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Mr. Gilson Helfer
Universidade de Santa Cruz do Sul

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Research Keywords & Expertise

0 Chemometrics
0 Computer Vision
0 Machine Learning
0 Mobile Devices
0 IoT

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Machine Learning
IoT

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Short Biography

Undergraduated in Industrial Chemistry (1998) and Master in Systems and Industrial Processes (2014) at the University of Santa Cruz do Sul. Also Undergraduated in Analysis and Systems Development (2015) at Lutheran University of Brazil. Currently doing a doctorate in Applied Computing at the University of Vale dos Sinos (UNISINOS) and lecturer at the University of Santa Cruz do Sul (Computer Science courses). Also doing a research in mobile computing and IoT applied to chemistry, chemometrics, image analysis with machine learning.

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Journal article
Published: 26 July 2021 in Food Chemistry
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Parallel data analysis was investigated to improve performance in variable selection and to develop predictive models for beer quality control. A set of spectral near infrared (NIR) data from 60 beer samples and its primitive extracts as the original concentration was used. The dataset was distributed to Raspberry Pi 3 Model B devices connected to a network that was running a Machine Learning service. With more than 4 devices acting in parallel, it was possible to reduce time in 57% to find the best linear regression coefficient (0.999) with the lower RMSECV (0.216) if compared to a singular desktop computer. Thus, parallel processing can significantly reduce the time to indicate the best model fitted during the variable’s selection.

ACS Style

Gilson Augusto Helfer; Jorge Luis Victória Barbosa; Eduardo Hermes; Brunno José Fagundes; Roberta Oliveira Santos; Adilson Ben da Costa. The application of parallel processing in the selection of spectral variables in beer quality control. Food Chemistry 2021, 367, 130681 .

AMA Style

Gilson Augusto Helfer, Jorge Luis Victória Barbosa, Eduardo Hermes, Brunno José Fagundes, Roberta Oliveira Santos, Adilson Ben da Costa. The application of parallel processing in the selection of spectral variables in beer quality control. Food Chemistry. 2021; 367 ():130681.

Chicago/Turabian Style

Gilson Augusto Helfer; Jorge Luis Victória Barbosa; Eduardo Hermes; Brunno José Fagundes; Roberta Oliveira Santos; Adilson Ben da Costa. 2021. "The application of parallel processing in the selection of spectral variables in beer quality control." Food Chemistry 367, no. : 130681.

Journal article
Published: 25 June 2021 in Journal of Sensor and Actuator Networks
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The present work proposed a low-cost portable device as an enabling technology for agriculture using multispectral imaging and machine learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed a good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

ACS Style

Gilson Helfer; Jorge Barbosa; Douglas Alves; Adilson da Costa; Marko Beko; Valderi Leithardt. Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology. Journal of Sensor and Actuator Networks 2021, 10, 40 .

AMA Style

Gilson Helfer, Jorge Barbosa, Douglas Alves, Adilson da Costa, Marko Beko, Valderi Leithardt. Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology. Journal of Sensor and Actuator Networks. 2021; 10 (3):40.

Chicago/Turabian Style

Gilson Helfer; Jorge Barbosa; Douglas Alves; Adilson da Costa; Marko Beko; Valderi Leithardt. 2021. "Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction Technology." Journal of Sensor and Actuator Networks 10, no. 3: 40.

Preprint
Published: 26 May 2021
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The present work proposed a low-cost portable device as an enabling technology for Smart Farms using multispectral imaging and Machine Learning in soil texture. Clay is an important factor for the verification and monitoring of soil use due to its fast reaction to chemical and surface changes. The system developed uses the analysis of reflectance in wavebands for clay prediction. The selection of each wavelength is performed through an LED lamp panel. A NoIR microcamera controlled by a Raspberry Pi device is employed to acquire the image and unfold it in RGB histograms. Results showed an good prediction performance with R2 of 0.96, RMSEC of 3.66% and RMSECV of 16.87%. The high portability allows the equipment to be used in a field providing strategic information related to soil sciences.

ACS Style

Gilson Augusto Helfer; Jorge Luis Victoria Barbosa; Douglas Alves; Adilson Ben da Costa; Marko Beko; Valderi Reis Quietinho Leithardt. Multispectral Cameras and Machine Learning integrated into Portable Devices as Enabling Technology for Smart Farms. 2021, 1 .

AMA Style

Gilson Augusto Helfer, Jorge Luis Victoria Barbosa, Douglas Alves, Adilson Ben da Costa, Marko Beko, Valderi Reis Quietinho Leithardt. Multispectral Cameras and Machine Learning integrated into Portable Devices as Enabling Technology for Smart Farms. . 2021; ():1.

Chicago/Turabian Style

Gilson Augusto Helfer; Jorge Luis Victoria Barbosa; Douglas Alves; Adilson Ben da Costa; Marko Beko; Valderi Reis Quietinho Leithardt. 2021. "Multispectral Cameras and Machine Learning integrated into Portable Devices as Enabling Technology for Smart Farms." , no. : 1.

Journal article
Published: 08 April 2021 in Revista Jovens Pesquisadores
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Com o aumento da demanda por métodos que seguem os princípios da química analítica verde e devido a necessidade de maior produtividade das áreas cultivadas, novas técnicas de análise em tecido vegetal têm sido estudadas. Ultimamente, o uso da espectroscopia molecular vem se destacando, principalmente por apresentar resposta rápida em diversos tipos de análises, além de apresentar gastos significativamente menores em relação a outros métodos convencionais de laboratório. Assim, o presente estudo tem o objetivo de avaliar o uso da espectroscopia no infravermelho próximo aliado a métodos quimiométricos, para análise de nitrogênio em tecido vegetal. Para tanto, espectros de infravermelho na faixa de 960-2.400 nm de diversas espécies, foram obtidos a partir de uma mesa auto-amostradora. O equipamento produz um espectro de varredura com área de superfície de 4 cm2, na região entre 450-2450 nm, com capacidade de 40 amostras e frequência analítica da ordem 6 amostras por minuto. Os modelos de calibração foram realizados utilizando o software SOLO+MIA (Eigenvector Research, Inc), 8.6.1, com diferentes técnicas de pré-tratamentos dos espectros. Os resultados de concentração de nitrogênio não diferiram estatisticamente (p = 0,987) daqueles obtidos pelo método oficial. O modelo desenvolvido apresentou os seguintes erros, RMSEC 1,25 g kg-1; RMSECV 2,22 g kg-1 e R2 cal 0,986. Deste modo, observa-se que os resultados foram satisfatórios, legitimando que é possível o uso de espectroscopia NIR para estes tipos de análise.

ACS Style

Luiza Baumann; Eduarda Ferreira Pessel; Cristiane Pappis; Letícia Ana Fischborn; Marcia Librelotto; Roberta Oliveira Santos; Ronaldo Bastos dos Santos; Gilson Augusto Helfer; Adilson Ben da Costa. SISTEMA AUTOMATIZADO PARA ANÁLISE DIRETA DE TECIDO VEGETAL POR ESPECTROSCOPIA MOLECULAR. Revista Jovens Pesquisadores 2021, 10, 44 -52.

AMA Style

Luiza Baumann, Eduarda Ferreira Pessel, Cristiane Pappis, Letícia Ana Fischborn, Marcia Librelotto, Roberta Oliveira Santos, Ronaldo Bastos dos Santos, Gilson Augusto Helfer, Adilson Ben da Costa. SISTEMA AUTOMATIZADO PARA ANÁLISE DIRETA DE TECIDO VEGETAL POR ESPECTROSCOPIA MOLECULAR. Revista Jovens Pesquisadores. 2021; 10 (2):44-52.

Chicago/Turabian Style

Luiza Baumann; Eduarda Ferreira Pessel; Cristiane Pappis; Letícia Ana Fischborn; Marcia Librelotto; Roberta Oliveira Santos; Ronaldo Bastos dos Santos; Gilson Augusto Helfer; Adilson Ben da Costa. 2021. "SISTEMA AUTOMATIZADO PARA ANÁLISE DIRETA DE TECIDO VEGETAL POR ESPECTROSCOPIA MOLECULAR." Revista Jovens Pesquisadores 10, no. 2: 44-52.

Journal article
Published: 26 February 2021 in Sensors
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The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

ACS Style

Bruno Martini; Gilson Helfer; Jorge Barbosa; Regina Espinosa Modolo; Marcio da Silva; Rodrigo de Figueiredo; André Mendes; Luís Silva; Valderi Leithardt. IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories. Sensors 2021, 21, 1631 .

AMA Style

Bruno Martini, Gilson Helfer, Jorge Barbosa, Regina Espinosa Modolo, Marcio da Silva, Rodrigo de Figueiredo, André Mendes, Luís Silva, Valderi Leithardt. IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories. Sensors. 2021; 21 (5):1631.

Chicago/Turabian Style

Bruno Martini; Gilson Helfer; Jorge Barbosa; Regina Espinosa Modolo; Marcio da Silva; Rodrigo de Figueiredo; André Mendes; Luís Silva; Valderi Leithardt. 2021. "IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories." Sensors 21, no. 5: 1631.

Journal article
Published: 01 January 2021 in Journal of the Brazilian Chemical Society
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ACS Style

Adilson Da Costa; Gilson Helfer; Jorge Barbosa; Iberê Teixeira; Roberta Santos; Ronaldo Dos Santos; Mônica Voss; Sandra Schlessner; Juliano Barin. PhotoMetrix UVC: A New Smartphone-Based Device for Digital Image Colorimetric Analysis Using PLS Regression. Journal of the Brazilian Chemical Society 2021, 1 .

AMA Style

Adilson Da Costa, Gilson Helfer, Jorge Barbosa, Iberê Teixeira, Roberta Santos, Ronaldo Dos Santos, Mônica Voss, Sandra Schlessner, Juliano Barin. PhotoMetrix UVC: A New Smartphone-Based Device for Digital Image Colorimetric Analysis Using PLS Regression. Journal of the Brazilian Chemical Society. 2021; ():1.

Chicago/Turabian Style

Adilson Da Costa; Gilson Helfer; Jorge Barbosa; Iberê Teixeira; Roberta Santos; Ronaldo Dos Santos; Mônica Voss; Sandra Schlessner; Juliano Barin. 2021. "PhotoMetrix UVC: A New Smartphone-Based Device for Digital Image Colorimetric Analysis Using PLS Regression." Journal of the Brazilian Chemical Society , no. : 1.

Special issue research article
Published: 21 September 2020 in Journal of Chemometrics
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This study aims to develop, evaluate, and optimize the application of near‐infrared spectroscopy (NIR) autosampler device equipped with a fiber‐optic probe associated with chemometric methods for the fast quantification of routine chemical constituents such as total alkaloids, reducing sugars, nitrate, and ammonia in tobacco. For this purpose, an NIR autosampler device with capacity for 40 samples was used. The spectra were collected in the range of 950 to 2,500 nm, with three spins on the sample, a 3.0‐cm2 sample scanning area, and 2.0 ± 0.2 mm distance between the fiber‐optic probe and the sample. Four partial least‐squares (PLS) models were developed, and different preprocessing methods were investigated. The predicted results were compared with those obtained from the reference method (continuous‐flow analysis), and root mean square error of prediction values of 0.31%, 1.27%, 0.47%, and 0.026% were obtained for total alkaloids, reducing sugars, nitrate, and ammonia, respectively. The proposed method performed well for the analysis of total alkaloids and reducing sugars with an appropriate goodness‐of‐fit and fair precision. In conclusion, considering the performance of the regression models and the associated environmental and economic advantages, the application of NIR spectrometer autosampler device equipped with a fiber‐optic probe associated with a PLS and synergy interval PLS algorithms cannot replace the reference method, but it is a promising tool for tobacco monitoring.

ACS Style

Gabrielle Fernanda Zimmer; Roberta Oliveira Santos; Iberê Damé Teixeira; Rosana De Cassia De Souza Schneider; Gilson Augusto Helfer; Adilson Ben Da Costa. Rapid quantification of constituents in tobacco by NIR fiber‐optic probe. Journal of Chemometrics 2020, 34, 1 .

AMA Style

Gabrielle Fernanda Zimmer, Roberta Oliveira Santos, Iberê Damé Teixeira, Rosana De Cassia De Souza Schneider, Gilson Augusto Helfer, Adilson Ben Da Costa. Rapid quantification of constituents in tobacco by NIR fiber‐optic probe. Journal of Chemometrics. 2020; 34 (12):1.

Chicago/Turabian Style

Gabrielle Fernanda Zimmer; Roberta Oliveira Santos; Iberê Damé Teixeira; Rosana De Cassia De Souza Schneider; Gilson Augusto Helfer; Adilson Ben Da Costa. 2020. "Rapid quantification of constituents in tobacco by NIR fiber‐optic probe." Journal of Chemometrics 34, no. 12: 1.

Journal article
Published: 11 July 2020 in Computers and Electronics in Agriculture
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The application of sophisticated sensors to measure soil composition and plant needs are a tendence in precision agriculture. In any case, prediction models are built using machine learning algorithms. The goal is to make farming more efficient and productive with minimal impact on the environment. The present article proposes an architectural model that evaluates the soil’s fertility and productivity through context history with Partial Least Squares Regression. Also productivity prediction of a wheat planted area was performed using climatic events between the years of 2001 and 2015 resulting a mean square error of calibration (RMSEC) of 0.20 T/ha, mean square errors of cross-validation of 0.54 T/ha with a Pearson coefficient (R2) of 0.9189. For the prediction of organic matter and clay, the best results obtained were a R2 of 0.9345, RMSECV of 0.54% and R2 of 0.9239, RMSECV of 5.28%, respectively.

ACS Style

Gilson Augusto Helfer; Jorge Luis Victória Barbosa; Ronaldo dos Santos; Adilson Ben da Costa. A computational model for soil fertility prediction in ubiquitous agriculture. Computers and Electronics in Agriculture 2020, 175, 105602 .

AMA Style

Gilson Augusto Helfer, Jorge Luis Victória Barbosa, Ronaldo dos Santos, Adilson Ben da Costa. A computational model for soil fertility prediction in ubiquitous agriculture. Computers and Electronics in Agriculture. 2020; 175 ():105602.

Chicago/Turabian Style

Gilson Augusto Helfer; Jorge Luis Victória Barbosa; Ronaldo dos Santos; Adilson Ben da Costa. 2020. "A computational model for soil fertility prediction in ubiquitous agriculture." Computers and Electronics in Agriculture 175, no. : 105602.

Journal article
Published: 02 April 2019 in Águas Subterrâneas
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A ingestão frequente de água contendo concentração de flúor acima do limite estabelecido pela legislação brasileira causa uma doença chamada fluorose dentária. Atualmente, o monitoramento do nível de flúor na água para consumo humano é realizado utilizando métodos potenciométricos, cromatográficos ou colorimétricos, que são relativamente caros e utilizam equipamentos de difícil deslocamento para análises de campo. Portanto, surge a necessidade de desenvolver métodos alternativos, visando rapidez, praticidade, mobilidade e redução do custo das análises. O aplicativo para smartphone PhotoMetrix® é um software livre de análise de imagens digitais utilizando modelos matemáticos univariados e multivariados. A captura de imagens pode ser feita através do próprio smartphone ou com uma câmera USB externa. Assim, o objetivo deste estudo foi avaliar o uso do PhotoMetrix® no monitoramento da concentração de flúor em águas de abastecimento. Dois métodos de referência foram utilizados para comparação dos resultados: o método espectrofotométrico e o eletrodo de íon seletivos. Os resultados indicaram concordância entre os métodos, mostrando um coeficiente de determinação (R2) de 0,995 e erro de predição RMSEP (Root Mean Square Error of Prediction) de aproximadamente 0,05 mg L-1. Então, o PhotoMetrix® é um método satisfatório para a determinação de flúor, podendo se tornar uma alternativa viável para o monitoramento da qualidade da água, especialmente para sistemas alternativos de abastecimento.

ACS Style

Luiza Baumann; Marcia Librelotto; Cristiane Pappis; Ronaldo Bastos Dos Santos; Roberta Oliveira Santos; Gilson Augusto Helfer; Eduardo Alexis Lobo; Adilson Ben Da Costa. Uso do aplicativo PhotoMetrix no monitoramento da concentração de flúor em sistemas alternativos de abastecimento de água. Águas Subterrâneas 2019, 33, 1 .

AMA Style

Luiza Baumann, Marcia Librelotto, Cristiane Pappis, Ronaldo Bastos Dos Santos, Roberta Oliveira Santos, Gilson Augusto Helfer, Eduardo Alexis Lobo, Adilson Ben Da Costa. Uso do aplicativo PhotoMetrix no monitoramento da concentração de flúor em sistemas alternativos de abastecimento de água. Águas Subterrâneas. 2019; 33 (2):1.

Chicago/Turabian Style

Luiza Baumann; Marcia Librelotto; Cristiane Pappis; Ronaldo Bastos Dos Santos; Roberta Oliveira Santos; Gilson Augusto Helfer; Eduardo Alexis Lobo; Adilson Ben Da Costa. 2019. "Uso do aplicativo PhotoMetrix no monitoramento da concentração de flúor em sistemas alternativos de abastecimento de água." Águas Subterrâneas 33, no. 2: 1.

Journal article
Published: 01 October 2016 in Electrochimica Acta
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We herein report a potential point-of-use platform for multivariate analyses by presenting homemade potentiostat and smartphone. This system combined high-performance detection (linear sweep, cyclic, and square wave voltammetry) with great simplicity, low-cost, portability, autonomy (6 h), and cable-free (wireless communication) device. In addition, the smartphone showed ability for processing complex and multivariate data. To the best of our knowledge, this paper is the first reporting about the development of a totally integrated point-of-use system with chemometric data processing on smartphone. This feature is essential for in-situ assays by allowing the real-time accomplishment of the entire analytical measurement at remote places. Such ability further reduces the occurrence of model overfitting by non-expert users. As proof-of-concept, our system was successfully applied to fingerprint Brazilian honey samples according to their botanical and geographic origins. The method relied on the unsupervised technique of principal component analysis (PCA) and the assays were performed by cyclic voltammetry using a single and non-modified working electrode of gold.

ACS Style

Gabriela Giordano; Marcia B.R. Vicentini; Rui C. Murer; Fabio Augusto; Marco Ferrão; Gilson A. Helfer; Adilson Ben da Costa; Angelo L. Gobbi; Leandro Hantao; Renato S. Lima. Point-of-use electroanalytical platform based on homemade potentiostat and smartphone for multivariate data processing. Electrochimica Acta 2016, 219, 170 -177.

AMA Style

Gabriela Giordano, Marcia B.R. Vicentini, Rui C. Murer, Fabio Augusto, Marco Ferrão, Gilson A. Helfer, Adilson Ben da Costa, Angelo L. Gobbi, Leandro Hantao, Renato S. Lima. Point-of-use electroanalytical platform based on homemade potentiostat and smartphone for multivariate data processing. Electrochimica Acta. 2016; 219 ():170-177.

Chicago/Turabian Style

Gabriela Giordano; Marcia B.R. Vicentini; Rui C. Murer; Fabio Augusto; Marco Ferrão; Gilson A. Helfer; Adilson Ben da Costa; Angelo L. Gobbi; Leandro Hantao; Renato S. Lima. 2016. "Point-of-use electroanalytical platform based on homemade potentiostat and smartphone for multivariate data processing." Electrochimica Acta 219, no. : 170-177.