scholarly journals Estimating the area and weight of cactus forage cladodes using linear dimensions

2020 ◽  
Vol 43 ◽  
pp. e45460
Author(s):  
Leandro Ricardo Rodrigues de Lucena ◽  
Maurício Luiz de Mello Vieira Leite ◽  
Vicente José Laamon Pinto Simões ◽  
Camila Nóbrega ◽  
Mirna Clarissa Rodrigues Almeida ◽  
...  
Keyword(s):  
Plant Growth ◽  
Regression Model ◽  
Efficient Method ◽  
Gravimetric Method ◽  
Power Model ◽  
Gamma Model ◽  
Linear Dimensions ◽  
Length Width ◽  
Non Destructive ◽  
Nopalea Cochenillifera

The forage palm is one of the main forages of ruminants in semiarid regions. Measurements of leaf area are required in agronomic studies because they are one of the main parameters used to evaluate plant growth. The objective of this study was to validate and define the best models for estimating the area and weight of Giant Sweet clone (Nopalea cochenillifera) forage cladodes in a non-destructive way based on the linear dimensions of length, width and thickness. There were 432 randomly measured cladodes at 550 days after planting. The length, width and thickness of each cladode were measured using a digital calliper. The cladodes were weighed individually. The cladode area was calculated by the gravimetric method. The power regression model was the most efficient method to explain the cladode area as a function of the product of length by width, while the gamma model was the most efficient method to explain the weight of cladodes as a function of the product of length by width and thickness. The power model,  and gamma model, = 0.536T + 0.028LW, were used to determine the area and weight of Nopalea cochenillifera Giant Sweet clone cladodes, respectively, based on the values of linear dimensions measured independently of the order of the cladode.

scholarly journals Alometric Model for Estimation of Leaf Area of Garcinia brasiliensis Mart. Through Non-destructive Method

2019 ◽  
Vol 11 (10) ◽  
pp. 154
Author(s):  
Vinicius de Souza Oliveira ◽  
Cássio Francisco Moreira de Carvalho ◽  
Juliany Morosini França ◽  
Flávia Barreto Pinto ◽  
Karina Tiemi Hassuda dos Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Linear Equations ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Maximum Width ◽  
Linear Dimensions ◽  
Length Width ◽  
Independent Variable ◽  
The Mean ◽  
Non Destructive

The objective of the present study was to test and establish mathematical models to estimate the leaf area of Garcinia brasiliensis Mart. through linear dimensions of the length, width and product of both measurements. In this way, 500 leaves of trees with age between 4 and 6 years were collected from all the cardinal points of the plant in the municipality of São Mateus, North of the State of Espírito Santo, Brazil. The length (L) along the main midrib, the maximum width (W), the product of the length with the width (LW) and the observed leaf area (OLA) were obtained for all leaves. From these measurements were adjusted linear equations of first degree, quadratic and power, in which OLA was used as dependent variable as function of L, W and LW as independent variable. For the validation, the values of L, W and LW of 100 random leaves were substituted in the equations generated in the modeling, thus obtaining the estimated leaf area (ELA). The values of the means of ELA and OLA were tested by Student’s t test 5% of probability. The mean absolute error (MAE), root mean square error (RMSE) and Willmott’s index d for all proposed models were also determined. The choice of the best model was based on the non significant values in the comparison of the means of ELA and OLA, values of MAE and RMSE closer to zero and value of the index d and coefficient of determination (R2) close to unity. The equation that best estimates leaf area of Garcinia brasiliensis Mart. in a way non-destructive is the power model represented by por ELA = 0.7470(LW)0.9842 and R2 = 0.9949.

scholarly journals Leaf blade area estimate of Digitaria pentzii under different cutting Heights

2020 ◽  
Vol 21 ◽  
Author(s):  
Raul Caco Alves Bezerra ◽  
Mauricio Luiz de Mello Vieira Leite ◽  
Mirna Clarissa Rodrigues de Almeida ◽  
Leandro Ricardo Rodrigues de Lucena ◽  
Vicente José Laamon Pinto Simões ◽  
...  
Keyword(s):  
Regression Models ◽  
Leaf Blade ◽  
Gravimetric Method ◽  
Power Model ◽  
Average Value ◽  
Area Estimate ◽  
Blade Area ◽  
Cutting Height ◽  
Non Destructive ◽  
Digital Caliper

Abstract Pasture studies require information on leaf area, as it is one of the main parameters for evaluation of plant growth. Thus, the objective of this study was to estimate the leaf blade area of pangolão grass (Digitaria pentzii Stent.) using non-destructive methods by regression model analysis. The experimental design consisted of randomized blocks, with three cutting heights (10, 15, and 20 cm) and four replications. Three hundred leaf blades of pangolão grass were randomly collected, and their respective lengths (L) and widths (W) determined using a digital caliper. The leaf blade area of pangolão grass was estimated by the gravimetric method, using linear and power regression models to explain the leaf blade area as a function of the product of L and maximum W. The real leaf blade area presented an average value of 18.64 cm2, ranging from 4.29 to 45.95 cm2. The leaf blade area of pangolão grass, regardless of cutting height, was estimated with greater accuracy by the power model. The power model, Ŷ=LW1.007, can be used to estimate the leaf blade area of pangolão grass based on leaf blade L and W values.

scholarly journals Growth curve Nopalea cochenillifera in fractionation function of cladodes using power regression model

2018 ◽  
Vol 36 (3) ◽  
pp. 578
Author(s):  
Leandro Ricardo Rodrigues de LUCENA ◽  
Juliana De Souza PEREIRA ◽  
Maurício Luiz de Mello Vieira LEITE
Keyword(s):  
Plant Growth ◽  
Regression Model ◽  
Pest Control ◽  
Growth Curve ◽  
Regression Models ◽  
Growth Period ◽  
Coefficient Of Determination ◽  
Planting Method ◽  
Nopalea Cochenillifera

In this work we evaluate the growth length of bud of Nopalea cochenillifera using five different forms of crops through power regression model. The adjusted models showed very similar estimates of lengths observed independent using of planting method. The power regression models showed coefficient of determination of model high 99.65% (treatment 1), 99.82% (treatment 2), 99.26% (treatment 3), 99.93% (treatment 4) and 99.34% (treatment 5). The power regression model proved effective to model the growth length of Nopalea cochenillifera of bud can generate strategies and plans for future plantings, as well useful information as: appropriate crop management, increased plant growth period and pest control.

Ab Initio Two-sites Occupancy and Broadband Near-Infrared Emission of Cr3+ in Li2MgZrO4

2021 ◽  
Author(s):  
Xue Zhou ◽  
Jinmeng Xiang ◽  
Jiming Zheng ◽  
Xiaoqi Zhao ◽  
Hao Suo ◽  
...  
Keyword(s):  
Plant Growth ◽  
Ab Initio ◽  
Light Source ◽  
Near Infrared ◽  
Broad Band ◽  
Infrared Emission ◽  
Night Vision ◽  
Light Emitting ◽  
Non Destructive ◽  
Near Infrared Emission

Near-infrared (NIR) phosphor-converted light-emitting diodes (pc-LEDs) light source have great potential in non-destructive detection, promoting plant growth and night vision applications, while the discovery of a broad-band NIR phosphor still...

Correlation of pH on Soil Physicochemical Properties using Linear Regression Model for Spectral Data Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 3492-3500
Author(s):  
Vipin Y. Borole ◽  
◽  
Sonali B. Kulkarni ◽  
Keyword(s):  
Linear Regression ◽  
Plant Growth ◽  
Physicochemical Properties ◽  
Regression Model ◽  
Soil Properties ◽  
Spectral Data ◽  
Linear Regression Model ◽  
Soil Analysis ◽  
Soil Physicochemical Properties ◽  
Analytical Technique

Soil properties may be varied by spatially and temporally with different agricultural practices. An accurate and reliable soil properties assessment is challenging issue in soil analysis. The soil properties assessment is very important for understanding the soil properties, nutrient management, influence of fertilizers and relation between soil properties which are affecting the plant growth. Conventional laboratory methods used to analyses soil properties are generally impractical because they are time-consuming, expensive and sometimes imprecise. On other hand, Visible and infrared spectroscopy can effectively characterize soil. Spectroscopic measurements are rapid, precise and inexpensive. Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. In the present research, we use spectroscopy techniques for soil properties analysis. The spectra of agglomerated farming soils were acquired by the ASD Field spec 4 spectroradiometer. Different fertilizers treatment applied soil samples are collected in pre monsoon and post monsoon season for 2 year (4 season) for banana and cotton crops in the form of DS-I and DS-II respectively. The soil spectra of VNIR region were preprocessed to get pure spectra. Then process the acquired spectral data by statistical methods for quantitative analysis of soil properties. The detected soil properties were carbon, Nitrogen, soil organic matter, pH, phosphorus, potassium, moisture sand, silt and clay. Soil pH is most important chemical properties that describe the relative acidity or alkalinity of the soil. It directly effect on plant growth and other soil properties. The relationship between pH properties on soil physical and chemical parameters and their influence were analyses by using linear regression model and show the performance of regression model with R2 and RMSE. Keywords soil; physicochemical properties; spectroscopy; pH

scholarly journals Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch

2021 ◽  
Vol 22 (10) ◽  
Author(s):  
Benyamin Lakitan ◽  
Kartika Kartika ◽  
Laily Ilman Widuri ◽  
Erna Siaga ◽  
Lya Nailatul Fadilah
Keyword(s):  
Regression Model ◽  
Leaf Area ◽  
Morphological Traits ◽  
Leafy Vegetables ◽  
Stem Cuttings ◽  
Total Leaf Area ◽  
Tropical Ecosystem ◽  
Total Leaf ◽  
Tropical Conditions ◽  
Non Destructive

Abstract. Lakitan B, Kartika K, Widuri LI, Siaga E, Fadilah LN. 2021. Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch. Biodiversitas 22: 4487-4495. Talinum paniculatum known as Java ginseng is an ethnic vegetable in Indonesia that has also been utilized as a medical plant. Young leaves are the primary economic part of T. paniculatum, which can be eaten fresh or cooked. This study was focused on characterizing morphological traits of T. panicultaum and developing a non-destructive yet accurate and reliable model for predicting total area per leaf cluster on each elongated branch per flush growth cycle. The non-destructive approach allows frequent and timely measurements. In addition, the developed model can be used as guidance for deciding the time to harvest for optimum yield. Results indicated that T. paniculatum flourished rapidly under wet tropical conditions, especially if they were propagated using stem cuttings. The plants produced more than 50 branches and more than 800 leaves, or on average produced more than 15 leaves per branch at the age of nine weeks after planting (WAP). The zero-intercept linear model using a combination of two traits of length x width (LW) as a predictor was accurate and reliable for predicting a single leaf area (R2 = 0.997). Meanwhile, the estimation of total area per leaf cluster was more accurate if three traits, i.e., number of leaves, the longest leaf, and the widest leaf in each cluster were used as predictors with the zero-intercept linear regression model (R2 = 0.984). However, the use of a single trait of length (L) and width (W) of the largest leaf within each cluster as a predictor in the power regression model exhibited moderately accurate prediction at the R2 = 0.883 and 0.724, respectively.

scholarly journals New order-statistics-based ranking models and faster computation of outcome probabilities

2019 ◽  
Vol 31 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Rose Baker
Keyword(s):  
Order Statistics ◽  
Efficient Method ◽  
Consumer Preference ◽  
New Order ◽  
Fast Method ◽  
Gamma Model ◽  
Ranking Models ◽  
Pareto Distributions ◽  
Type V ◽  
Iterated Integral

Abstract In sport, order-statistics-based models such as Henery’s gamma model and the Thurstone–Mosteller type V model are useful in estimating competitor strengths from observed performance of players in competitions between two or more players. They can also be applied in many other areas, such as analysis of consumer preference data, which would be useful to marketing management. Two new families of such models derived from the exponentiated exponential, and Pareto distributions are introduced. Use of order-statistics-based models when there are more than two competitors has been hampered by lack of an efficient method of computation of outcome probabilities as a function of competitor strengths, and a fast method of computation of outcome probabilities is presented, which exploits the fact that the integral to be evaluated is an iterated integral.

scholarly journals CARS Algorithm-Based Detection of Wheat Moisture Content before Harvest

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 115
Author(s):  
Hong Ji ◽  
Wanzhang Wang ◽  
Dongfeng Chong ◽  
Boyang Zhang
Keyword(s):  
Moisture Content ◽  
Regression Model ◽  
Regression Models ◽  
Partial Least Squares Regression ◽  
Early Stage ◽  
Least Squares Regression ◽  
Calibration Sample ◽  
Non Destructive ◽  
Plsr Model ◽  
Variable Subset

To rapidly detect the wheat moisture content (WMC) without harm to the wheat and before harvest, this paper measured wheat and panicle moisture content (PMC) and the corresponding spectral reflectance of panicle before harvest at the Beijing Tongzhou experimental station of China Agricultural University. Firstly, we used correlation analysis to determine the optimal regression model of WMC and PMC. Secondly, we derived the spectral sensitive band of PMC before filtering the redundant variables competitive adaptive reweighted sampling (CARS) to select the variable subset with the least error. Finally, partial least squares regression (PLSR) was used to build and analyze the prediction model of PMC. At the early stage of wheat harvest, a high correlation existed between WMC and PMC. Among all regression models such as exponential, univariate linear, polynomial models, and the power function regression model, the logarithm regression model was the best. The determination coefficients of the modeling sample were: R2 = 0.9284, the significance F = 362.957, the determination coefficient of calibration sample R2v = 0.987, the root mean square error RMSEv = 3.859, and the relative error REv = 7.532. Within the range of 350–2500 nm, bands of 728–907 nm, 1407–1809 nm, and 1940–2459 nm had a correlation coefficient of PMC and wavelength reflectivity higher than 0.6. This paper used the CARS algorithm to optimize the variables and obtained the best variable subset, which included 30 wavelength variables. The PLSR model was established based on 30 variables optimized by the CARS algorithm. Compared with the all-sensitive band, which had 1103 variables, the PLSR model not only reduced the number of variables by 1073, but also had a higher accuracy in terms of prediction. The results showed that: RMSEC = 0.9301, R2c = 0.995, RMSEP = 2.676, R2p = 0.945, and RPD = 3.362, indicating that the CARS algorithm could effectively remove the variables of spectral redundant information. The CARS algorithm provided a new way of thinking for the non-destructive and rapid detection of WMC before harvest.

scholarly journals Methods of the Detection and Identification of Structural Defects in Saturated Metallic Composite Castings

2017 ◽  
Vol 17 (3) ◽  
pp. 37-44 ◽  
Author(s):  
K. Gawdzińska
Keyword(s):  
Electron Microscopy ◽  
Complex Structure ◽  
Gravimetric Method ◽  
Structural Defects ◽  
X Ray ◽  
Metallic Composite ◽  
Detection And Identification ◽  
Metallic Composites ◽  
Non Destructive ◽  
Scanning Electron

Abstract Diagnostics of composite castings, due to their complex structure, requires that their characteristics are tested by an appropriate description method. Any deviation from the specific characteristic will be regarded as a material defect. The detection of defects in composite castings sometimes is not sufficient and the defects have to be identified. This study classifies defects found in the structures of saturated metallic composite castings and indicates those stages of the process where such defects are likely to be formed. Not only does the author determine the causes of structural defects, describe methods of their detection and identification, but also proposes a schematic procedure to be followed during detection and identification of structural defects of castings made from saturated reinforcement metallic composites. Alloys examination was conducted after technological process, while using destructive (macroscopic tests, light and scanning electron microscopy) and non-destructive (ultrasonic and X-ray defectoscopy, tomography, gravimetric method) methods. Research presented in this article are part of author’s work on castings quality.

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