Monitoring of mechanical sugarcane harvesting through control charts

Voltarelli, Murilo Aparecido; Silva, Rouverson Pereira da; Zerbato, Cristiano; Paixão, Carla Segatto Strini; Tavares, Tiago de Oliveira

doi:10.1590/1809-4430-eng.agric.v35n6p1079-1092/2015

Cited by 9 publications

(13 citation statements)

References 6 publications

(2 reference statements)

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“…Cunha et al (2014) observed that the control charts were effective tools in detecting the total losses of the industrial tomato crop, because it was detected that the process was out of control and that the adoption of corrections in the process would provide a better harvesting efficiency (Cunha et al 2014). Voltarelli et al (2015) used the control charts as an indicator of quality in the monitoring of losses in mechanized harvesting of sugarcane in the Triângulo Mineiro region, in the State of Minas Gerais, Brazil. The control charts for individual values and for exponentially weighted moving averages were used.…”

Section: Introductionmentioning

confidence: 99%

Control chart based on residues: Is a good methodology to detect outliers?

et al. 2019

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The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models.

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Section: Introductionmentioning

confidence: 99%

Control chart based on residues: Is a good methodology to detect outliers?

et al. 2019

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“…If no point is highlighted in the control chart, it is considered that there are no special causes of variation and, consequently, the process will be considered as "stable" or "under statistical control". Voltarelli et al (2015) report that, regardless of the assumption of data normality, the use of control charts of individual values is adequate for the monitoring and analysis of the process, provided that the analyst has deep knowledge on the analyzed quality indicators.…”

Section: Methodsmentioning

confidence: 99%

Physical and physiological quality of soybean seeds at three speeds of the harvester

Paixão

Chrispin

Silva

et al. 2017

Rev. bras. eng. agríc. ambient.

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A B S T R A C TThe mechanized harvesting of soybean is a fundamental tool in the production process of this crop and, if not performed properly, it can result in severe mechanical damage to the seeds, causing significant losses at harvest, particularly due to reduced quality. Thus, this study aimed to evaluate the physical and physiological quality of soybean seeds at three speeds of the harvester, using statistical process control. The experiment was carried out in the municipality of Conceição das Alagoas-MG, Brazil, with three travel speeds: 4.5, 5.0 and 5.5 km h -1 . The seed samples were collected at 20 min intervals for 3 h of operation, with a total of 9 samples for each speed. The evaluations were performed based on the following parameters: grain temperature, water content, concave opening, cylinder rotation, mechanical damage and electrical conductivity. The physical and physiological quality of the seeds was dependent on the displacement speed of machine. The highest speed led to lower mechanical damage and higher vigor of soybean seeds.Qualidade física e fisiológica das sementes de soja em três velocidades da colhedora R E S U M O A colheita mecanizada de soja é fundamental no processo produtivo desta cultura e, caso não seja realizada adequadamente, poderá resultar em danos mecânicos severos às sementes, acarretando prejuízos significativos na colheita, particularmente devido à redução da qualidade. Neste sentido se objetivou, neste trabalho, avaliar a qualidade física e fisiológica das sementes de soja em três velocidades da colhedora, por meio do controle estatístico de processo. O experimento foi realizado em área agrícola, no município de Conceição das Alagoas, MG, com três velocidades de deslocamento da colhedora (4,5; 5,0 e 5,5 km h -1 ). As amostras de sementes foram coletadas em intervalos de 20 min durante 3 h de operação, totalizando nove amostras para cada velocidade. As avaliações foram realizadas a partir dos seguintes parâmetros: temperatura das sementes, teor de água, abertura do côncavo, rotação do cilindro, danos mecânicos e condutividade elétrica. A qualidade física e fisiológica das sementes foi influenciada pela velocidade de deslocamento da máquina. A maior velocidade apresentou menor valor de dano mecânico e maior vigor das sementes de soja. Key words:Glycine max (L.) Merrill mechanical damage vigor control charts Palavras-chave:Glycine max (L.) Merrill dano mecânico vigor cartas de controle

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“…Regardless of the assumption of normality, these control chart models can be used to analyze and detect the intrinsic and extrinsic variability to the mechanized agricultural processes since the full knowledge of the process is essential for managing the operation (Voltarelli et al, 2015b).…”

Section: Methodsmentioning

confidence: 99%

Monitoring of Soybean Mechanical Harvesting as a Function of Plot Shapes

et al. 2017

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Information on harvester performance is important in the management and optimization of mechanized agricultural systems. In this sense, the aim of this study was to monitor the operation quality of soybean mechanical harvesting in different plot shapes by means of statistical process control. The harvest was carried out in an agricultural area of a farm located in Uberaba, MG, Brazil, with an experimental design based on the assumptions of statistical quality control and 18, 28, and 24 replications for irregular, rectangular, and trapezoidal plots, respectively. The variables and/or quality indicators of harvester performance were forward speed, engine speed, cylinder speed, and concave opening. The lowest variation occurred in the irregular plot for the quality indicators forward speed and engine speed of harvester. Engine speed and the concave opening of harvester were unstable for all plot shapes. Only the cylinder speed of the soybean harvester presented process stability during operation for the three plot shapes.

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Monitoring of mechanical sugarcane harvesting through control charts

Cited by 9 publications

References 6 publications

Control chart based on residues: Is a good methodology to detect outliers?

Control chart based on residues: Is a good methodology to detect outliers?

Physical and physiological quality of soybean seeds at three speeds of the harvester

Monitoring of Soybean Mechanical Harvesting as a Function of Plot Shapes

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