinsic water‐use efficiency (iWUE) to eCO2, in addition to variations in these responses between species (phylogeny and leaf habit), within species (tree age) and within individuals (canopy position and leaf age); (2) the effects of changes in temperature and/or water availability on these responses; and (3) whether Asat and gs acclimatize to eCO2 in parallel.ResultsWe found that eCO2 stimulated Asat (β‐factor = .68) and decreased gs (β‐factor = −.36) and that iWUE increased in proportion to eCO2 (β‐factor = 1.01), which conform with the theory of optimal stomatal behaviour. Meanwhile, conifers and older trees showed greater increases in Asat but less decreases in gs than angiosperms and young trees, respectively, with a proportional increase in iWUE being independent of phylogeny and tree age, which support the theory further. Warming had little effect on the responses of Asat and gs to eCO2, whereas drought amplified the Asat response but attenuated the gs response. However, Asat, rather than gs, acclimatized to eCO2, owing to nitrogen reduction, active investment of nitrogen and sink limitation.Main conclusionsOur findings suggest that eCO2 can enhance carbon uptake and reduce water loss at the leaf scale, which may be modified by water stress. This study improves our physiological understanding of tree gas exchange in a changing climate.

Modelling the long-term dynamics of tropical forests: From leaf traits to whole-tree growth patterns

Tropical forests are the most diverse terrestrial ecosystems and home to numerous tree species competing for resources in space and time. Functional traits influence the ecophysiological performance of tree species, yet the relationship between traits and emergent long-term growth patterns is poorly understood. Here, we present a novel 3D forest stand model in which growth patterns of individual trees and forest stands are emergent properties of leaf traits. Individual trees are simulated as 3D functional-structural tree models (FSTMs), considering branches up to the second order and leaf dynamics at a resolution of one cubic meter. Each species is characterized by a set of leaf traits that corresponds to a specific position on the leaf economics spectrum and determines light-driven carbon assimilation, respiration and mortality rates. Applying principles of the pipe model theory, these leaf scale processes are coupled with within-tree carbon allocation, i.e., 3D tree growth emerges from low-level processes. By integrating these FSTMs into a dynamic forest stand model, we go beyond modern stand models to integrate structurally detailed internal physiological processes with interspecific competition and interactions with the environment in diverse tree communities. For model calibration and validation, we simultaneously compared a large number of emergent patterns at both the tree and forest levels in a pattern-oriented modelling framework. At the tree level, the specific leaf area and correlated leaf traits determined the maximum height and age of trees, as well as their size-dependent growth rate and shade tolerance. Trait variations along the leaf economics spectrum led to a continuous transition from fast-growing, short-lived and shade-intolerant to slow-growing, long-lived and shade-tolerant trees. These emerging patterns resemble well-known functional tree types, indicating a fundamental impact of leaf traits on long-term growth patterns. At the forest level, a large number of patterns taken from lowland Neotropical forests were reproduced, indicating that our forest model simulates structurally realistic forests over long time spans. Our ecophysiological approach improves the understanding of how leaf level processes scale up to the tree and the stand level, and facilitates the development of next-generation forest models for species-rich forests in which tree performance emerges directly from functional traits.

Interactions of gall-formers and leaf-chewers on a tropical tree fern: evidence for non-repulsion and co-occurrence between insect guilds.

Host plant selection by herbivores is driven by a complex array of cues, including leaf traits and previous leaf damage. Herbivore-associated cues to host selection at the plant and leaf scale aid understanding of mechanisms responsible for host preference that might translate into increased performance, as well as processes structuring herbivore populations mediated by interactions. We investigated how changes induced by a galling insect in the tropical fern Cyathea phalerata act as repellent or attractant cues for sawfly feeding and the effects of leaf size on herbivory levels. We recorded gall abundance, damage by chewers, leaf size, plant nutritional quality, phenolic concentration and leaf anatomical traits between galled and non-galled leaf samples. Galled samples contained less N, higher levels of phenolics and higher C/N ratio. However, leaf-chewing damage did not differ between galled and non-galled leaves. The gall structure was avoided by chewers, as it had high concentrations of phenolics, lignification and suberization. Larger leaves sustained higher gall abundance, but leaf size did not have a significant effect on chewer damage. A co-occurrence index calculated for both guilds indicated that galls and chewers exhibited a distribution that did not differ from random, reinforcing that the two guilds on C. phalerata do not show patterns of repulsion such as those maintained by interspecific competition. Sawflies dismissing chemical cues indicate that the increase in phenolics caused by galling insects does not generate increased protection of the galled pinnules. Our results highlight ferns as key resources for herbivores and as a potential plant group to study new research avenues on plant-insect interactions.

A Hyperspectral Library of Foliar Diseases of Wheat.

This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions.

Unpacking the drivers of diurnal dynamics of sun-induced chlorophyll fluorescence (SIF): Canopy structure, plant physiology, instrument configuration and retrieval methods

Sun-induced chlorophyll fluorescence (SIF) from spaceborne sensors is a promising tool for global carbon cycle monitoring, but its application is constrained by insufficient understanding of the drivers underlying diurnal SIF dynamics. SIF measurements from ground-based towers can reveal diurnal SIF dynamics across biomes and environmental conditions; however, meaningful interpretation of diurnal variations requires disentangling impacts from canopy structure, plant physiology, instrument configuration and retrieval methods, which often interact with and confound each other. This study aims to unpack these drivers using 1) concurrent ground and airborne canopy-scale and leaf-scale measurements at a corn field, 2) a mechanistic SIF model that explicitly considers the dynamics of photochemistry (via the fraction of open photosystem II reaction centers, qL) and photoprotection (via nonphotochemical quenching, NPQ) as well as their interactive dependence on the sub-canopy light environment, and 3) cross-comparison of SIF instrument configurations and retrieval methods. We found that crop row orientations and sun angles can introduce a distinctive midday dip in SIF in absence of stress, due to a midday drop of absorbed photosynthetically active radiation (APAR) when crop rows are north-south oriented. Canopy structure caused distinctive responses in both qL and NPQ at different positions within the vertical canopy that collectively influenced fluorescence quantum yield (ΦF) at the leaf scale. Once integrated at the canopy scale, diurnal dynamics of both APAR and canopy escape probability (ε) are critical for accurately shaping diurnal SIF variations. While leaf-level qL and NPQ exhibited strong diurnal dynamics, their influence was attenuated at the canopy scale due to opposing effects on SIF at different canopy layers. Furthermore, different system configurations (i.e., bi-hemispherical vs. hemispherical-conical) and retrieval methods can bias the SIF magnitude and distort its diurnal shapes, therefore confounding the interpretation of inherent strength and dynamics of SIF emission. Our findings demonstrate the importance of crop row structures, interactive variations in canopy structure and plant physiology, instrument configuration, and retrieval method in shaping the measured dynamics of diurnal SIF. This study highlights the necessity to account for these factors to accurately interpret satellite SIF, and informs future synthesis work with different SIF instrumentation and retrieval methods across sites.

Blue and Far-Red Light Affect Area and Number of Individual Leaves to Influence Vegetative Growth and Pigment Synthesis in Lettuce.

Published work indicates that high percentage of blue light can enhance pigment levels but decreases growth, while addition of far-red light to growth light can increase quantum efficiency and photosynthesis in leafy greens. Combining high-energy blue light with low-energy far-red light may increase both vegetative growth and pigment levels. However, the effect of high-energy blue and low-energy far-red light on the vegetative growth and pigments synthesis is unclear. This information can be potentially useful for enhancing the levels of pigments with nutritional value (e.g., beta-carotene and anthocyanins) in the produce grown in vertical farms. We grew romaine lettuce (cv. Amadeus) under similar light intensity (approximately 130 μmol⋅m–2⋅s–1) but different proportions of red: blue: far-red including 90:10: 0 (“High-R”), 50: 50: 0 (“High-B”), and 42: 42: 16 (“High-B+FR”) for 31 days. Results indicated that canopy area and leaf photosynthetic rate of lettuce plants was reduced in the High-B, thereby reducing plant growth. We did not observe photosynthesis enhancement in the High-B+FR. Instead, plants clearly showed photomorphogenic effects. The phytochrome photostationary state (PSS) decreased with far-red addition, resulting in reduced leaf number per plant. This was likely to shift the allocation of resources toward elongation growth for shade avoidance. Further, we observed an increase in the area of individual leaves, canopy area, and shoot dry weight in the High-B+FR. However, these appear to be an indirect consequence of decreased leaf number per plant. Our results also indicate that changes in expansion growth at individual leaf scale largely regulated pigment concentration in plants. As individual leaf area became smaller (e.g., High-B) or larger (e.g., High-B+FR), the levels of pigments including chlorophylls and beta-carotene increased or decreased, respectively. Area of individual leaves also positively influenced canopy area (and likely light interception) and shoots dry weight (or vegetative growth). Our study provides additional insights into the effects of high-energy blue and low-energy far-red light on individual leaf number and leaf growth, which appear to control plant growth and pigment levels in lettuce.