Estimating Leaf Area Research Articles

Leaf Area Estimation of Yellow Oleander Thevetia peruviana (Pers.) K. Schum Using a Non-Destructive Allometric Model

The resurgence of interest in medicinal plants and their potential in pharmaceuticals has driven research into harnessing bioactive compounds for innovative treatments. This study proposes an accurate and non-destructive method to estimate leaf area (LA) for Thevetia peruviana through linear measurements of the leaf length (L), the leaf width (W), or the product of the leaf length and width (LW). The study encompasses comprehensive analyses of leaf dimensions collected during different seasons (rainy and dry season), employing linear and non-linear regression models to predict LA. Among the diverse models tested, non-linear equations emerged as superior predictors of LA, surpassing simpler linear models. However, in the rigorous selection process, the equations were linear with the intercept and power model, meeting the requirements for accurate and unbiased LA estimation. Despite the competence of these models, distinguishing between them based on evaluation criteria proved inconclusive. Following the principle of simplicity, equations linear with the intercept [LA = 0.284 + 0.766 × (LW)] are preferred as power models [LA = 0.914 × (LW)0.939] and are recommended as an optimal and practical choice for estimating T. peruviana LA in field experiments. The investigation emphasizes the importance of a robust approach to LA estimation, offering crucial insights into the allometric relationships and facilitating informed agricultural decisions. This comprehensive study advances our understanding of T. peruviana and contributes to the broader discourse on accurate and efficient leaf area estimation techniques in plant biology and agriculture.

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.

Método não destrutivo para estimar a área foliar do crisântemo

ABSTRACT Chrysanthemum (Dendranthema grandiflora) is the second most produced and commercialized ornamental plant in the world. Measuring leaf area through non-destructive methods is fundamental for studies on its growth and production. The estimation of leaf area by linear dimensions of the leaves can be a strategy for this purpose. The objective of this study was to find allometric equations to estimate the leaf area of chrysanthemum. The linear, linear without intercept, quadratic, cubic, power, and exponential regression models were used for the analysis. The choice of equations was based on the highest coefficients of determination. The non-destructive method using allometric models has accuracy for estimating the leaf area (LA) of chrysanthemum from the product between leaf length (L) and leaf width (W). The LA of chrysanthemum can be estimated using the equation ŷ = 0.6611*LW0.9490 (L - leaf length; W - leaf width). This equation will allow researchers and producers to determine leaf area non-destructively.

Leaf area index (LAI) and gap fraction. A discussion

Methodological aspects of estimating leaf area from gap fraction measurements are discussed. Instead of the common practice of linking in the Beer-Lambert law leaf area index and clumping factor together, the clumping factor and Ross-Nilson geometry function as two structure parameters should be combined into the effective geometry function, which considers both the leaf angle distribution and clumping/regularity of foliage in the expression of the gap fraction of a vegetation layer. Key words: leaf area index; foliage clumping; gap fraction; LAI-2000; G-function

Use of Satellite-based Leaf Area Index Data for Monitoring Green Gram Crop Growth in Ikombe-Katangi Area, Machakos County, Kenya

Monitoring the conditions under which crops grow and estimating their yield is essential to the process of economic development in any nation. The traditional approaches to crop monitoring are labor- and resource-intensive, as well as limited in their ability to cover expansive geographic regions. Since remote sensing data is integrated with ground measurements, it has been used as a potentially useful tool to extract biophysical variables such as leaf area index (LAI), biomass, and phenology. Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes such as yield. The potential of satellite-based LAI for monitoring crop growth was investigated and compared to field measurements. This was the objective of his study. High resolution leaf area index was retrieved from Landsat 8 OLI imageries, and ground leaf area index measurements were taken in Ikombe-Katangi Machakos in Machakos county. An equation based on regression was developed to estimate the leaf area index using the normalized difference vegetation index that was derived from Landsat-8 OLI (NDVI). According to the findings, the derived LAI exhibited strong linear relationships with the leaf area index that was measured on the ground for green gram crops, with RMSE values being 0.09846, and R2 values of 0.9249. The overall findings shed light on the viability of using multispectral data, in estimating leaf area index in a very fragmented agricultural landscape, such as that found in the Ikombe and Katangi areas of Machakos, Kenya. Therefore, accurate crop monitoring on a large scale can be accomplished through remote sensing data in conjunction with LAI measurements taken from the ground. The implementation of the leaf area index is subject to the environmental condition which requires to be investigated as the use of remote sensing data is advocated for. The advancement in the use of satellite data for extraction LAI data is key towards transforming crop production within Kenya ad Africa at large. Scarce resources for crop growth monitoring can now be augmented with open-source satellite datasets such as Landsat 8 in LAI estimation.

Leaf area estimation in lettuce: Comparison of artificial intelligence-based methods with image analysis technique

Artificial intelligence (AI) as a simulation approach is increasingly being used in precision agriculture. The current study evaluated the performance of five methods for estimating lettuce leaf area (LA), including simple linear regression (SLR), artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), support vector regression (SVR), and image analysis technique. The standardized Ranking Performance Index (sRPI) was used to determine the overall ranking of the simulation approaches. Image analysis and ANFIS methods were found to have superior performance to other methods evaluated, according to the evaluation criteria of R2 values of 0.98 and 0.92, and RRMSE values of 4.3 and 2.32, respectively. The SLR, on the other hand, was found to be the least ranked method. Overall, the results indicated that the image analysis method could be a cost-effective option, and the ANFIS method could serve as a non-destructive alternative to conventional LA determination methods.

An Approach for Plant Leaf Image Segmentation Based on YOLOV8 and the Improved DEEPLABV3.

Accurate plant leaf image segmentation provides an effective basis for automatic leaf area estimation, species identification, and plant disease and pest monitoring. In this paper, based on our previous publicly available leaf dataset, an approach that fuses YOLOv8 and improved DeepLabv3+ is proposed for precise image segmentation of individual leaves. First, the leaf object detection algorithm-based YOLOv8 was introduced to reduce the interference of backgrounds on the second stage leaf segmentation task. Then, an improved DeepLabv3+ leaf segmentation method was proposed to more efficiently capture bar leaves and slender petioles. Densely connected atrous spatial pyramid pooling (DenseASPP) was used to replace the ASPP module, and the strip pooling (SP) strategy was simultaneously inserted, which enabled the backbone network to effectively capture long distance dependencies. The experimental results show that our proposed method, which combines YOLOv8 and the improved DeepLabv3+, achieves a 90.8% mean intersection over the union (mIoU) value for leaf segmentation on our public leaf dataset. When compared with the fully convolutional neural network (FCN), lite-reduced atrous spatial pyramid pooling (LR-ASPP), pyramid scene parsing network (PSPnet), U-Net, DeepLabv3, and DeepLabv3+, the proposed method improves the mIoU of leaves by 8.2, 8.4, 3.7, 4.6, 4.4, and 2.5 percentage points, respectively. Experimental results show that the performance of our method is significantly improved compared with the classical segmentation methods. The proposed method can thus effectively support the development of smart agroforestry.

Implementation of a dynamic specific leaf area (SLA) into a land surface model (LSM) incorporated crop-growth model

Croplands exert a strong influence on the land–atmosphere coupling and the surface exchanges of heat, water vapor, and momentum. The current trend is incorporating crop models into process-based land surface models (LSMs) to capture photosynthesis, crop growth process, and yield process. However, the models still have shortcomings, such as the assumption of constant SLA tends to ignore specific leaf area (SLA) changes caused by the accumulation and transport of photosynthetic products between organs. In this study, a dynamic SLA, which is specified for each crop, was implemented in the Noah-Multiparameterization Land Surface Model (Noah-MP-Crop), a coupled-crop-land-surface model, with the aim of improving the accuracy of estimated leaf area index (LAI) and above-ground biomass (leaf, stem and grain yield) of crops (maize, rice, soybean and winter wheat) in China. By including the dynamic SLA, the new model (Noah-MP-Cropsla) is more than qualified for capturing the measured variability in LAI and crops’ above-ground biomass, with the mean dim (Willmott’s index of agreement for the dynamic SLA scheme) of the four crops were all above 0.7. A remarkable improvement in the simulations of crop LAI and above-ground biomass were found during the crop’s jointing stage as indicated by the mean relative change rate of RMSE (RMSE% > 50%). Excluding the dynamic process of SLA will result in higher biases in the grain yield for most of the study sites. the improvement degree of crop yield was related to the improvement degree of LAI at the jointing stage (heading stage) of maize, rice and soybean (winter wheat). With the improved SLA process, the Noah-MP-Cropsla have a solid ability to capture the spatial pattern of crop yields, the R2 of the simulated and census data of the four major crop yield in China were all above 0.5.

An improved volume power approach to estimate LAI from optimized dual-polarized SAR decomposition

ABSTRACT Synthetic Aperture Radar (SAR) imagery has proven to be a valuable tool for monitoring the Earth’s surface, particularly vegetation. However, many studies using SAR have focused solely on backscattering sensitivity without considering the structural and physical properties of the vegetation being analysed. This study proposes a new approach for vegetation monitoring using the Volume Power (VP) analysis technique. The proposed method aims to improve the accuracy of VP derived from the Freeman-Durden (FD) decomposition technique for dual polarimetric SAR data. To increase the sensitivity of vegetation scattering in SAR analysis, this study modified the VP using Depolarized Volume Power (DVP) and Anisotropic Volume Power (AVP). The first modification, DVP, was achieved by incorporating the degree of polarization () in the analysis. The second modification, AVP, is achieved by considering the anisotropic scattering properties of vegetation. The modified VP is then used to estimate Leaf Area Index (LAI) using an empirical relationship between LAI and the modified VPs. The accuracy of the LAI estimation is evaluated using ground truth measurements. The results demonstrate that the proposed method provides more accurate LAI estimates than traditional methods. The approach also shows improved sensitivity to vegetation scattering compared to the original VP from the FD, indicating the effectiveness of the degree of polarization and anisotropic scattering in reducing the impact of unwanted scattering mechanisms from the surface. The accuracy (R 2) between in-situ LAI and the LAI retrieved from AVP and DVP was 0.85 and 0.82, whereas for FDVP, it was 0.74. These findings highlight the potential of the proposed approach for improving the accuracy of VP estimation in dual-polarimetric SAR data and enabling more accurate and efficient estimation of LAI.

Agronomic Characteristics of Maize Grown in Paddy Soil, Along with Root Zone Soil Chemical Properties and Soil Bacterial Numbers

Maize is being considered as an alternative crop for off-season rice production in Thailand due to increasing water scarcity. The objective of this study was to evaluate the agronomic characteristics and root-zone soil properties of 4 hybrid maize varieties grown in paddy soil during off-season rice cultivation, in order to assess their growth and development after the flooded-rice harvest and the associated soil microorganisms. The randomized complete block design with 4 treatments and 4 replications was used. The 4 treatments were 2 new maize varieties (Suwan 5731 and Suwan 5819), 1 check variety (Suwan 4452), and 1 local use variety (PAC 789). Suwan 5819 showed significantly higher plant height than Suwan 4452, Suwan 5731 and PAC 789, with the highest plant height recorded for Suwan 5819 (221 cm). Similarly, Suwan 5819 exhibited significantly higher estimated leaf area index than Suwan 4452, Suwan 5731 and PAC 789, with differences of 32.67, 42.57 and 29.29 % at VT, respectively. These superior performances of Suwan 5819 could be attributed in part to increased dry matter and nutrient accumulation at the VT stage. The grain yield for PAC 789 was 13.71 t ha–1, Suwan 4452 was 13.23 t ha–1, Suwan 5731 was 12.83 t ha–1, and Suwan 5819 was 12.25 t ha–1. However, there was no significant difference in the grain yields among the 4 varieties. The soil in the root zone of Suwan 5819 at VT was significantly less acidic (pH 5.08) than Suwan 4452 (pH 4.58), PAC 789 (pH 4.61) and Suwan 5731 (pH 4.75). The nitrogen-fixing bacteria were most abundant in PAC 789 (84 %), phosphate-solubilizing bacteria in Suwan 4452 (15 %), and potassium-solubilizing bacteria in Suwan 5819 (16 %). Overall, the study suggests that Suwan 5819 is a promising maize variety for agronomic performance in the tested region. HIGHLIGHTS Maize is a viable alternative to rice for off-season cultivation in water-scarce regions. One new maize hybrid (Suwan 5819) showed better growth and nutrient accumulation during the vegetative transition stage. Soil properties, including beneficial microorganisms, did not differ significantly among the four maize varieties studied. Suwan 5819 new maize hybrid had less acidic root zone soil conditions than the other tested varieties. Commercial maize hybrid had the highest counts of N-fixing and K-solubilizing bacteria in the root zone soil, but there was no significant difference in bacterial count compared to the Suwan maize GRAPHICAL ABSTRACT

Non-destructive mathematical models to estimate leaf area in noni (Morinda citrifolia)

Non-destructive mathematical models to estimate leaf area in noni (Morinda citrifolia)

Comparison of Machine Learning Methods for Estimating Leaf Area Index and Aboveground Biomass of Cinnamomum camphora Based on UAV Multispectral Remote Sensing Data

UAV multispectral technology is used to obtain leaf area index (LAI) and aboveground biomass (AGB) information on Cinnamomum camphora (C. camphora) and to diagnose the growth condition of Cinnamomum camphora dwarf forests in a timely and rapid manner, which helps improve the precision management of Cinnamomum camphora dwarf forests. Multispectral remote sensing images provide large-area plant spectral information, which can provide a detailed quantitative assessment of LAI, AGB and other plant physicochemical parameters. They are very effective tools for assessing and analyzing plant health. In this study, the Cinnamomum camphora dwarf forest in the red soil area of south China is taken as the research object. Remote sensing images of Cinnamomum camphora dwarf forest canopy are obtained by the multispectral camera of an unmanned aerial vehicle (UAV). Extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), random forest (RF), radial basis function neural network (RBFNN) and support vector regression (SVR) algorithms are used to study the correlation and estimation accuracy between the original band reflectance, spectral indices and LAI and AGB of Cinnamomum camphora. The results of this study showed the following: (1) The accuracy of model estimation based on RF is significantly different for different model inputs, while the other four models have small differences. (2) The accuracy of the XGBoost-based LAI model was the highest; with original band reflectance as the model input, the R2 of the model test set was 0.862, and the RMSE was 0.390. (3) The accuracy of the XGBoost-based AGB model was the highest; with spectral indices as the model input, the R2 of the model test set was 0.929, and the RMSE was 587.746 kg·hm−2. (4) The XGBoost model was the best model for the LAI and AGB estimation of Cinnamomum camphora, which was followed by GBDT, RF, RFNN, and SVR. This research result can provide a theoretical basis for monitoring a Cinnamomum camphora dwarf forest based on UAV multispectral technology and a reference for rapidly estimating Cinnamomum camphora growth parameters.

Scale variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using model–data fusion

Abstract. Many terrestrial landscapes are heterogeneous. Mixed land cover and land use generate a complex mosaic of fragmented ecosystems at fine spatial resolutions with contrasting ecosystem stocks, traits, and processes, each differently sensitive to environmental and human factors. Representing spatial complexity within terrestrial ecosystem models is a key challenge for understanding regional carbon dynamics, their sensitivity to environmental gradients, and their resilience in the face of climate change. Heterogeneity underpins this challenge due to the trade-off between the fidelity of ecosystem representation within modelling frameworks and the computational capacity required for fine-scale model calibration and simulation. We directly address this challenge by quantifying the sensitivity of simulated carbon fluxes in a mixed-use landscape in the UK to the spatial resolution of the model analysis. We test two different approaches for combining Earth observation (EO) data into the CARDAMOM model–data fusion (MDF) framework, assimilating time series of satellite-based EO-derived estimates of ecosystem leaf area and biomass stocks to constrain estimates of model parameters and their uncertainty for an intermediate complexity model of the terrestrial C cycle. In the first approach, ecosystems are calibrated and simulated at pixel level, representing a “community average” of the encompassed land cover and management. This represents our baseline approach. In the second, we stratify each pixel based on land cover (e.g. coniferous forest, arable/pasture) and calibrate the model independently using EO data specific to each stratum. We test the scale dependence of these approaches for grid resolutions spanning 1 to 0.05∘ over a mixed-land-use region of the UK. Our analyses indicate that spatial resolution matters for MDF. Under the community average baseline approach biological C fluxes (gross primary productivity, Reco) simulated by CARDAMOM are relatively insensitive to resolution. However, disturbance fluxes exhibit scale variance that increases with greater landscape fragmentation and for coarser model domains. In contrast, stratification of assimilated data based on fine-resolution land use distributions resolved the resolution dependence, leading to disturbance fluxes that were 40 %–100 % higher than the baseline experiments. The differences in the simulated disturbance fluxes result in estimates of the terrestrial carbon balance in the stratified experiment that suggest a weaker C sink compared to the baseline experiment. We also find that stratifying the model domain based on land use leads to differences in the retrieved parameters that reflect variations in ecosystem function between neighbouring areas of contrasting land use. The emergent differences in model parameters between land use strata give rise to divergent responses to future climate change. Accounting for fine-scale structure in heterogeneous landscapes (e.g. stratification) is therefore vital for ensuring the ecological fidelity of large-scale MDF frameworks. The need for stratification arises because land use places strong controls on the spatial distribution of carbon stocks and plant functional traits and on the ecological processes controlling the fluxes of C through landscapes, particularly those related to management and disturbance. Given the importance of disturbance to global terrestrial C fluxes, together with the widespread increase in fragmentation of forest landscapes, these results carry broader significance for the application of MDF frameworks to constrain the terrestrial C balance at regional and national scales.

A method to estimate leaf area index from VIIRS surface reflectance using deep transfer learning

The leaf area index (LAI) retrieval methods based on traditional neural networks require a large number of training samples constructed from remote sensing data or simulation data using radiative transfer models. Furthermore, the training samples for the neural networks are sensor-specific. Therefore, the existing training samples for a sensor cannot be directly applied to estimate LAI values from remote sensing data acquired by another sensor. In addition, a large number of currently available LAI ground measurements (considered as “true values”) are not used to construct training datasets of the neural networks to further improve the accuracy of the retrieved LAI values. In this study, a method based on deep transfer learning is proposed to retrieve LAI values from the Visual Infrared Imaging Radiometer Suite (VIIRS) surface reflectance data based on the existing training dataset established from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and the fused LAI values of the MODIS and Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) LAI products. A transferable model is constructed by using a deep belief network (DBN) composed of a restricted Boltzmann machine (RBM) and a back propagation (BP) network. The DBN is pretrained by the existing training dataset, and the hyperparameters of the DBN are determined by using a Bayesian optimization algorithm. Then, the RBM parameters of the pretrained DBN are frozen, and the BP parameters are fine-tuned by a small sample dataset composed of the LAI ground measurements and the corresponding VIIRS surface reflectance data. Finally, the VIIRS surface reflectance data are inputted into the fine-tuned DBN to estimate LAI values. The retrieved LAI values were cross-compared with the MODIS and VIIRS LAI products and were directly evaluated with LAI ground measurements at the Ground-Based Observations for Validation (GBOV) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) sites. The results demonstrate that the retrieved LAI values at these sites with different vegetation types show reasonable seasonality and the spatial distributions of the retrieved LAI values are similar to those of the MODIS and VIIRS LAI products. The direct validation results show that the LAI values retrieved by the DBN with fine-tuned BP parameters (RMSE over the USA: 0.48, RMSE over Europe: 0.71) are obviously superior to the LAI values retrieved by the DBN without fine-tuned BP parameters (RMSE over the USA: 0.70, RMSE over Europe: 0.98). This study demonstrates that deep transfer learning can effectively retrieve LAI values from the VIIRS surface reflectance data with limited LAI ground measurement samples and the existing training dataset.

Non-destructive Leaf Area Measurement Using Mathematical Modeling for Paddy Varieties

Leaf area is considered an important parameter in fields such as plant phenotyping and plant breeding. In this study, leaf areas of different rice varieties were measured using a leaf area meter. Subsequently, a mathematical model was developed using leaf dimensions to estimate leaf area. Multiple regression analysis was used in the study to examine how leaf area is related to leaf dimensions. The results showed significant differences in leaf areas among different paddy varieties (Efe, Osmancık-97, Hamzadere, and Paşalı). Additionally, leaf dimensions were found to be a strong predictor for estimating leaf area. The equation of leaf area (LA= a + (b x L) + (c x W) + (d x L²) + [e x (L x W)] for paddy varieties tested. The R² values for paddy varieties between 84% - 99%. The mathematical model is an important tool that can be used in plant phenotyping and plant breeding, and can be further utilized in future research in these fields.

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method

The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R2). Among the linear regression models, the equation yˆ=0.515+0.584*LW was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R2 in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R2: 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R2: 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame.

Image-based phenomic prediction can provide valuable decision support in wheat breeding

Key messageGenotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials.Traditionally, breeders’ selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders’ ratings with such information became attractive. In this research, it is hypothesized that GtimesE interactions of secondary traits (i.e., growth dynamics’ traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate GtimesE interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose–response curve parameters. Most of these secondary traits and grain protein content showed little GtimesE interactions. In contrast, the modeling of GtimesE for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.

Leaf Area Estimation by Photographing Leaves Sandwiched between Transparent Clear File Folder Sheets

Image analysis is a promising method for in situ leaf area measurement. However, as leaves are three-dimensional, the use of two-dimensional images captured using a digital camera can result in underestimation. To overcome this problem, we tested a clear folder method. Before photographing leaves with a digital camera, we flattened the leaves by sandwiching them between a pair of transparent plastic clear file folder sheets, which are stationery implements for carrying documents. Although similar methods have been previously proposed, their applicability to species with different leaf shapes has never been investigated. We tested the efficacy of this method using 12 species from various taxa (monocots, magnoliids, and basal and core eudicots) and leaf morphology (entire vs. lobed, simple vs. compound leaves, small and large leaves). Individual leaf areas and the Montgomery parameters obtained using this method were then compared with those obtained using the standard method, which employs a flatbed digital scanner. We observed strong correlations (R2 > 0.98) between the camera and scanner data. The regression slopes were close to unity (0.96–1.01) and the intercepts were close to zero. These findings suggest that the clear folder method can be used as an inexpensive alternative method to estimate the area of leaves in situ with acceptable accuracy. An introductory manual for readers unfamiliar with image analysis using ImageJ is presented in the end of the paper.

Estimating Three-Dimensional Distribution of Leaf Area Using Airborne LiDAR in Deciduous Broad-Leaved Forest

We examined the performance of airborne light detection and ranging (LiDAR) data obtained in 2011 for leaf area estimation in deciduous broad-leaved forest using the Beer–Lambert law in Takayama, Gifu, Japan. We estimated leaf area index (LAI, allometry-LAI) and vertical leaf area density (LAD) using field survey data by applying allometric equations to estimate leaf-area of trees and a Weibull distribution equation to estimate vertical leaf distribution. We then estimated extinction coefficients (Ke) of LiDAR data for three height layers from the ground to the canopy top using the vertical LAD and vertical laser pulse distribution. The estimated PAI (LiDAR-PAI) using the Beer–Lambert law and Ke, when treating the canopies as three height layers, showed a significant linear relationship with allometry-LAI (p < 0.001). However, LiDAR-PAI when treating the canopies as single layer saturated at a PAI of six. It was similar to the lesser PAI estimation by hemispherical photography or relative photosynthetic photon flux density which treated the canopy as a single layer, compared to LAI measurements by litter traps. It is therefore important to allocate distinct Ke values to each of the multiple height layers for an accurate estimation of PAI and vertical PAD when applying the Beer–Lambert law to airborne LiDAR data.

Non-Destructive Model for Estimating Leaf Area and Growth of Cnidoscolus aconitifolius Cultivated Using Different Stem Diameter of the Semi Hardwood Cuttings

Chaya (Cnidoscolus aconitifolius (Mill.) I.M. Johnst.) is a perennial leafy vegetable. Since the leaf blade is the most crucial component of the Chaya, it should be advantageous to estimate the leaf area non-destructively. This research aimed to (a) develop a beneficial non-destructive model for leaf area estimation, and (b) ascertain the impact of stem cutting diameter when used as planting material on chaya growth rate. The results demonstrated that utilizing the length x width (LW) of the middle lobe as the predictor and the zero intercepts linear regression LA = 1.679 LW with R2=0.947 determination coefficient was the most dependable model for Chaya plant leaf area estimation. The stem diameter, which ranged from 9.9 to 17.3 mm, had affected the shoot length, number of leaves, and canopy area between 5 and 7 weeks after planting (WAP). The variations in growth metrics diminished along with the age of plants. The diameter of the stem cutting used as planting material had no effect on the fresh weight of young edible shoot tip at 11 WAP. On Chaya plants that were cultivated with larger stem cutting diameters, the fresh leaf weight at the branch tips was heavier.