Measured Leaf Area Research Articles

Do the same genus plants have consistent adaptation strategies in cold-wet environments? A case study of leaf suitability of 19 Syringa species

Carbon cost-benefit theory indicates that leaf photosynthesis benefit and structure cost have scaling relationship. However, this relationship has not been tested in the perennial cold-wet regions. Shorter growing seasons in cold-wet climates severely limit plant growth, making it important to explore the light interception and economic strategies for the survival of plants in this region. We measured leaf area (LA), leaf fresh mass (LFM) and leaf dry mass (LDM) of 19 Syringa species around Xi’ning city to explore leaf scaling relationship and adaptation strategies. Our results showed that LA, LFM and LDM varied among species, with the coefficients of variation (CV) among species being 64.61%, 76.43% and 65.94%, respectively. For all species, LDM with LFM and LA showed isometric growth, and only LA and LFM followed allometric growth with a scaling exponent greater than 1 (p < 0.05). Linear correlation indicated that, with the increase of LA, the scaling constant tended diminishingly. Ultimately, according to the CSR (Competitor - Stress tolerator - Ruderal) strategy division, nineteen plants were divided into strategy CS (competitor & stress tolerator) and strategy S/CS (stress tolerator/competitor & stress tolerator). What’s more, LA, LFM and LDM of CS strategy plant were all significantly greater than S/CS strategy. In conclusion, plants in cold-wet habitats tend to have a more conservative economic strategies; and combined with the CSR theory, these plants are more competitive and stress tolerators. More attention should be paid to the effects of harsh environments on plant life history strategies.

Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud

Exploring the key technologies of agricultural robots is an inevitable trend in the development of smart agriculture. It is significant to continuously transplant and develop novel algorithms and models to update agricultural robots that use light detection and ranging (LiDAR) as a remote sensing method. This paper implements a method for extracting and estimating rapeseed leaves through agricultural robots based on LiDAR point cloud, taking leaf area (LA) measurement as an example. Firstly, the three-dimensional (3D) point cloud obtained with a terrestrial laser scanner (TLS) were used to extract crop phenotypic information. We then imported the point cloud within the study area into a custom hybrid filter, from which the rapeseed point cloud was segmented. Finally, a new LA estimation model, based on the Delaunay triangulation (DT) algorithm was proposed, namely, LA-DT. In this study, a crop canopy analyzer, LAI-2200C, was used to measure rapeseed LA in farmland. The measured values were employed as standard values to compare with the calculated results obtained using LA-DT, and the differences between the two methods were within 3%. In addition, 100 individual rapeseed crops were extracted, and the output of the LA-DT model was subjected to linear regression analysis. The R² of the regression equation was 0.93. The differences between the outputs of the LAI-2200C and LA-DT in these experiments passed the paired samples t-test with significant correlation (p &lt; 0.01). All the results of the comparison and verification showed that the LA-DT has excellent performance in extracting LA parameters under complex environments. These results help in coping with the complex working environment and special working objects of agricultural robots. This is of great significance for expanding the interpretation methods of agricultural 3D information.

Design of Crop Leaf Area Measurement using Webcam and Raspberry Pi

This paper describes an electronic system for measuring the number of leaves and the total leaf area. This system is built to make it easier for users to compute several leaves and measure leaf area electronically. The number of leaves and leaf area are used to determine the level of fertility of a plant. Several important factors in measuring leaf area are the accuracy and the speed of measurement. The stages conducted in this research consist of needs analysis, design, implementation and testing. The system built uses a mini PC Raspberry Pi as the data processor and a webcam to capture images. Moreover, the image is processed using Binary threshold and Otsu threshold methods. The results showed that the designed system was functioning properly with an error rate of 0% for the number of leaves calculation and a 0.39% error rate in the of leaf area measurement.

Geometric models for plant leaf area estimation from 3D point clouds: A comparative study

Measuring leaf area is a critical task in plant biology. Meshing techniques, parametric surface modelling and implicit surface modelling allow estimating plant leaf area from acquired 3D point clouds. However, there is currently no consensus on the best approach because of little comparative evaluation. In this paper, we provide evidence about the performance of each approach, through a comparative study of four meshing, three parametric modelling and one implicit modelling methods. All selected methods are freely available and easy to use. We have also performed a parameter sensitivity analysis for each method in order to optimise its results and fully automate its use. We identified nine criteria affecting the robustness of the studied methods. These criteria are related to either the leaf shape (length/width ratio, curviness, concavity) or the acquisition process (e.g. sampling density, noise, misalignment, holes). We used synthetic data to quantitatively evaluate the robustness of the selected approaches with respect to each criterion. In addition we evaluated the results of these approaches on five tree and crop datasets acquired with laser scanners or photogrammetry. This study allows us to highlight the benefits and drawbacks of each method and evaluate its appropriateness in a given scenario. Our main conclusion is that fitting a Bézier surface is the most robust and accurate approach to estimate plant leaf area in most cases.

Seed size effects on plant establishment under low atmospheric CO2, with implications for seed size evolution.

Low atmospheric CO2 concentration depresses photosynthesis and resource use efficiency, and therefore can inhibit phases of the life cycle such as seedling establishment. Seed reserves can compensate for photosynthetic inhibition by accelerating seedling growth. We therefore hypothesize that seedlings arising from large seeds show less inhibition from low atmospheric CO2 than young plants from small seeds. Seed size effects on seedling responses to low CO2 may also be enhanced in warm environments, due to greater photorespiration at high temperature. Phaseolus and Vigna seeds differing in mass by over two orders of magnitude were planted and grown for 14 d in growth chambers with CO2 concentrations of 370, 180 or 100 ppm, in thermal regimes of 25 °C/19 °C, 30 °C/24 °C or 35 °C/29 °C (day/night). We measured leaf area expansion, shoot growth and mortality of the seedlings arising from the variously sized seeds at 14 days after planting (14 DAP). Relative to small-seeded plants, large-seeded genotypes produced greater leaf area and shoot mass at 14 DAP across the range of CO2 treatments in the 25 °C/19 °C and 30 °C/24 °C regimes, and at 100 ppm in the 35 °C/29 °C treatment. The proportional decline in leaf area and seed mass with CO2 reduction was generally greater for seedlings arising from small than from large seeds. Reductions in leaf area due to CO2 reduction increased in the warmer temperature treatments. In the 35 °C/19 °C treatment at 100 ppm CO2, seedling mortality was greater in small- than in large-seeded genotypes, and the small-seeded genotypes were unable to exit the seedling stage by the end of the experiment. The results support a hypothesis that seedlings from large seeds grow and establish better than seedlings from small seeds in warm, low CO2 environments. During low CO2 episodes in Earth's history, such as the past 30 million years, large seeds may have been favoured by natural selection in warm environments. With the recent rise in atmospheric CO2 due to human activities, trade-offs between seed size and number may already be affected, such that seed size today may be non-optimal in their natural habitats.

Determination of species-specific leaf angle distribution and plant area index in a cool-temperate mixed forest from UAV and upward-pointing digital photography

Leaf angle distribution (LAD) is an essential canopy element that controls the foliage projection function (G-function), which is a parameter for estimating the plant area index (PAI). In recent studies, digital cover photography (DCP) has been applied to estimate the PAI (PAIDCP) in many terrestrial ecosystems. However, few studies related to this topic have been performed in mixed forests, where LAD features differ among multiple species. This study sought to evaluate the PAIDCP in a cool temperate mixed deciduous-coniferous forest in western Japan using a field-based G-function that was estimated from digital images captured by using a small uncrewed aerial vehicle (UAV; a quadcopter). The intensive UAV observations showed that measured throughout a vertical canopy profile of the deciduous broadleaved tree species, LAD was plagiophile-planophile for Fagus crenata, but was planophile throughout the canopy for Carpinus tschonoskii. Meanwhile, for the other dominant coniferous Abies firma species, we assumed either plagiophile or spherical for LAD and the G-function based on previous studies instead of measuring the parameters directly using the small UAV. With the species-specific LAD and G-function of dominant tree species, we compared the four-year PAIDCP time series with those based on a direct method using litter-trap inventories (PAIlit). The results indicate that PAIDCP provided more suitable representation of the canopy than that obtained assuming the spherical LAD for all tree species in the forest, although estimations of PAIlit includes uncertainties, mainly associating with measurements of specific leaf area (SLA). Our study highlights that in mixed forests with a tall canopy and multiple species it is important to ascertain species-specific LAD values for accurate estimation of the PAI using digital cover photography. Overall, we argue that the combined UAV–DCP photographic approach serves as a powerful tool for estimating the PAI.

Assessing the Effects of Gas Flaring on the Growth Physiology of Tea Crop

Assam is known worldwide for its production of tea; the state contributes around 52 percent to the India’s tea production.Similar to tea, crude oil operations date back to the 19th century and accounts for about 15 percent of India’s crude output, thus making these sectors important economically. However apart from the positive effects, crude oil operations result in gas flaring, which is already recognized as an environmental problem. Taking into context the harmful effects of gas flaring the present investigation aims to study the impact of gas flaring on physiological parameters of tea plant with respect to growth adjacent to oil field in Merbil Majuli OCS 6(WEST) in Dibrugarh district of Assam during 2019-2020. The study was designed with Randomized Complete Block Design (RCBD) accommodating five levels under distance and two levels under seasons. Plant samples were collected at random intervals and estimated for plant physiological parameters such as water saturation deficit(WSD),relative turgidity, stomatal count, polyphenol content, specific leaf weight, caffeine content, chlorophyll content, plucking point density. The present study found that gas flaring had a significant effect on the physiology of the tea crop in terms of growth. The study found a gradual decrease in relative turgidity, specific leaf weight, plucking point density, stomatal count, leaf area measurement, polyphenol content, chlorophyll content, caffeine content, but an increase in water saturation deficit of green tea leaves on plants growing closer to the flare pit.

Leaf area measurement of green wall plants using deep learning with terrestrial laser

Leaf area measurement of green wall plants using deep learning with terrestrial laser

Measurement of Overlapping Leaf Area of Ice Plants Using Digital Image Processing Technique

Non-destructive and destructive leaf area estimation are critical in plant physiological and ecological experiments. In modern agriculture, ubiquitous digital cameras and scanners are primarily replacing traditional leaf area measurements. Thus, measuring the leaflet’s dimension is integral in analysing plant photosynthesis and growth. Leaf dimension assessment with image processing is widely used nowadays. In this investigation employed an image segmentation algorithm to classify the ice plant (Mesembryanthemum crystallinum L.) canopy image with a threshold segmentation technique by grey colour model and calculating the degree of green colour in the HSV (hue, saturation, value) model. Notably, the segmentation technique is used to separate suitable surfaces from a defective noisy background. In this work, the canopy area was measured by pixel number statistics relevant to the known reference area. Furthermore, this paper proposed total leaf area estimation in a destructive method by a computer coordinating area curvimeter and lastly evaluated the overlapping percentage using the total leaf area and canopy area measurements. To assess the overlapping percentage using the proposed algorithm, the curvimeter method experiment was performed on 24 images of ice plants. The obtained results reveal that the overlapping percentage is less than 10%, as evidenced by a difference in the curvimeter and the proposed algorithm’s results with the canopy leaf area approach. Furthermore, the results show a strong correlation between the canopy and total leaf area (R2: 0.99) calculated by our proposed method. This overlapping leaf area finding offers a significant contribution to crop evolution by using computational techniques to make monitoring easier.

Innovative Leaf Area Detection Models for Orchard Tree Thick Canopy Based on LiDAR Point Cloud Data

Orchard spraying can effectively control pests and diseases. Over-spraying commonly results in excessive pesticide residues on agricultural products and environmental pollution. To avoid these problems, variable spraying technology uses target canopy detection to evaluate the leaf area in a canopy and adjust the application rate accordingly. In this study, a mobile LiDAR detection platform was set up to automatically measure point cloud data for a thick canopy in an apple orchard. A test platform was built, and manual measurements of the canopy leaf area were taken. Then, polynomial regression, back propagation (BP) neural network regression, and partial least squares regression (PLSR) algorithms were used to study the relationship between the orchard tree canopy point clouds and leaf areas. The BP neural network algorithm (86.1% and 73.6% accuracies for the test and verification data, respectively) and the PLSR algorithm (78.46% and 60.3%, respectively) performed better than the Fourier function of the polynomial regression (59.73% accuracy). The leaf area model obtained using PLSR was intuitive and simple, while the BP neural network algorithm was more accurate and could meet the requirements for high-precision variable spraying.

Effect of Rye cover crop on weed control, soybean (Glycine max L.) yield and profitability

Considerable variations in farm productivity were reported across soils and climates when winter cover crops (CC) were rotated with summer main cash crops. Hence, a three-year field study (2019-2021) was conducted on Dundee silt loam in a humid climate to assess soybean growth and yield, weed control, and profitability under no-till conditions in response to (i) no CC (NC), (ii) winter rye CC rolled when green, followed by soybean planting and desiccation by paraquat (GR) and iii) winter rye CC desiccated using paraquat and rolled followed by soybean planting (BR). No differences in phenological growth stages of soybean were observed among the treatments. Measured leaf area index was comparable among the treatments across the three seasons. The rate of rye CC biomass decay estimated eight weeks after planting (WAP) was much higher than at four WAP. In 2019, at eight WAP plant residue ranged from 29.3% under NC to 52.9% under GR, indicating the paraquat desiccated natural winter vegetation decays faster than the desiccated rye CC biomass. The weed biomass was 72% higher at eight WAP (0.29 Mg ha-1) than that of four WAP (0.17 Mg ha-1) and NC plots had higher weed biomass at both four WAP and eight WAP over CC plots. Field established soybean stand in the GR plots were consistently higher than the NC plots by 8%, 30%, and 22% in 2019, 2020, and 2021, respectively. Soybean yield in NC plot was 13% higher than GR and 15% higher than BR plots in 2019. However, in 2020 and 2021, soybean yield from BR and GR plots was significantly higher than NC plots (10% and 13%, respectively). In the three-year study, net returns from soybean with rye CC (regardless of GR or BR) in the first year was negative. In the second and third year, net returns in GR and BR were positive and comparable to NC. There were no differences in soybean yield and net returns between rye CC rolled green (GR) and rye CC desiccated (BR) prior to planting. These results show that a rye CC–based soybean conservation production system could be an economically a viable choice after the first year with an invaluable potential for carbon sequestration, weed suppression and positive impact on summer soybean productivity.

Relative leaf expansion rate as an indicator of compensatory growth of defoliated vines of Prokupac (Vitis vinifera L.)

Reaction of the grapevine to the early defoliation is to mitigate its effects through compensatory growth by producing more lateral shoots with a greater number of leaves. In this study, we evaluated the usage of non-destructive and continuous measurements of mean and lateral leaf area on the same shoots for the purpose of monitoring the leaf area development and calculating relative leaf expansion rate - RLER during vegetation. This study has shown that the grapevine's ability to recover its leaf area after defoliation depends mainly on the time when the defoliation occurs. Early defoliated vines had time to compensate removed leaves by producing more lateral shoots with a greater number of leaves, which resulted in a larger leaf area. With the decrease in the intensity of shoot growth during vegetation, the recovery ability decreases, therefore, compensatory growth is not enough to restore the reduced leaf area. Based on the value of RLER, its defoliation is performed in the period of intensive growth of shoots, it affects the stagnation of the emergence of new shoots and leaves over several days, followed by a period of re-growth. Very slow or no growth of shoots and leaves occurred after the veraison stage.

Non-destructively Determining Blueberry (Vaccinium corymbosum L.) Leaf Area Using DPI-Based Software

Blueberries (Vaccinium spp.) are a popular crop all throughout the world. Blueberry (Vaccinium corymbosum L.) leaves were randomly selected from the experimental area of Ondokuz Mayis University, Faculty of Agriculture as a research material. Blueberries are high in polyphenolic chemicals, particularly anthocyanins, which are antioxidants and anti-inflammatory. A total of 1500 leaves were collected, with 100 for each cultivar, to represent the variety of sizes found on 15 plants of each cultivar. Manual leaf area measurement was made with a digital planimeter. The area was measured using a 300dpi resolution image read from the relevant file in such a way that every one of them is defined by 8bits in RGB color space. A weighted sum of the RGB components of the image is used to convert RGB values to grayscale values. This three-dimensional gray image generates a binary image. The Otsu method was used to determine the threshold value required to minimize the in-class variation of the threshold black and white pixels. Simulink was used for easy use of the end-user with the developed software. This software can be used for the area measurement of all plant leaves.

Variety-specific response of bulk stomatal conductance of grapevine canopies to changes in net radiation, atmospheric demand, and drought stress.

In wine growing regions around the world, climate change has the potential to affect vine transpiration and overall vineyard water use due to related changes in daily atmospheric conditions and soil water deficits. Grapevines control their transpiration in response to such changes by regulating conductance of water through the soil-plant-atmosphere continuum. The response of bulk stomatal conductance, the vine canopy equivalent of stomatal conductance, to such changes were studied on Cabernet-Sauvignon, Merlot, Tempranillo, Ugni blanc, and Semillon vines in a non-irrigated vineyard in Bordeaux France. Whole-vine sap flow, temperature and humidity in the vine canopy, and net radiation absorbed by the vine canopy were measured on 15-minute intervals from early July through mid-September 2020, together with periodic measurements of leaf area, canopy porosity, and predawn leaf water potential. From these data, bulk stomatal conductance was calculated on 15-minute intervals, and multiple linear regression analysis was performed to identify key variables and their relative effect on conductance. For the regression analysis, attention was focused on addressing non-linearity and collinearity in the explanatory variables and developing a model that was readily interpretable.Variability of vapour pressure deficit in the vine canopy over the day and predawn water potential over the season explained much of the variability in bulk stomatal conductance overall, with relative differences between varieties appearing to be driven in large part by differences in conductance response to predawn water potential between the varieties. Transpiration simulations based on the regression equations found similar differences between varieties in terms of daily and seasonal transpiration. These simulations also compared well with those from an accepted vineyard water balance model, although there appeared to be differences between the two approaches in the rate at which conductance, and hence transpiration is reduced as a function of decreasing soil water content (i.e., increasing water deficit stress). By better characterizing the response of bulk stomatal conductance, the dynamics of vine transpiration can be better parameterized in vineyard water use modeling of current and future climate scenarios.

Remarkable Resilience of Forest Structure and Biodiversity Following Fire in the Peri-Urban Bushland of Sydney, Australia

In rapidly urbanizing areas, natural vegetation becomes fragmented, making conservation planning challenging, particularly as climate change accelerates fire risk. We studied urban forest fragments in two threatened eucalypt-dominated (scribbly gum woodland, SGW, and ironbark forest, IF) communities across ~2000 ha near Sydney, Australia, to evaluate effects of fire frequency (0–4 in last 25 years) and time since fire (0.5 to &gt;25 years) on canopy structure, habitat quality and biodiversity (e.g., species richness). Airborne lidar was used to assess canopy height and density, and ground-based surveys of 148 (400 m2) plots measured leaf area index (LAI), plant species composition and habitat metrics such as litter cover and hollow-bearing trees. LAI, canopy density, litter, and microbiotic soil crust increased with time since fire in both communities, while tree and mistletoe cover increased in IF. Unexpectedly, plant species richness increased with fire frequency, owing to increased shrub richness which offset decreased tree richness in both communities. These findings indicate biodiversity and canopy structure are generally resilient to a range of times since fire and fire frequencies across this study area. Nevertheless, reduced arboreal habitat quality and subtle shifts in community composition of resprouters and obligate seeders signal early concern for a scenario of increasing fire frequency under climate change. Ongoing assessment of fire responses is needed to ensure that biodiversity, canopy structure and ecosystem function are maintained in the remaining fragments of urban forests under future climate change which will likely drive hotter and more frequent fires.

Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat

BackgroundThe superposition of COVID-19 and climate change has brought great challenges to global food security. As a major economic crop in the world, studying its phenotype to cultivate high-quality wheat varieties is an important way to increase grain yield. However, most of the existing phenotyping platforms have the disadvantages of high construction and maintenance costs, immobile and limited in use by climatic factors, while the traditional climate chambers lack phenotypic data acquisition, which makes crop phenotyping research and development difficult. Crop breeding progress is slow. At present, there is an urgent need to develop a low-cost, easy-to-promote, climate- and site-independent facility that combines the functions of crop cultivation and phenotype acquisition. We propose a movable cabin-type intelligent artificial climate chamber, and build an environmental control system, a crop phenotype monitoring system, and a crop phenotype acquisition system.ResultWe selected two wheat varieties with different early vigor to carry out the cultivation experiments and phenotype acquisition of wheat under different nitrogen fertilizer application rates in an intelligent artificial climate chamber. With the help of the crop phenotype acquisition system, images of wheat at the trefoil stage, pre-tillering stage, late tillering stage and jointing stage were collected, and then the phenotypic information including wheat leaf area, plant height, and canopy temperature were extracted by the crop type acquisition system. We compared systematic and manual measurements of crop phenotypes for wheat phenotypes. The results of the analysis showed that the systematic measurements of leaf area, plant height and canopy temperature of wheat in four growth periods were highly correlated with the artificial measurements. The correlation coefficient (r) is positive, and the determination coefficient (R2) is greater than 0.7156. The root mean square error (RSME) is less than 2.42. Among them, the crop phenotype-based collection system has the smallest measurement error for the phenotypic characteristics of wheat trefoil stage. The canopy temperature RSME is only 0.261. The systematic measurement values of wheat phenotypic characteristics were significantly positively correlated with the artificial measurement values, the fitting degree was good, and the errors were all within the acceptable range. The experiment showed that the phenotypic data obtained with the intelligent artificial climate chamber has high accuracy. We verified the feasibility of wheat cultivation and phenotype acquisition based on intelligent artificial climate chamber.ConclusionIt is feasible to study wheat cultivation and canopy phenotype with the help of intelligent artificial climate chamber. Based on a variety of environmental monitoring sensors and environmental regulation equipment, the growth environment factors of crops can be adjusted. Based on high-precision mechanical transmission and multi-dimensional imaging sensors, crop images can be collected to extract crop phenotype information. Its use is not limited by environmental and climatic factors. Therefore, the intelligent artificial climate chamber is expected to be a powerful tool for breeders to develop excellent germplasm varieties.

Piperonylic acid alters growth, mineral content accumulation and reactive oxygen species-scavenging capacity in chia seedlings.

p-Coumaric acid synthesis in plants involves the conversion of phenylalanine to trans-cinnamic acid via phenylalanine ammonia-lyase (PAL), which is then hydroxylated at the para-position under the action of trans-cinnamic acid 4-hydroxylase. Alternatively, some PAL enzymes accept tyrosine as an alternative substrate and convert tyrosine directly to p-coumaric acid without the intermediary of trans-cinnamic acid. In recent years, the contrasting roles of p-coumaric acid in regulating the growth and development of plants have been well-documented. To understand the contribution of trans-cinnamic acid 4-hydroxylase activity in p-coumaric acid-mediated plant growth, mineral content accumulation and the regulation of reactive oxygen species (ROS), we investigated the effect of piperonylic acid (a trans-cinnamic acid 4-hydroxylase inhibitor) on plant growth, essential macroelements, osmolyte content, ROS-induced oxidative damage, antioxidant enzyme activities and phytohormone levels in chia seedlings. Piperonylic acid restricted chia seedling growth by reducing shoot length, fresh weight, leaf area measurements and p-coumaric acid content. Apart from sodium, piperonylic acid significantly reduced the accumulation of other essential macroelements (such as K, P, Ca and Mg) relative to the untreated control. Enhanced proline, superoxide, hydrogen peroxide and malondialdehyde contents were observed. The inhibition of trans-cinnamic acid 4-hydroxylase activity significantly increased the enzymatic activities of ROS-scavenging enzymes such as superoxide dismutase, ascorbate peroxidase, catalase and guaiacol peroxidase. In addition, piperonylic acid caused a reduction in indole-3-acetic acid and salicylic acid content. In conclusion, the reduction in chia seedling growth in response to piperonylic acid may be attributed to a reduction in p-coumaric acid content coupled with elevated ROS-induced oxidative damage, and restricted mineral and phytohormone (indole-3-acetic acid and salicylic) levels.

Area estimation of soybean leaves of different shapes with artificial neural networks

Leaf area is one of the most commonly used physiological parameters in plant growth analysis because it facilitates the interpretation of factors associated with yield. The different leaf formats related to soybean genotypes can influence the quality of the model fit for the estimation of leaf area. Direct leaf area measurement is difficult and inaccurate, requires expensive equipment, and is labor intensive. This study developed methodologies to estimate soybean leaf area using neural networks and considering different leaf shapes. A field experiment was carried out from February to July 2017. Data were collected from thirty-six cultivars separated into three groups according to the leaf shape. Multilayer perceptrons were developed using 300 leaves per group, of which 70% were used for training and 30% for validation. The most important morphological measures were also tested with Garson’s method. The artificial neural networks were efficient in estimating the soybean leaf area, with coefficients of determination close to 0.90. The left leaflet width and right leaflet length are sufficient to estimate the leaf area. Network 4, trained with leaves from all groups, was the most general and suitable for the prediction of soybean leaf area.

Using an IR camera to improve leaf area and temperature measurements: A new method for increasing the accuracy of photosynthesis-related parameters

Accurate measurements of leaf temperature and area are crucial to calculate leaf gas exchange parameters when using portable gas analysers such as the LI-6400 XT portable photosynthesis system (LI‐COR Inc., Lincoln, USA). The importance of leaf temperature data is well known for robust measurements, but there are still technical limitations in measuring it with full accuracy. Here we present a tentative solution by incorporating leaf temperature measurements with thermal imaging. An infrared (IR) camera (IRay T3 Pro, Shandong, China) was combined with a LI-6400XT to measure mean leaf temperatures while avoiding leaf thermal heterogeneity and to accurately calculate leaf area while avoiding destructive sampling. The thermal heterogeneity of Helianthus annuus and Zea mays leaves were studied. We then evaluated the accuracies of leaf area and temperature measurements obtained for 9 species and discussed the influences of these measurements on the resulting photosynthesis-related parameters. Our results provide direct visual evidence of the thermal heterogeneity of leaves in an infrared gas analyzer (IRGA) chamber. The temperature differences across the leaf surface were as high as 0.28°C∼1.38°C, causing large errors in stomatal conductance (gs, -5.07%∼-55.02%) and intercellular CO2 concentrations (Ci, -0.41%∼-6.72%). The IR camera reduced the area measurement error (-0.71∼0.29 cm2) and greatly increased the accuracy by -11.12%∼17.60% for the net photosynthesis (Pn) and transpiration rate (E) and by -11.24%∼17.69% for gs calculations. Our results showed that combining an IR camera with LI-6400XT can improve leaf area measurements, avoid thermal heterogeneity in leaves and thus dramatically improve the accuracy of photosynthesis-related parameter calculations under semi controlled and moderate climate conditions.

Accelerating leaf area measurement using a volumetric approach

BackgroundDespite the advances in the techniques of indirect estimation of leaf area, the destructive measurement approaches have still remained as the reference and the most accurate methods. However, even utilizing the modern sensors and applications usually requires the laborious and time-consuming practice of unfolding and analyzing the single leaves, separately. In the present study, a volumetric approach was tested to determine the pile leaf area based on the ratio of leaf volume divided by thickness. For this purpose, the suspension technique was used for volumetry, which is based on the simple practice and calculations of the Archimedes’ principle.ResultsWheat volumetric leaf area (VLA), had a high agreement and approximately 1:1 correlation with the conventionally measured optical leaf area (OLA). Exclusion of the midrib volume from calculations, did not affect the estimation error (NRMSE < 2.61%); however, improved the slope of the linear model by about 6%, and also reduced the bias between the methods. The error of sampling for determining mean leaf thickness of the pile, was also less than 2% throughout the season. Besides, a more practical and facilitated version of pile volumetry was tested using Specific Gravity Bench (SGB), which is currently available as a laboratory equipment. As an important observation, which was also expectable according to the leaf 3D expansion (i.e., in a given 2D plane), it was evidenced that the variations in the OLA exactly follows the pattern of the changes in the leaf volume. Accordingly, it was suggested that the relative leaf areas of various experimental treatments might be compared directly based on volume, and independently of leaf thickness. Furthermore, no considerable difference was observed among the OLAs measured using various image resolutions (NRMSE < 0.212%); which indicates that even the superfast scanners with low resolutions as 200 dpi may be used for a precision optical measurement of leaf area.ConclusionsIt is expected that utilizing the reliable and simple concept of volumetric leaf area, based on which the measurement time might be independent of sample size, facilitate the laborious practice of leaf area measurement; and consequently, improve the precision of field experiments.