scholarly journals A Simple, Cost-Effective Method for Leaf Area Estimation

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
S. K. Pandey ◽  
Hema Singh
Keyword(s):  
Leaf Area ◽  
Cost Effective ◽  
Botanical Garden ◽  
Area Estimation ◽  
Graph Paper ◽  
Cost Effective Method ◽  
Cost Effective Alternative ◽  
Cost Efficient ◽  
Non Destructive ◽  
Paper Method

Easy, accurate, inexpensive, and nondestructive methods to determine individual leaf area of plants are a useful tool in physiological and agronomic studies. This paper introduces a cost-effective alternative (called here millimeter graph paper method) for standard electronic leaf area meter, using a millimeter graph paper. Investigations were carried out during August–October, 2009-2010, on 33 species, in the Botanical garden of the Banaras Hindu University at Varanasi, India. Estimates of leaf area were obtained by the equation, leaf area (cm2) = x/y, where x is the weight (g) of the area covered by the leaf outline on a millimeter graph paper, and y is the weight of one cm2 of the same graph paper. These estimates were then compared with destructive measurements obtained through a leaf area meter; the two sets of estimates were significantly and linearly related with each other, and hence the millimeter graph paper method can be used for estimating leaf area in lieu of leaf area meter. The important characteristics of this cost-efficient technique are its easiness and suitability for precise, non-destructive estimates. This model can estimate accurately the leaf area of plants in many experiments without the use of any expensive instruments.

Non-destructive leaf area estimation in banana

1991 ◽  
Vol 45 (3-4) ◽  
pp. 251-254 ◽  
Author(s):  
M.V. Potdar ◽  
K.R. Pawar
Keyword(s):  
Leaf Area ◽  
Area Estimation ◽  
Non Destructive

scholarly journals Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less

2015 ◽  
Vol 75 (1) ◽  
pp. 152-156 ◽  
Author(s):  
MC. Souza ◽  
CL. Amaral
Keyword(s):  
Linear Regression ◽  
Leaf Area ◽  
Leaf Development ◽  
Linear Equations ◽  
Accurate Estimate ◽  
Area Estimation ◽  
Leaf Dimensions ◽  
Non Destructive ◽  
Linear Regressions ◽  
Very High

Leaf area estimation is an important biometrical trait for evaluating leaf development and plant growth in field and pot experiments. We developed a non-destructive model to estimate the leaf area (LA) of Vernonia ferruginea using the length (L) and width (W) leaf dimensions. Different combinations of linear equations were obtained from L, L2, W, W2, LW and L2W2. The linear regressions using the product of LW dimensions were more efficient to estimate the LA of V. ferruginea than models based on a single dimension (L, W, L2 or W2). Therefore, the linear regression “LA=0.463+0.676WL” provided the most accurate estimate of V. ferruginea leaf area. Validation of the selected model showed that the correlation between real measured leaf area and estimated leaf area was very high.

scholarly journals ESTIMATING ABOVE GROUND BIOMASS OF <i>PINUS ROXBHURGHII</i> USING SLOPE BASED VEGETATION INDEX MODEL

2019 ◽  
Vol IV-5/W2 ◽  
pp. 35-42
Author(s):  
A. M. Joshi ◽  
S. Shahnawaz ◽  
B. Ranjit
Keyword(s):  
Vegetation Index ◽  
Cost Effective ◽  
Regression Equation ◽  
Pinus Roxburghii ◽  
Community Forests ◽  
Index Model ◽  
Cost Effective Method ◽  
Non Destructive ◽  
Planted Tree

Abstract. Pinus roxburghii is one of the important and most widely planted tree species in Nepal. Despite its large abundance and high economic values, limited studies on its AGB have been conducted in Nepal, especially using in situ non-destructive method. There are different methods to study the AGB. Regression equation based on the correlation between VI and AGB is cost effective method, and replicable in another sites of similar environment by just acquiring satellite images. Numerous methods have been developed to calculate VIs and each calculated VI shows different relation with AGB in different environments for same species. Therefore, there is a need to identify a most appropriate VI that has the highest correlation with AGB of P. roxburghii. The current study was carried out in Hattiban and Dollu community forests of Kathmandu district, using ResourceSat-2 imagery. In this study, Slope based VIs were used. Regression analysis between slope based VIs and AGB showed that relation between all VIs and AGB were significant. However, NDVI had the highest relation with AGB compared to others. Therefore, it was concluded that NDVI was the most appropriate VI to estimate AGB of P. roxburghii, and the regression equation with NDVI was used to estimate the AGB of P. roxburghii in the study area.

Allometric models for non-destructive leaf area estimation in Eugenia uniflora (L.)

2018 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
M. F. Pommpelli ◽  
J. M. Figueirôa ◽  
F. Lozano-Isla
Keyword(s):  
Leaf Area ◽  
Allometric Models ◽  
Eugenia Uniflora ◽  
Area Estimation ◽  
Non Destructive

scholarly journals Non-destructive and Destructive Methods to Determine the Leaf Area of Zucchini

2020 ◽  
Vol 8 (3) ◽  
pp. 295
Author(s):  
Adriano Bicioni Pacheco ◽  
Jéssica Garcia Nascimento ◽  
Larissa Brêtas Moura ◽  
Tárcio Rocha Lopes ◽  
Sergio Nascimento Duarte ◽  
...  
Keyword(s):  
Leaf Area ◽  
Prediction Models ◽  
Standard Procedure ◽  
Efficiency Index ◽  
Estimation Model ◽  
Statistical Parameters ◽  
Important Indicator ◽  
Area Estimation ◽  
Non Destructive ◽  
Destructive Methods

Leaf area estimation is a very important indicator in studies related to plant anatomy, morphology and physiology, and in many cases, it is a fundamental criterion to understand plant response to input conditions. Although there are leaf area prediction models have been produced for some plant species, a leaf area estimation model has not yet been developed for the zucchini. The objective of this paper was to determine the leaf area based on destructive and non-destructive methods for zucchini. The accuracy of measurement methods was evaluated and compared to determinations of the leaf area by the scanning integration method (LICOR equipment LI 3100C), considered as standard procedure. Non-destructive methods consisted of digital photography and measurement of leaf dimensions (width and length) based on ImageJ software. The destructive methods used were a) leaf area integrator LI-3100C, b) determination of leaf mass and c) weighing of leaf discs punched from the leaves. According to statistical parameters that evaluate the performance of the analyzed methods: determination coefficient (R2), Pearson (r) correlation coefficient, Willmott agreement index (d) and Camargo and Sentelhas performance index (c) the parameters presented values higher than 0.8820, classifying the methods as very good, whereas the modeling efficiency index (NSE) and the percentage of bias (PBIAS) also classified the methods as very good (0.87≤NSE≤0.99; -4.80≤PBIAS≤1.40), except the ImageJ method without correction (NSE=0.77; PBIAS = -22.70).

scholarly journals Quantification of Kaolinite and Halloysite Using Machine Learning from FTIR, XRF, and Brightness Data

Minerals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1350
Author(s):  
Pieter I. Du Plessis ◽  
Michael F. Gazley ◽  
Stephanie L. Tay ◽  
Eliza F. Trunfull ◽  
Manuel Knorsch ◽  
...  
Keyword(s):  
Cost Effective ◽  
X Ray Diffraction ◽  
Yilgarn Craton ◽  
Weathering Profile ◽  
X Ray ◽  
Clay Deposits ◽  
Cost Effective Method ◽  
Cost Effective Alternative ◽  
Quantified Analysis

Quantification of halloysite and kaolinite in clay deposits from X-ray diffraction (XRD) commonly requires extensive sample preparation to differentiate the two phyllosilicates. When assessing hundreds of samples for mineral resource estimations, XRD analyses may become unfeasible due to time and expense. Fourier transform infrared (FTIR) analysis is a fast and cost-effective method to discriminate between kaolinite and halloysite; however, few efforts have been made to use this technique for quantified analysis of these minerals. In this study, we trained machine- and deep-learning models on XRD data to predict the abundance of kaolinite and halloysite from FTIR, chemical composition, and brightness data. The case study is from the Cloud Nine kaolinite–halloysite deposit, Noombenberry Project, Western Australia. The residual clay deposit is hosted in the saprolitic and transition zone of the weathering profile above the basement granite on the southwestern portion of the Archean Yilgarn Craton. Compared with XRD quantification, the predicted models have an R2 of 0.97 for kaolinite and 0.96 for halloysite, demonstrating an excellent fit. Based on these results, we demonstrate that our methodology provides a cost-effective alternative to XRD to quantify kaolinite and halloysite abundances.

scholarly journals A simple and non-destructive model for individual leaf area estimation in citrus

Fruits ◽  
2010 ◽  
Vol 65 (5) ◽  
pp. 269-275 ◽  
Author(s):  
Renata Bachin Mazzini ◽  
Rafael Vasconcelos Ribeiro ◽  
Rose Mary Pio
Keyword(s):  
Leaf Area ◽  
Area Estimation ◽  
Individual Leaf ◽  
Non Destructive
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