Non-destructive Leaf Area Research Articles

A Non-destructive Leaf Area Prediction Model and Some Physical Leaf Properties in Apples

A Non-destructive Leaf Area Prediction Model and Some Physical Leaf Properties in Apples

Association of Agronomic, Physiological Traits and Biomass of a Diverse Set of Sugarcane Genotypes under Varied Early Season Droughts

Background: Recently, multipurpose and biomass canes have become important in breeding. However, biomass sampling is limited in early generation selection. The surrogate traits that represent biomass performance need to be established, especially using non-destructive sampling. Therefore, the objective of this study was to determine the correlation between agronomic and physiological traits and the biomass of a diverse set of sugarcane genotypes under different drought durations. Methods: The experiment was conducted under field conditions and arranged in a split plot in a randomized complete block design with four replications. The main plot was represented by three drought durations: no water stress, short-term drought and long-term drought and the subplot consisted of six sugarcane genotypes. Samples were collected at 3, 6, 8 and 12 months after transplanting to determine biomass and nondestructive agronomic and physiological traits. A simple correlation was used to determine the relationship between biomass and physiological and agronomic traits. Result: In both field capacity and long drought conditions, 3 months after transplanting, there was a positive correlation between canopy height and biomass and between green leaf number and biomass. The non-destructive leaf area index was a trait that contributed to biomass at 6 months after transplanting under non-water stress conditions. At 8 months after transplanting, a positive relationship between canopy height and biomass was found under short drought and long drought conditions and green leaf number was also related to biomass under field capacity conditions. Biomass at the harvesting stage contributed to canopy width in sugarcane under field capacity conditions. Non-destructive traits in this experiment, such as canopy height and green leaf number, could be used as indirect measurements to reflect the biomass performance under field capacity and long drought conditions at the tillering and physiological maturity phases. For the elongation phase, the non-destructive leaf area index was an altered characteristic that indirectly determined biomass. This information will be useful as an alternative measurement to indicate biomass in the breeding program for drought resistance at the early growth stage.

Non-destructive Leaf Area Index estimation via guided optical imaging for large scale greenhouse environments

This paper presents a financially viable and non-destructive rail-based video monitoring method that utilizes optical image segmentation to estimate the canopy leaf area index (LAI) of greenhouse tomato plants. The LAI is directly related to the time-dependent crop growth and indicates plant health and potential crop yields. A rail-guided mobile camera system was commissioned that records continuous images by scanning multiple rows of two tomato plant species for over two years. UNET semantic image segmentation of the individual image frames was performed to compute the relative leaf area over time. This study also describes the image annotation process necessary to train the neural network and evaluate the segmentation results. The results are calibrated and compared to the defoliation-based (destructive) LAI estimation performed by the grower. This UNET segmentation performs well, which is enabled through the controlled environment and the well-defined boundary conditions provided by the greenhouse environment and the managed measurement conditions. Our results deviate from the manual LAI estimation by less than ten percent. Further, we are able to minimize confusion between foreground and background plants and other obstructions with an estimated error smaller than three percent, which is strictly necessary to produce reproducible results.

Development of a Simple Empirical Yield Predition Model Based on Dry Matter Production in Sweet Pepper

Abstract The development of models for yield prediction in greenhouse sweet peppers may help improve yield and labour productivity. We aimed to monitor the growth and yield of hydroponically grown sweet pepper plants without destructive sampling. First, we constructed a prediction model and validated it in a cultivation experiment. In the developed model, daily node appearance and light use efficiency were predicted from daily mean air temperature and daytime carbon dioxide (CO2) concentration. The daily light interception was obtained by non-destructive leaf area estimation. Second, we validated the model through the cultivation experiment. The predicted total dry matter production at 200 days after transplanting (DAT), 1,379 g/m2, fell within the range of the observed value, 1,353 ± 46 g/m2 (mean ± SE). The predicted and observed yields at 200 DAT were 7.90 kg/m2 and 7.73 ± 0.82 kg/m2, respectively. We approximately predicted node appearance, total dry matter production, and fruit yield, while partially succeeding in predicting leaf area index and dry matter partitioning to fruit. Our non-destructive prediction model can be an effective tool for growers and to improve the yield of sweet pepper production.

A research on Non-Destructive Leaf Area Estimation Modeling for some Apple Cultivars

A research on Non-Destructive Leaf Area Estimation Modeling for some Apple Cultivars

Effect of the Cultivation Systems and Split Fertilizer Applications on the Growth and Yields of Tatsoi (Brassica rapa subsp. narinosa)

Tatsoi [Brassica rapa subsp. narinosa (L.H.Bailey) Hanelt] is a leafy vegetable with potential for cultivation in a riparian wetland ecosystem. The floating culture system has the advantage of ensuring a sufficient water supply in the growing substrate due to the continuous upward water movement caused by capillarity force. This study evaluated the responses of the tatsoi plant to split fertilizer applications under conventional cultivation and floating culture systems. The fertilizer treatments consisted of control (F0), single (F1), split into 2 (F2), split into 3 (F3) or split into 4 applications (F4). The results showed that early growth of tatsoi was better using floating culture than the conventional system. However, tatsoi adapted well to conventional and floating culture systems as long as sufficient nutrients were available. A single application of 6 g NPK fertilizer (16:16:16 v/v/v) at transplanting provided sufficient nutrients, as indicated by the number of leaves, total leaf area, canopy area, stem dry weight, and leaf dry weight at harvest. The leaf SPAD values in the tatsoi plants were higher in the floating culture system than in the conventional system at 14 and 20 DAT fertilized with a 4-split application. Transplanting occurred 14 days after seed planting (DAP), and harvest occurred 49 days later. A quadratic regression model using leaf length (L), leaf width (W), or L×W as predictors was reliable for non-destructive leaf area estimation in the tatsoi plant. It is recommended for farmers to apply a single NPK fertilizer at the rate of 6 g/plant to achieve an optimum yield in tatsoi cultivated using a conventional or floating culture system.
 HIGHLIGHTS
 
 Tatsoi plant grew well under conventional and floating culture systems as long as sufficient nutrients were available
 Splitting fertilizer into several times of application did not significantly better than single application of NPK fertilizer at time of transplanting
 SPAD values in the tatsoi leaves were higher in the floating culture than in the conventional system
 Length and width of leaves were reliable predictors for non-destructive accurate leaf area estimation in the tatsoi plant
 
 GRAPHICAL ABSTRACT

Non-destructive leaf area estimation of Fig (Ficus carica L.) cv. Iraqi grown in different root zone spatial limitation and controlled porosity level under greenhouse condition

Leaf area (LA) is associated with many agronomic and physiological processes including growth, photosynthesis, transpiration, photon interception, energy balance and yield potential of the plant. Accurate, rapid and non-destructive leaf area estimation is a useful subject of study for the fields of applied plant science especially with potted plants. However, till date there is lack of information available on Fig (Ficus carica L.) cv. Iraqi grown in containers under different root zone spatial limitation and controlled porosity level. Thus, this study aims to determine the effect of both factors on leaf area of the crop. Determining the individual LA of F. carica cv. Iraqi involves measurements of leaf parameters such as length (L) and width (W), or some combinations of these parameters. The widest part of the foliage was taken as leaf width (W) and leaf length (L) was defined as the distance between the two furthest points (from lamina tip to the point of petiole intersection along the midrib) of the foliage. The length (L) and width (W) of each leaf will be measured by using a leaf area meter having a sensor and read-out unit which calibrated to 0.01 cm 2. There is significant interaction was observed for each treatment. It can be concluded that application of the right root zone and porosity level gave positive effect on leaf area of F. carica cv. Iraqi grown under greenhouse condition.

Allometric models for non-destructive leaf area measurement of stevia: an in depth and complete analysis

ABSTRACT Leaf area measurement is pivotal for plant physiologists. Hence, accurate measurement of their leaf area is incredibly relevant in agronomic terms. The plant Stevia rebaudiana is a sucrose-free plant species that is now vital to the global production of sucrose-free foods. Here, we estimated S. rebaudiana leaf area using a nondestructive methodology comprising allometric equations. Through leaf length (L), leaf width (W), and/or their product (LW) the leaf area was determined. One thousand leaves were sampled from four distinct S. rebaudiana genotypes for model construction. Linear or power models were generated, and the best equation was selected using a statistical criterion. The statistical criteria indicated that the linear models best suited all genotypes tested, included a function of LW, exhibited increased stability, and precisely estimated coefficients. ANOVA revealed that both generalized and combined equations were feasible. Nevertheless, grouping all genotypes into a single model was not possible as the genotype leaf architectures were very dissimilar.

Plant Age Has a Minor Effect on Non-Destructive Leaf Area Calculations in Moso Bamboo (Phyllostachys edulis)

Leaf area is among the most important leaf functional traits, and it determines leaf temperature and alters light harvesting. The calculation of individual leaf area is the basis of calculating the leaf area index (i.e., the total leaf area per unit ground area) that is directly associated with the ability of plants to intercept light for photosynthesis. It is valuable to provide a fast and reliable approach to measuring leaf area. Here, we examined the validity and calculation accuracy of the Montgomery equation (ME), which describes the area of a leaf as a product of leaf length, width and a specific coefficient referred to as the Montgomery parameter, MP. Using ME, we calculated leaf areas of different age groups of bamboo culms. For most broad-leaved plants, leaf area is proportional to the product of leaf length and width, and MP falls within a range of 1/2 to π/4, depending on leaf shape. However, it is unknown whether there is an intra-specific variation in MP resulting from age structure and whether such a variation can significantly reduce the predictability of ME in calculating leaf area. This is relevant as a population of perennial plants usually composes of different age groups. We used Moso bamboos as model as this species is of ecological and economic importance in southern China, and pure stands can cover six to seven plant age groups. We used five age groups of moso bamboo and sampled 260–380 leaves for each group to test whether ME holds true for each group and all groups combined, whether there are significant differences in MP among different age groups, and whether the differences in MP can lead to large prediction errors for leaf area. We observed that for each age group and all groups combined, there were significant proportional relationships between leaf area and the product of leaf length and width. There were small but significant differences in MP among the five age groups (MP values ranged from 0.6738 to 0.7116 for individual plant ages; MP = 0.6936 for all age groups combined), which can be accounted for by the minor intergroup variation of leaf shape (reflected by the leaf width/length ratio). For all age classes, MP estimated for the pooled data resulted in <4% mean absolute percentage error, indicating that the effect of variation in MP among different age groups was small. We conclude that ME can serve as a useful tool for accurate calculations of leaf area in moso bamboo independent of culm age, which is valuable for estimation of leaf area index as well as evaluating the productivity and carbon sequestration capacity of bamboo forests.

A Simple, Inexpensive, and Portable Image-Based Technique for Nondestructive Leaf Area Measurements

This new 6-page article, part of a series introducing various image-based measurements for horticultural research, introduces a simple, inexpensive, and portable image-based technique for nondestructive leaf area measurements. It uses an imaging apparatus made with ordinary office supplies to obtain leaf images in greenhouse or field environments. Leaf images are then processed and analyzed to measure leaf area using ImageJ, an open-source image processing program. Because both image capture and analysis are performed nondestructively, leaf area can be measured on the same leaf repeatedly, enabling the monitoring of leaf growth over time, as well as photosynthesis and transpiration. This technique is particularly useful to researchers and students studying leaf growth and physiology in greenhouse or field environments. Written by Shinsuke Agehara, Lillian Pride, Mariel Gallardo, and Jose Hernandez-Monterroza, and published by the UF/IFAS Horticultural Sciences Department.https://edis.ifas.ufl.edu/hs1395

Non-Destructive Leaf Area Estimation Model for Overall Growth Performances in Relation to Yield Attributes of Cassava (Manihot esculenta Cranz) under Water Deficit Conditions

Cassava is a tropical storage root crop, a source of carbohydrate and alternative energy. It has been classified as “drought tolerant plant” for the whole life cycle, except during the root initiation stage (120-150 DAP). Leaf area index (LAI) is one of the most parameters representing the overall growth and yield prediction in cassava. The aim of this investigation was to validate the physiological and growth performance of cassava in responses to water deficit stress in the field trial as well as to investigate the leaf area index as an important factor to cassava growth and storage root bulking. Leaf relative water content in cassava declined significantly upon a long period of water withholding, and regulated non-photochemical quenching (NPQ), leading to chlorophyll degradation, reduced number of leaves and limited leaf area index (LAI) and loss of storage root yield when compared with well-irrigated plants. Non-destructive leaf area estimation model under water deficit stress condition using spectral reflectance to determine the LAI and VIs was validated. The Ratio Vegetation Index (RVI) was suitable model with high coefficient of determination (R2 = 0.89). However, the RVI as LAI at 150 DAP (120 d water withholding) could be considered as the critical point to indicate cassava growth and yield performance. Based on the results, cassava growth, biomass and yield in the different environments may further be investigated, taking into consideration the genotypic variation and using remote sensing technology for rapid monitoring and accurate and cost-effective data assessment.
 
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 In press - Online First. Article has been peer reviewed, accepted for publication and published online without pagination. It will receive pagination when the issue will be ready for publishing as a complete number (Volume 47, Issue 3, 2019). The article is searchable and citable by Digital Object Identifier (DOI). DOI link will become active after the article will be included in the complete issue.
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Leaf shape simulation of castor bean and its application in nondestructive leaf area estimation

Leaf shape simulation of castor bean and its application in nondestructive leaf area estimation

Estimating the leaf area of an individual tree in urban areas using terrestrial laser scanner and path length distribution model

Urban leaf area measurement is crucial to properly determining the effect of urban trees on micro-climate regulation, heat island effect, building cooling, air quality improvement, and ozone formation. Previous works on the leaf area measurement have mainly focused on the stand level, although the presence of individual trees is more common than forests in urban areas. The only feasible ways for an operational non-destructive leaf area measurement, namely, optical indirect methods, are mostly limited in urban areas because light path is constantly intercepted by surrounding buildings or other objects. A terrestrial laser scanner (TLS), which can extract an individual tree by using its unique distance information, provides a possibility for indirectly measuring the leaf area index (LAI) in urban areas. However, indirect LAI measurement theory, which uses the cosine of an observation zenith angle for path-length correction, is incompatible for an individual tree because the representative projected area of LAI changes while the observation zenith angle changes, thus making the results incomparable and ambiguous. Therefore, we modified a path length distribution model for the leaf area measurement of an individual tree by replacing the traditional cosine path length correction for a continuous canopy with real path length distribution. We reconstructed the tree crown envelope from a TLS point cloud and calculated a real path length distribution through laser pulse-envelope intersections. Consequently, leaf area density was separated from the path length distribution model for leaf area calculation. Comparisons with reference measurement for an individual tree showed that the TLS-derived leaf area using the path length distribution is insensitive to the scanning resolution and agrees well with an allometric measurement with an overestimation from 5 m2 to 18 m2 (3–10%, respectively). Results from different stations are globally consistent, and using a weighted mean for different stations by sample numbers further improves the universality and efficiency of the proposed method. Further automation of the proposed method can facilitate a rapid and operational leaf area extraction of an individual tree for urban climate modeling.

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages.

Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices.

Estimation of Leaf Area for Blanc Du Bois Vitis spp., Carlos Vitis rotundifolia, and Southern Home Vitis rotundifolia x Vitis vinifera Using Simple Linear Measurement

Florida hybrid (Vitis spp.) and muscadine (V. rotundifolia Michx.) grape acreage is increasing in the south-eastern United States for the fresh market, wine production, and other diversified products. There is little research on determining leaf area in Muscadine grapes that could be used to assess canopy balance. Previous research on nondestructive methods of assessing canopy leaf area in V. vinifera L. and V. labrusca L. varieties may not apply to V. rotundifolia and Florida hybrid varieties. Thus this research sought to verify the accuracy previous non-destructive methods for V. rotundifolia and Vitis spp. varieties grown in Florida. Linear measurements were taken on fully expanded leaf of ‘Blanc Du Bois’ (Vitis spp.), ‘Carlos’ (V. rotundifolia Michx.) and ‘Southern Home’ (interspecific V. rotundifolia × V. vinifera hybrid). Maximum length and width were used to predict leaf area using linear regressions. For each cultivar, 5 linear regression equations were developed to estimate leaf area using length and width. Length and width were log transformed and an additional 3 linear equations were developed. Equations with the product of length and width had a higher coefficient of determination for all three varieties. Therefore, these equations are recommended for use when conducting non-destructive leaf area measurements of muscadine and Florida hybrid grape varieties.

Apical Dominance and Planting Density Effects on Weed Suppression by Sunn Hemp (Crotalaria juncea L.)

A field study was conducted in 2008 and 2009 in Citra, FL, to evaluate the effects of seeding rate and removal of apical dominance of sunn hemp (Crotalaria juncea L.) on weed suppression and seed production by sunn hemp. Three seeding rates of sunn hemp were used: a representative seed production rate of 11 kg·ha−1, an intermediate seeding rate of 28 kg·ha−1, and a cover crop seeding rate of 45 kg·ha−1. Cutting the main stem at 3, 4, or 5 weeks after planting to break apical dominance was compared with an uncut treatment. Cutting had no significant effect on shoot biomass, photosynthetically active radiation (PAR) penetrating the canopy, and nondestructive leaf area index (LAI). As a result, cutting also had no effect on weed density and biomass in 2008 and very little effect in 2009. Increase in seeding rate resulted in linear decrease in PAR and increase in LAI in both years. Seeding rate had a greater effect on suppression of weed biomass than on suppression of weed density. There was a linear decline in sunn hemp branching with increased seeding rate in 2009 and, averaged across years, flower number decreased linearly with increased seeding rate. Cutting to break apical dominance induced branching but had no effect on flower number. No seed pod production occurred and we postulate that the lack of seed production may be the result of the absence of effective pollinators in fall when short-day varieties of sunn hemp flower in Florida.

Influence of woody tissue and leaf clumping on vertically resolved leaf area index and angular gap probability estimates

Leaf area index L is a key vegetation parameter that can be used in soil–vegetation–atmosphere exchange modeling. To represent the structure of ecosystems in vertically distributed modeling, vertically resolved L distributions as well as vertical and angular gap probability Pgap distributions are needed, but they are rarely available. On the experimental side, studies often neglect woody plant components when using indirect methods for L or Pgap observations. This can lead to significantly biased results, particularly in semi-arid savannah-type ecosystems with low L values.The objective of this study is to compare three non-destructive leaf area index measurement techniques in a sparse savannah-type cork oak canopy in central Portugal in order to derive vertically resolved L as well as vertically and angularly resolved Pgap. We used the established LAI-2000 device as well as fast digital cover photography (DCP), which was vertically and angularly distributed. We applied object-based image analysis to DCP to exclude woody plant components. We compared the results with vertically distributed LAI-2000 measurements and with vertical estimates based on easily measurable crown parameters.Height and angularly distributed DCP was successfully applied here for the first time. It delivers gap probability Pgap and effective leaf area index Le measurements that are comparable to the established LAI-2000. The height and angularly dependent leaf clumping index Ω could be determined with DCP, which led to a 30% higher total leaf area index L for DCP compared to LAI-2000. The exclusion of woody tissue from DCP yields on average a 6.9% lower leaf area index L. Including Ω and excluding woody tissue, the L of DCP matched precisely with direct measurements using litter traps. However, the set-up and site-specific adjustment of the image analysis algorithm remains challenging. We propose a special filter for LAI-2000 to enhance data quality when used in open canopies. Finally, if height-dependent observations are not feasible, ground-based observations of crown parameters can be used to derive very reasonable L height distributions from a single, ground-based L observation.

A simple linear model for leaf area estimation in Persian walnut (Juglans regia L.)

Accurate non-destructive leaf area estimation is a useful subject of study for the fields of applied plant science, physiology and plant genetic engineering. The relationship between leaf area and fruit is very important in fruit crops. It determines the nut size and nut filling potential. The aim of this paper is to produce a statistical model based on linear measurements such as leaflet length and width in combination with other simple parameters. Measurements of leaf length, width and surface area of the leaflets were achieved using digital photography. Digital images of Persian walnut (Juglans regia L.) leaves were prepared in August 2011 for 14 genotypes and in 2012 for one genotype under open field conditions. Results of regression analysis indicated several models are suitable to estimate leaf area of Persian walnut. The linear model with LW as an independent variable (LA=1.11+0.69LW) with accurate estimation (maximum R2=0.99 and lowest MSE=10.09) was the best model. To determine the leaf area, the earliest measurements to take would be the length and width of leaflets. Total area of the leaflets is then reported as leaf area. In conclusion, in walnut, number of leaflets and leaflet area should be involved in the estimation of leaf area.

Allometric models for non-destructive leaf area estimation of Jatropha curcas

We evaluated the current used allometric models and proposed a reliable and accurate model using non-destructive measurements of leaf length (L) and/or width (W) for estimating the leaf area of the Jatropha plant ( Jatropha curcas L.). For model construction, a total of 1200 leaves were randomly selected from different levels of the tree canopies and encompassed the full spectrum of measurable leaf sizes (0.19–367 cm 2 ). Power models better fit the Jatropha leaf area (LA) compared to linear models; however, when the LW was used, the intercept was removed from the linear model to precisely estimate LA. To validate these models, two independent data sets of 300 leaves were used. We demonstrate that the currently used models are biased and underestimate the area of Jatropha leaves. We developed an unbiased, single power model (Y = x β1 ) based on two leaf dimensions [LA = (LW) 0.9660 ] with high precision and accuracy as well as a random dispersion pattern of the residuals. The model may be simplified by using only one leaf dimension (Y = x β1 ), LA = L 1.9644 or LA = W 1.8929 . However, when either W or L alone was used as the single leaf dimension, the power model predicted the LA with good accuracy at the expense heteroscedastic residual dispersion behavior. ► A rapid and simple model was developed to predict the leaf area for the purging nut. ► The models are simple and able to produce accurate results without expensive equipment. ► Researchers can accurately estimate large quantities leaf areas of purging nut plants. ► The current model to estimate leaf area of the jatropha underestimate LA.

A simple model for nondestructive leaf area estimation in bedding plants

Measurement of leaf area is commonly used in many horticultural research experiments, but it is generally destructive, requiring leaves to be removed for measurement. Determining the individual leaf area (LA) of bedding plants like pot marigold (Calendula officinalis L.), dahlia (Dahlia pinnata), sweet William (Dianthus barbatus L.), geranium (Pelargonium × hortorum), petunia (Petunia × hybrida), and pansy (Viola wittrockiana) involves measurements of leaf parameters such as length (L) and width (W) or some combinations of these parameters. Two experiments were carried out during spring 2010 (on two pot marigold, four dahlia, three sweet William, four geranium, three petunia, and three pansy cultivars) and summer 2010 (on one cultivar per species) under greenhouse conditions to test whether a model could be developed to estimate LA of bedding plants across cultivars. Regression analysis of LA versus L and W revealed several models that could be used for estimating the area of individual bedding plants leaves. A linear model having LW as the independent variable provided the most accurate estimate (highest R 2, smallest mean square error, and the smallest predicted residual error sum of squares) of LA in all bedding plants. Validation of the model having LW of leaves measured in the summer 2010 experiment coming from other cultivars of bedding plants showed that the correlation between calculated and measured bedding plants leaf areas was very high. Therefore, these allometric models could be considered simple and useful tools in many experimental comparisons without the use of any expensive instruments.