Potential of using statistical quality control in agriculture 4.0

Silva, Rouverson Pereira da; Santos, Adão Felipe dos; Oliveira, Bruno Rocca de; Souza, Jarlyson Brunno Costa; Oliveira, Danilo Tedesco de; Carneiro, Franciele Morlin

doi:10.5935/1806-6690.20200105

Cited by 7 publications

(3 citation statements)

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“…In this context, the systematic review by Ho et al highlights how augmented reality can be a powerful tool to enhance quality control activities, integrating real-time data and interactive visualization to improve the precision and efficiency of inspections [1]. Similarly, Silva et al explore the use of statistical quality control in agriculture, adapting traditional techniques to modern agricultural production systems, demonstrating the versatility and adaptability required in Quality 4.0 approaches [2].…”

Section: Introduction 1quality Controlmentioning

confidence: 99%

Real-Time Quality Inspection through Industry 4.0 Technologies

Anchundia,

Berrones,

Verdugo

et al. 2024

Preprint

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The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.

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Section: Introduction 1quality Controlmentioning

confidence: 99%

Real-Time Quality Inspection through Industry 4.0 Technologies

Anchundia,

Berrones,

Verdugo

et al. 2024

Preprint

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“…The experimental data were directly transformed into formulas through various interpolation procedures. Since experimental research has taken place on a large scale, statistics have been increasingly used for the processing of experimental data, as shown in Da Silva et al (2020) or Gomez and Gomez (1984), referring to modern agriculture. Having as an objective the rationalisation of fuel consumption, the authors Mamkagh(2018) and Singh et al (2018) carried out some review of the main influencing factors: forward speed, tractor ballast, and tyre pressure.…”

Section: Introductionmentioning

confidence: 99%

Superior Inferential Statistics of the Experimental Data of a Complex Experimental Cultivator

CARDEI,

CONSTANTIN,

PERSU

et al. 2023

INMATEH

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The aim of the research is to highlight some statistical tools that favour extracting the components of the dynamic process that are dependent on the forward speed of some agricultural aggregates. The main objectives are: (I) identification of a minimum number of components in a multitude of random variables, with the help of which the other random variables can be calculated, and the application of this result to the strain gauge measurements; (II) establishing the connection between the synthetic results that partially solve the first objective and the forward speed of the agricultural aggregate. The second objective is used to obtain indications in search of the parameters’ dependencies on the forward speed of the aggregate. The first objective seeks to determine a group of three signals from the twelve, with the help of which the best multivariate linear interpolation is obtained for the other nine signals, which in physical terms means the reduction to a quarter of the measurement points and of the strain sensors used. A result associated with the first objective refers to the estimation of information loss due to the limited number of deformation sensors mounted on the tested structure. The article also presents attempts to use the results of the theory of neural networks and statistical interaction. In order to capitalise on the experimental data in this complex statistical framework, it is necessary to monitor at least the working speed (not only the average speed per experiment), fuel consumption, working depth (continuously monitored), soil moisture etc.

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“…In the conditions in which the experimental research took a large scale, statistics was more and more involved in the process of extracting the essences from the experimental data, the authors emphasize, Da Silva et al, (2020) or Gomez & Gomez, (1976), referring to modern agriculture. Intending to rationalize fuel consumption, Mamkagh A.M., (2018) reviews the main influencing factors: forward speed, tractor weight, and tire pressure.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Dependence of Traction Resistance Force on Forward Speed for Tractor-Cultivator Aggregates

CÂRDEI,

CONSTANTIN,

PERSU

et al. 2023

INMATEH

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The article presents results generated by research on the influence of forward speed on the traction resistance force for soil processing machines. Statistical estimators are proposed to highlight the intensity of the connection between the traction resistance force and the forward speed and an optimal evaluation method of an exponent of the work speed to explain as well as possible the behaviour of the traction resistance force. It is found that the variance and the standard deviation are the statistical estimators that highlight the most intensively the connection between the traction resistance force and the forward speed.

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Potential of using statistical quality control in agriculture 4.0

Cited by 7 publications

References 0 publications

Real-Time Quality Inspection through Industry 4.0 Technologies

Real-Time Quality Inspection through Industry 4.0 Technologies

Superior Inferential Statistics of the Experimental Data of a Complex Experimental Cultivator

Analysis of the Dependence of Traction Resistance Force on Forward Speed for Tractor-Cultivator Aggregates

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