scholarly journals Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards

Agronomy ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 427
Author(s):  
Ana Belén González-Fernández ◽  
Enoc Sanz-Ablanedo ◽  
Víctor Marcelo Gabella ◽  
Marta García-Fernández ◽  
José Ramón Rodríguez-Pérez
Keyword(s):  
Mean Squared Error ◽  
Water Status ◽  
Vegetation Indices ◽  
Least Squares Regression ◽  
Squared Error ◽  
Difficult Time ◽  
Grape Quality ◽  
Regression Error ◽  
The Impact ◽  
Non Destructive

Water status controls plant physiology and is key to managing vineyard grape quality and yield. Water status is usually estimated by leaf water potential (LWP), which is measured using a pressure chamber; however, this method is difficult, time-consuming, and error-prone. While traditional spectral methods based on leaf reflectance are faster and non-destructive, most are based on vegetation indices derived from satellite imagery (and so only take into account discrete bandwidths) and do not take full advantage of modern hyperspectral sensors that capture spectral reflectance for thousands of wavelengths. We used partial least squares regression (PLSR) to predict LWP from reflectance values (wavelength 350–2500 nm) captured with a field spectroradiometer. We first identified wavelength ranges that minimized regression error. We then tested several common data pre-processing methods to analyze the impact on PLSR prediction precision, finding that derivative pre-processing increased the determination coefficients of our models and reduced root mean squared error (RMSE). The models fitted with raw data obtained their best results at around 1450 nm, while the models with derivative pre-processed achieved their best estimates at 826 nm and 1520 nm.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

scholarly journals Automated Bale Mapping Using Machine Learning and Photogrammetry

2021 ◽  
Vol 13 (22) ◽  
pp. 4675
Author(s):  
William Yamada ◽  
Wei Zhao ◽  
Matthew Digman
Keyword(s):  
Lower Cost ◽  
Mean Squared Error ◽  
Spatial Clustering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Data Set ◽  
Map Projection ◽  
Squared Error ◽  
Aerial Vehicle ◽  
The Impact

An automatic method of obtaining geographic coordinates of bales using monovision un-crewed aerial vehicle imagery was developed utilizing a data set of 300 images with a 20-megapixel resolution containing a total of 783 labeled bales of corn stover and soybean stubble. The relative performance of image processing with Otsu’s segmentation, you only look once version three (YOLOv3), and region-based convolutional neural networks was assessed. As a result, the best option in terms of accuracy and speed was determined to be YOLOv3, with 80% precision, 99% recall, 89% F1 score, 97% mean average precision, and a 0.38 s inference time. Next, the impact of using lower-cost cameras was evaluated by reducing image quality to one megapixel. The lower-resolution images resulted in decreased performance, with 79% precision, 97% recall, 88% F1 score, 96% mean average precision, and 0.40 s inference time. Finally, the output of the YOLOv3 trained model, density-based spatial clustering, photogrammetry, and map projection were utilized to predict the geocoordinates of the bales with a root mean squared error of 2.41 m.

scholarly journals Effect of unfavourable population structure on estimates of heritability, systematic effects and breeding values

2008 ◽  
Vol 51 (6) ◽  
pp. 601-610
Author(s):  
A. P. Kominakis
Keyword(s):  
Population Structure ◽  
Mean Squared Error ◽  
Heritability Estimate ◽  
Breeding Values ◽  
Parameter Estimations ◽  
Squared Error ◽  
Heritability Estimation ◽  
Population Structures ◽  
Systematic Effects ◽  
The Impact

Abstract. Empirical estimations of heritability, systematic effects and predictions of sires’ breeding values (BVs) were obtained under various population structures for simulated populations consisted of n = 400 animals in 5 herds for a trait of medium heritability (h2 = 0.30). An infinitesimal additive genetic animal model was assumed while simulating data. Population structure was varied to allow for good and poor connectedness across herds and (non)random association between the genetic and the environmental effects. The impact of the various population structures on the parameter estimation(s) was assessed using Mean Squared Error (MSE) and Pearson’s correlations. Allowing sires to have progenies in more than one herd (good herd connectedness) and random use of sires across herds generally resulted in good parameter estimations. Poor connectedness significantly affected herd effects estimation and BV prediction but not heritability estimation as long as random usage of sires across environments was guaranteed. Selective use of the best sires in the best herds along with poor connectedness resulted in poorest estimations of all parameters examined. In the latter case, heritability was seriously underestimated (h2 = 0.06) while highest error, lowest accuracies for the BVs and a remarkable underestimation of the genetic gain were observed. Use of reference sires on a natural mating basis to create genetic links between herds has served a good solution for both heritability and BVs estimation under unfavorable structure. Mating 0.25 of the herd ewes with reference sires resulted in a heritability estimate close to the simulated one. Significantly better estimates of systematic effects and BVs were, however, obtained when 0.5 of the herd ewes were mated by reference sires.

THE IMPROVED BPNN-NAR AND BPNN-NARMA MODELS ON MALAYSIAN AGGREGATE COST INDICES WITH OUTLYING DATA

2016 ◽  
Vol 78 (12-3) ◽  
Author(s):  
Saadi Ahmad Kamaruddin ◽  
Nor Azura Md Ghani ◽  
Norazan Mohamed Ramli
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Squared Error ◽  
The Impact ◽  
Nonlinear Autoregressive

Neurocomputing have been adapted in time series forecasting arena, but the presence of outliers that usually occur in data time series may be harmful to the data network training. This is because the ability to automatically find out any patterns without prior assumptions and loss of generality. In theory, the most common training algorithm for Backpropagation algorithms leans on reducing ordinary least squares estimator (OLS) or more specifically, the mean squared error (MSE). However, this algorithm is not fully robust when outliers exist in training data, and it will lead to false forecast future value. Therefore, in this paper, we present a new algorithm that manipulate algorithms firefly on least median squares estimator (FFA-LMedS) for  Backpropagation neural network nonlinear autoregressive (BPNN-NAR) and Backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) models to reduce the impact of outliers in time series data. The performances of the proposed enhanced models with comparison to the existing enhanced models using M-estimators, Iterative LMedS (ILMedS) and Particle Swarm Optimization on LMedS (PSO-LMedS) are done based on root mean squared error (RMSE) values which is the main highlight of this paper. In the meanwhile, the real-industrial monthly data of Malaysian Aggregate cost indices data set from January 1980 to December 2012 (base year 1980=100) with different degree of outliers problem is adapted in this research. At the end of this paper, it was found that the enhanced BPNN-NARMA models using M-estimators, ILMedS and FFA-LMedS performed very well with RMSE values almost zero errors. It is expected that the findings would assist the respected authorities involve in Malaysian construction projects to overcome cost overruns.

scholarly journals Development of Diameter Growth Models by Thinning Intensity of Planted Quercus glauca Thunb. Stands

2021 ◽  
Vol 24 (6) ◽  
pp. 629-638
Author(s):  
Su Young Jung ◽  
Kwang Soo Lee ◽  
Hyun Soo Kim
Keyword(s):  
Mean Squared Error ◽  
Growth Models ◽  
Diameter Growth ◽  
Least Squares Regression ◽  
Permanent Sample Plots ◽  
Thinning Intensity ◽  
Squared Error ◽  
Quercus Glauca ◽  
The Mean ◽  
Diameter Growth Models

Background and objective: This study was conducted to develop diameter growth models for thinned Quercus glauca Thunb. (QGT) stands to inform production goals for treatment and provide the information necessary for the systematic management of this stands.Methods: This study was conducted on QGT stands, of which initial thinning was completed in 2013 to develop a treatment system. To analyze the tree growth and trait response for each thinning treatment, forestry surveys were conducted in 2014 and 2021, and a one-way analysis of variance (ANOVA) was executed. In addition, non-linear least squares regression of the PROC NLIN procedure was used to develop an optimal diameter growth model.Results: Based on growth and trait analyses, the height and height-to-diameter (H/D) ratio were not different according to treatment plot (p > .05). For the diameter of basal height (DBH), the heavy thinning (HT) treatment plot was significantly larger than the control plot (p < .05). As a result of the development of diameter growth models by treatment plot, the mean squared error (MSE) of the Gompertz polymorphic equation (control: 2.2381, light thinning: 0.8478, and heavy thinning: 0.8679) was the lowest in all treatment plots, and the Shapiro-Wilk statistic was found to follow a normal distribution (p > .95), so it was selected as an equation fit for the diameter growth model.Conclusion: The findings of this study provide basic data for the systematic management of Quercus glauca Thunb. stands. It is necessary to construct permanent sample plots (PSP) that consider stand status, location conditions, and climatic environments.

Rates of Expansions for Functional Estimators

2021 ◽  
Author(s):  
Yulia Kotlyarova ◽  
Marcia M. A. Schafgans ◽  
Victoria Zinde-Walsh
Keyword(s):  
Convergence Rates ◽  
Mean Squared Error ◽  
Model Averaging ◽  
Finite Sample ◽  
Squared Error ◽  
Semiparametric Estimators ◽  
Asymptotic Mean Squared Error ◽  
The Impact ◽  
Nonconvex Model

AbstractIn this paper, we summarize results on convergence rates of various kernel based non- and semiparametric estimators, focusing on the impact of insufficient distributional smoothness, possibly unknown smoothness and even non-existence of density. In the presence of a possible lack of smoothness and the uncertainty about smoothness, methods of safeguarding against this uncertainty are surveyed with emphasis on nonconvex model averaging. This approach can be implemented via a combined estimator that selects weights based on minimizing the asymptotic mean squared error. In order to evaluate the finite sample performance of these and similar estimators we argue that it is important to account for possible lack of smoothness.

Rapid and Early Detection of Salmonella Serotypes with Hyperspectral Microscopy and Multivariate Data Analysis†

2015 ◽  
Vol 78 (4) ◽  
pp. 668-674 ◽  
Author(s):  
MATTHEW EADY ◽  
BOSOON PARK ◽  
SUN CHOI
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Pearson Correlation ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Microscope Imaging ◽  
Salmonella Serotypes

This study was designed to evaluate hyperspectral microscope images for early and rapid detection of Salmonella serotypes Enteritidis, Heidelberg, Infantis, Kentucky, and Typhimurium at incubation times of 6, 8, 10, 12, and 24 h. Images were collected by an acousto-optical tunable filter hyperspectral microscope imaging system with a metal halide light source measuring 89 contiguous wavelengths every 4 nm between 450 and 800 nm. Pearson correlation values were calculated for incubation times of 8, 10, and 12 h and compared with data for 24 h to evaluate the change in spectral signatures from bacterial cells over time. Regions of interest were analyzed at 30% of the pixels in an average cell size. Spectral data were preprocessed by applying a global data transformation algorithm and then subjected to principal component analysis (PCA). The Mahalanobis distance was calculated from PCA score plots for analyzing serotype cluster separation. Partial least-squares regression was applied for calibration and validation of the model, and soft independent modeling of class analogy was utilized to classify serotype clusters in the training set. Pearson correlation values indicate very similar spectral patterns for reduced incubation times ranging from 0.9869 to 0.9990. PCA score plots indicated cluster separation at all incubation times, with incubation time Mahalanobis distances of 2.146 to 27.071. Partial least-squares regression had a maximum root mean squared error of calibration of 0.0025 and a root mean squared error of validation of 0.0030. Soft independent modeling of class analogy correctly classified values at 8 h (98.32%), 10 h (96.67%), 12 h (88.33%), and 24 h (98.67%) with the optimal number of principal components (four or five). The results of this study suggest that Salmonella serotypes can be classified by applying a PCA to hyperspectral microscope imaging data from samples after only 8 h of incubation.

scholarly journals The impact of studies with no events in both arms on meta-analysis of rare events: a simulation study using generalized linear mixed model

2021 ◽  
Author(s):  
Chang Xu ◽  
Lifeng Lin
Keyword(s):  
Effect Size ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Mean Squared Error ◽  
Meta Analysis ◽  
Unbiased Estimation ◽  
Double Zero ◽  
Squared Error ◽  
Meta Analyses ◽  
The Impact

AbstractObjectiveThe common approach to meta-analysis with double-zero studies is to remove such studies. Our previous work has confirmed that exclusion of these studies may impact the results. In this study, we undertook extensive simulations to investigate how the results of meta-analyses would be impacted in relation to the proportion of such studies.MethodsTwo standard generalized linear mixed models (GLMMs) were employed for the meta-analysis. The statistical properties of the two GLMMs were first examined in terms of percentage bias, mean squared error, and coverage. We then repeated all the meta-analyses after excluding double-zero studies. Direction of estimated effects and p-values for including against excluding double-zero studies were compared in nine ascending groups classified by the proportion of double-zero studies within a meta-analysis.ResultsBased on 50,000 simulated meta-analyses, the two GLMMs almost achieved unbiased estimation and reasonable coverage in most of the situations. When excluding double-zero studies, 0.00% to 4.47% of the meta-analyses changed the direction of effect size, and 0.61% to 8.78% changed direction of the significance of p-value. When the proportion of double-zero studies increased in a meta-analysis, the probability of the effect size changed the direction increased; when the proportion was about 40% to 60%, it has the largest impact on the change of p-values.ConclusionDouble-zero studies can impact the results of meta-analysis and excluding them may be problematic. The impact of such studies on meta-analysis varies by the proportion of such studies within a meta-analysis.

scholarly journals Patient Text Feedback Based Optimized Deep Learned Model to Identify the Impact of Therapy

2019 ◽  
Vol 8 (12) ◽  
pp. 1960-1967
Keyword(s):  
Opinion Mining ◽  
Mean Squared Error ◽  
Method Comparison ◽  
Machine Learning Algorithms ◽  
Whole Process ◽  
Squared Error ◽  
N Gram ◽  
The Impact ◽  
Deep Learning Model ◽  
User Content

Text classification or Text mining is a very demanding field because the content created by the user in natural language is not easily understandable. It becomes very important to systematically identify and extract subjective information from user content so that it can be easily understandable. The whole process is done by assigning a particular class to text. In the field of opinion mining, most of the work has been done in common areas like restaurants, electronic goods, movie feedback, etc. and a lot of work needs to be done in the area of healthcare and medical. So, the proposed work has been carried over healthcare. The aim of this study is to classify the text feedback of patient using optimized deep learning model to identify the impact of therapy. In proposed method comparison of CNN with machine learning algorithms has been done, in which, CNN gave better results in terms of accuracy (99.98%), precision (0.981), recall (0.981), mean squared error (0.282). Further, we have implemented the CNN with N-gram technique and found that this method improved the results of CNN based on precision (0.999), recall (0.999), mean squared error (0.001), area under curve (0.998) but accuracy remained the same.

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