scholarly journals Non-destructive Method for Estimating the Leaf Area of Pear cv. ‘Triunfo’

2019 ◽  
Vol 11 (7) ◽  
pp. 14
Author(s):  
Vinicius de Souza Oliveira ◽  
Karina Tiemi Hassuda dos Santos ◽  
Andréia Lopes de Morais ◽  
Gleyce Pereira Santos ◽  
Jéssica Sayuri Hassuda Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Mean Squared Error ◽  
Linear Equations ◽  
Absolute Error ◽  
Equation Model ◽  
Maximum Width ◽  
Northern Side ◽  
Mathematical Equations ◽  
The Mean ◽  
High Degree

The present study had as objective to determine mathematical equations to estimate the leaf area of pear cv. ‘Triunfo’ using linear dimensions of the leaves. For that, 300 healthy leaves of different sizes from each quadrant of plants from the small farm of Boa Vista located in the city of Montanha, at the northern side of the State of Espírito Santo, Brazil were used. The length (L) along the main vein was measured, along with the maximum width (W) of the leaf blade and observed leaf area (OLA), in addition to the product of the length and width (LW) of each leaf. From these measurements models of linear equations of first degree, quadratic and power were adjusted and their respective R2, using OLA as dependent variable and L, W and LW as independent variable. Based on the proposed equations, the data were validated obtaining the estimated leaf area (ELA). The mean of the ELA and OLA were compared by Student t test 5% probability. The mean error (E), the mean absolute error (MAE) and the root mean squared error (RMSE) was also used as validation criterion. The best equation model was defined based on the non-significant values from the comparison of means of ELA and OLA, E, MAE and RMSE values closer to zero and highest R2. The leaf area of pear cv. ‘Triunfo’ can be estimated by the equation ELA = -0.432338 + 0.712862(LW) non-destructively and with a high degree of precision.

scholarly journals Estimation of Leaf Area of Jackfruit Through Non-destructive Method

2019 ◽  
Vol 11 (6) ◽  
pp. 77
Author(s):  
Vinicius de Souza Oliveira ◽  
Leonardo Raasch Hell ◽  
Karina Tiemi Hassuda dos Santos ◽  
Hugo Rebonato Pelegrini ◽  
Jéssica Sayuri Hassuda Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Linear Equations ◽  
Absolute Error ◽  
Order Model ◽  
Sanitary Condition ◽  
Federal Institute ◽  
Independent Variable ◽  
Mathematical Equations ◽  
The Mean ◽  
Non Destructive

The objective of this study was to determine mathematical equations that estimate the leaf area of jackfruit (Artocarpus heterophyllus) in an easy and non-destructive way based on linear dimensions. In this way, 300 leaves of different sizes and in good sanitary condition of adult plants were collected at the Federal Institute of Espírito Santo, Campus Itapina, located in Colatina, municipality north of the State of Espírito Santo, Brazil. Were measured The length (L) along the midrib and the maximum leaf width (W), observed leaf area (OLA), besides the product of the multiplication of length with width (LW), length with length (LL) and width with width (WW). The models of linear equations of first degree, quadratic and power and their respective R2 were adjusted using OLA as dependent variable in function of L, W and LW, LL and WW as independent variable. The data were validated and the estimated leaf area (ELA) was obtained. The means of ELA and OLA were compared by Student’s t test (5% probability) and were evaluated by the mean absolute error (MAE) and root mean square error (RMSE) criteria. The choice of the best model was based on non-significant comparative values of ELA and OLA, in addition to the closest values of zero of EAM and RQME. The jackfruit leaf area estimate can be determined quickly, accurately and non-destructively by the linear first-order model with LW as the independent variable by equation ELA = 1.07451 + 0.71181(LW).

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 Estimation Leaf Area by Composite Leaves of Canavalia rosea Seedlings Through Linear Dimensions From Last Leaflet

2019 ◽  
Vol 11 (9) ◽  
pp. 299
Author(s):  
Ana Paula Braido Pinheiro ◽  
Vinicius de Souza Oliveira ◽  
Karina Tiemi Hassuda dos Santos ◽  
Jéssica Sayuri Hassuda Santos ◽  
Gleyce Pereira Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Mean Absolute Error ◽  
Linear Equations ◽  
Absolute Error ◽  
Mean Values ◽  
Linear Dimensions ◽  
Power Models ◽  
Independent Variable ◽  
The Mean ◽  
Non Linear Equations

The objective of this work was to propose models of equations from measurements of the linear dimensions of the last leaflet for the estimation of the leaf area of the composite leaves of Canavalia rosea. For this purpose, 441 composite leaves of 198 seedlings were used, 45 days after sowing, produced in nursery and belonging to the Federal University of Espírito Santo, Campus São Mateus, located in the municipality of São Mateus, North of the State of Espírito Santo, Brazil. The length (L) along the main midrib and the maximum leaf width (W) of the last leaflet of each composite leaf, as well as the leaf area of all leaflets, were measured. Subsequently, it was determined the product of the multiplication of the length with the width (LW) and leaf area observed (OLA) from the sum of leaf area of leaflets in front of these measures were adjusted linear and non-linear equations of linear first degree, quadratic and power models, where, OLA was used as a dependent variable in function of L, W and LW as independent variable. Based on the models tested, we obtained equations for the estimated leaf area (ELA). The mean values of ELA and OLA were compared by Student's t test 5% probability. The mean absolute error (MAE), the root mean square error (RMSE) and the Willmott d index, were determined as criteria for validation. The best adjusted equation was chosen through the non-significant values in the comparison of the means of ELA and OLA, values of MAE and RMSE closer to zero, value of the index d near the unitary and higher values of R2. Thus, the leaf area of the composite leaf of C. rosea seedlings can be estimated by the power model represented by equation ELA = 2.2951 (LW)0.9474 quickly, easily and non-destructively.

scholarly journals Mathematical Modeling to Estimation Leaf Area of Pimenta dioica from Linear Dimensions

2019 ◽  
pp. 1-8
Author(s):  
Vinicius De Souza Oliveira ◽  
Lucas Caetano Gonçalves ◽  
Amanda Costa ◽  
Karina Tiemi Hassuda dos Santos ◽  
Jéssica Sayuri Hassuda Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Linear Equations ◽  
Selection Criterion ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Maximum Width ◽  
Independent Variable ◽  
Pimenta Dioica ◽  
Non Destructive

The objective of this work was to obtain regression equations and to indicate the most appropriate from different mathematical models for the estimation of the leaf area of ​​ Allspice (Pimenta dioica) by non - destructive method. 500 leaves of plants located in the municipality of São Mateus, North of Espírito Santo State, Brazil, were collected, 400 of which were used to adjust the equations and 100 for validation. 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 measured from all leaves. We fitted models of linear equations of first degree, quadratic and power, where OLA was the dependent variable in function of L, W and LW. From the 100 sheets intended for validation, and using the adjusted equations for each mathematical model, the estimated leaf area (ELA) was obtained. Subsequently, a simple linear regression was fitted for each model of the proposed equation in which ELA was the dependent variable and OLA the independent variable. The mean absolute error (MAE), the root mean square error (RMSE) and Willmott's index d also determined. The best fit had as selection criterion the non-significance of the comparative means of ELA and OLA, MAE and RMSE values ​​closer to zero and value of the coefficient of determination coefficient (R2) close to one. Thus, the power model (ELA = 0.7605(LW)0.9926, R2 = 0.9764, MAE = 1.0066, RMSE = 1.7759 and d = 0.9950) based on the product of length and width (LW) is the most appropriate for estimating the leaf area of ​​Pimenta dioica.

scholarly journals Determination of the Leaflet Area of Schinus terebinthifolius Raddi in Function of Linear Dimensions

2019 ◽  
Vol 11 (14) ◽  
pp. 198
Author(s):  
Vinicius de Souza Oliveira ◽  
André Monzoli Covre ◽  
Drielly Stephania Gouvea ◽  
Luciano Canal ◽  
Karina Tiemi Hassuda dos Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Absolute Error ◽  
T Test ◽  
Quadratic Model ◽  
Angular Coefficient ◽  
Schinus Terebinthifolius ◽  
Linear Coefficient ◽  
Mean Values ◽  
Mathematical Equations ◽  
The Mean

The objective of this study was to select mathematical equations that best fit the estimation of the leaf area of pink pepper (Schinus terebinthifolius Raddi) from the linear leaflet dimensions. 500 leaflets with different physiological ages of a commercial plantation were collected, located in the region of Gameleira, municipality of São Mateus, North of the State of Espírito Santo, Brazil. Was measured the length (L) along the main midrib, the largest width (W) and the observed leaf area (OLA) of each sheet. The product of the multiplication between L and W of the leaflets (LW) was determined. For the modeling the measurements of 400 leaflets were used, where OLA was used in function of L, W or LW. Based on the models found, we obtained the estimated leaf area (ELA). A simple linear regression was fitted for each proposed model of OLA in function of ELA. We tested the hypotheses H0: β0 = 0 versus Ha: β0 ≠ 0 and H0: β1 = 1 versus Ha: β1 ≠ 1, using Student’s t test at 5% probability. The mean values of ELA and OLA were compared by Student’s t test 5% probability. It was determined the mean error (E), mean absolute error (MAE), root mean square error (RMSE) and Willmott d index. The best adjusted equation was chosen by linear coefficient (β0) not different from zero, angular coefficient (β1) not unlike one, non-significant values of ELA and OLA, E, EAM and RQME closer to zero and Willmott’s index d closer to one. In this way, the leaf area of leaflets of Schinus terebinthifolius Raddi can be estimated by the quadratic model equation ELA = -2.6646 + 2.2124W + 1.3953(W)2 , using only the width of the leaves as a measure.

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

scholarly journals Designing accurate emulators for scientific processes using calibration-driven deep models

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jayaraman J. Thiagarajan ◽  
Bindya Venkatesh ◽  
Rushil Anirudh ◽  
Peer-Timo Bremer ◽  
Jim Gaffney ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Heterogeneous Data ◽  
Small Data ◽  
Machine Learning Methods ◽  
Squared Error ◽  
Noise Structure ◽  
The Mean ◽  
Modern Machine

Abstract Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes.

On the mean squared error, the mean absolute error and the like

1999 ◽  
Vol 28 (8) ◽  
pp. 1813-1822 ◽  
Author(s):  
Shaul K. Bar-Lev ◽  
Benzion Boukai ◽  
Peter Enis
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Squared Error ◽  
The Mean

A Hybrid Recommender Algorithm Based on an Improved Similarity Method

2013 ◽  
Vol 475-476 ◽  
pp. 978-982 ◽  
Author(s):  
Rui Ping Song ◽  
Bo Wang ◽  
Guo Ming Huang ◽  
Qi Dong Liu ◽  
Rong Jing Hu ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Similarity Measures ◽  
Absolute Error ◽  
Squared Error ◽  
Coverage Accuracy ◽  
The Mean ◽  
Recommender Algorithm ◽  
Hybrid Recommender ◽  
Important Measurement ◽  
Similarity Method

Recommendation systems have achieved widespread success in E-commerce nowadays. There are several evaluation metrics for recommender systems, such as accuracy, diversity, computational efficiency and coverage. Accuracy is one of the most important measurement criteria. In this paper, to improve accuracy, we proposed a hybrid recommender algorithm by an improved similarity method (ISM), combining demographic recommendation techniques and user-based collaborative filtering (CF) algorithms. Experiments were performed to compare the present approach with the other classical similarity measures based on the MovieLens dataset. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values show the superiority of the proposed algorithm.

scholarly journals Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6264
Author(s):  
Muammar Sadrawi ◽  
Yin-Tsong Lin ◽  
Chien-Hung Lin ◽  
Bhekumuzi Mathunjwa ◽  
Ho-Tsung Hsin ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Mean Squared Error ◽  
Massachusetts General Hospital ◽  
Venous Pressure ◽  
Absolute Error ◽  
Cerebral Hemodynamics ◽  
Non Invasive ◽  
Arterial Blood ◽  
Convolutional Autoencoder ◽  
The Mean

This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson’s linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system—the systolic blood pressure (SBP) and diastolic blood pressures (DBP)—the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system—the systolic and diastolic pressures—the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.

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