An Investigation into Electrical Treatment Methods for the Processing of Tobacco Leaves

Armyanov, N.; Diprose, M. F.; Stefanova, S.; Stoyanova, T.; Dimitrova, St.K.

doi:10.1006/jaer.2000.0548

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…In (Armyanov, 2006b) it was established that while an ark discharge occurred the energy consumption during of the PEF treatment of tobacco stems significantly increased. According to (Armyanov and Russeva, 1992;Armyanov et al, 2000), the effect of the treatment was significantly reduced. This is why the occurrence of an ark discharge has to be avoided during the treatment.…”

Section: Determination Of the Parameter Of The Elements Of The Electrmentioning

confidence: 97%

Longitudinal pulsed electric field equipment for tobacco leaves: Methodology for calculating parameters for an electric circuit

Armyanov¹,

Gachovska²

2008

2008 Providence, Rhode Island, June 29 - July 2, 2008

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Drying of tobacco leaves and stems is a high energy intensive process. Pulsed electric field treatment can be applied to decrease significant energy requirements. An analysis of the electrical circuits of the equipment for pulsed electric field treatment of plants has been accomplished and an electric circuit for longitudinal PEF equipment to treat tobacco leaves and stems with a buffer capacitor and a rectifier for high voltage has been proposed. A method for determination of the electric circuit parameters and for power supply to the equipment is given. Results of the calculation of the two electrical circuits are also presented.

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Section: Determination Of the Parameter Of The Elements Of The Electrmentioning

confidence: 97%

Longitudinal pulsed electric field equipment for tobacco leaves: Methodology for calculating parameters for an electric circuit

Armyanov¹,

Gachovska²

2008

2008 Providence, Rhode Island, June 29 - July 2, 2008

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“…Over the last five decades, weed control technology development has focused primarily on herbicides; however, evaluation of alternative weed control technologies has continued, albeit at a relatively slower pace. Many novel thermal technologies have been identified as potential alternatives to herbicides, including targeted lasers (Coleman et al 2021;Couch and Gangstad 1974;Mathiassen et al 2006), electrical discharge (Armyanov et al 2000;Diprose and Benson 1984), and microwaves (Brodie et al 2012;Sartorato et al 2006), as reviewed in Bauer et al (2020). Compared with herbicides, the use of these alternatives as whole-field treatments in large-scale cropping systems has not been viable given the intensive resource demands (energy, labor, and time).…”

Section: Introductionmentioning

confidence: 99%

Weed detection to weed recognition: reviewing 50 years of research to identify constraints and opportunities for large-scale cropping systems

Coleman

Bender

et al. 2022

Weed Technol

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The past 50 years of advances in weed recognition technologies have poised site-specific weed control (SSWC) on the cusp of requisite performance for large-scale production systems. The technology offers improved management of diverse weed morphology over highly variable background environments. SSWC enables the use of non-selective weed control options, such as lasers and electrical weeding, as feasible in-crop selective alternatives to herbicides by targeting individual weeds. This review looks at the progress made over this half-century of research and its implications for future weed recognition and control efforts; summarizing advances in computer vision techniques and the most recent deep convolutional neural network (CNN) approaches to weed recognition. The first use of CNNs for plant identification in 2015 began an era of rapid improvement in algorithm performance on larger and more diverse datasets. These performance gains and subsequent research have shown that the variability of large-scale cropping systems is best managed by deep learning for in-crop weed recognition. The benefits of deep learning and improved accessibility to open-source software and hardware tools has been evident in the adoption of these tools by weed researchers and the increased popularity of CNN-based weed recognition research. The field of machine learning holds substantial promise for weed control, especially the implementation of truly integrated weed management strategies. While previous approaches sought to reduce environmental variability or manage it with advanced algorithms, research in deep learning architectures suggests that large-scale, multi-modal approaches are the future for weed recognition.

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Influences of cold atmospheric plasma pretreatment on drying kinetics, structural, fractional and chemical characteristics of tobacco leaves

2022

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An Investigation into Electrical Treatment Methods for the Processing of Tobacco Leaves

Cited by 3 publications

References 2 publications

Longitudinal pulsed electric field equipment for tobacco leaves: Methodology for calculating parameters for an electric circuit

Longitudinal pulsed electric field equipment for tobacco leaves: Methodology for calculating parameters for an electric circuit

Weed detection to weed recognition: reviewing 50 years of research to identify constraints and opportunities for large-scale cropping systems

Influences of cold atmospheric plasma pretreatment on drying kinetics, structural, fractional and chemical characteristics of tobacco leaves

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