Multi-agent approach for developing a digital twin of wheat

Skobelev, Petr; Laryukhin, Vladimir; Simonova, Elena; Goryanin, Oleg Ivanovich; Yalovenko, Vladimir; Yalovenko, Olga

doi:10.1109/smartcomp50058.2020.00062

Cited by 26 publications

(10 citation statements)

References 3 publications

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“…The major proportion of digital twin applications could be found in the production stage, often referred to as agricultural applications (54.90%). Many applications focus on the growth of plants [ 36 , 42 , 53 , 54 , 61 , 62 , 63 , 64 ] or monitoring the condition of animals [ 23 , 61 , 64 , 65 , 66 ]. Further, entire production systems as greenhouses or fields are twinned [ 34 , 43 , 44 , 61 , 62 , 67 , 68 , 69 , 70 , 71 ].…”

Section: Resultsmentioning

confidence: 99%

“…In this category, many use cases focus on the control of food processing [ 6 , 45 , 46 , 72 ]. Other applications regard the design and personalization of food [ 77 , 79 ], or the twinning of a wheat plant [ 54 ]. All digital twins are based on statistical methods using means and standard deviations for conclusions and predictions.…”

Section: Resultsmentioning

confidence: 99%

“…Recollective digital twins, that maintain the complete history of physical objects (even if those do not longer exist), can be found in all stages of the FSC (31.37%). Some applications use the stored information for learning and improving the system [ 34 , 53 , 54 , 61 , 62 , 63 , 68 , 78 , 80 ]. Other applications were implemented to better document the processes and quality parameters of the physical objects [ 6 , 30 , 47 , 57 , 74 , 76 , 79 ].…”

Section: Resultsmentioning

confidence: 99%

“…Further, the originally published digital twin might be sufficient for some applications. In particular, those publications were from Skobelev et al [ 36 , 53 , 54 ] concerning (wheat) plants; from Defraeye et al [ 8 , 55 ], Shoji et al [ 14 ], and Tagliavini et al [ 56 ] regarding fruits; and from Bottani et al [ 30 ] and Vignali and Bottani [ 57 ] relating to a pasteurizer.…”

Section: Methodsmentioning

confidence: 99%

“… [ 46 ] Processing plan (chocolate bars) Processing X X X X n.a. [ 47 ] Fruits and vegetables Distribution/Retail X X X X X [ 53 ] Plant Production X X X X X X [ 54 ] Wheat plant Production X X X …”

Section: Table A1mentioning

confidence: 99%

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Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

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Piedrahita

et al. 2021

Sensors

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The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.

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