Intelligent tobacco flue-curing method based on leaf texture feature analysis

Wang, Lutao; Cheng, Bei; Li, Zhengzhou; Liu, Tianmei; Li, Jianing

doi:10.1016/j.ijleo.2017.09.088

Cited by 16 publications

(7 citation statements)

References 17 publications

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“…Interactions between the storage, method of curing, water activity, potency, chemical profile, and maturity of cannabis in this postharvest step are still not widely investigated. Color and texture changes of tobacco are determinants for curing [ 121 ] and a similar method to detect the more subtle changes in color, aroma, and texture in cannabis curing might be worth looking into.…”

Section: Postharvest Operations Involved In Cannabismentioning

confidence: 99%

Postharvest Operations of Cannabis and Their Effect on Cannabinoid Content: A Review

et al. 2022

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In recent years, cannabis (Cannabis sativa L.) has been legalized by many countries for production, processing, and use considering its tremendous medical and industrial applications. Cannabis contains more than a hundred biomolecules (cannabinoids) which have the potentiality to cure different chronic diseases. After harvesting, cannabis undergoes different postharvest operations including drying, curing, storage, etc. Presently, the cannabis industry relies on different traditional postharvest operations, which may result in an inconsistent quality of products. In this review, we aimed to describe the biosynthesis process of major cannabinoids, postharvest operations used by the cannabis industry, and the consequences of postharvest operations on the cannabinoid profile. As drying is the most important post-harvest operation of cannabis, the attributes associated with drying (water activity, equilibrium moisture content, sorption isotherms, etc.) and the significance of novel pre-treatments (microwave heating, cold plasma, ultrasound, pulse electric, irradiation, etc.) for improvement of the process are thoroughly discussed. Additionally, other operations, such as trimming, curing, packaging and storage, are discussed, and the effect of the different postharvest operations on the cannabinoid yield is summarized. A critical investigation of the factors involved in each postharvest operation is indeed key for obtaining quality products and for the sustainable development of the cannabis industry.

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Section: Postharvest Operations Involved In Cannabismentioning

confidence: 99%

Postharvest Operations of Cannabis and Their Effect on Cannabinoid Content: A Review

et al. 2022

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“…In the research on the intelligent control of the bulk tobacco flue-curing process, artificial neural network-based approaches were commonly used [6,7]. Wang and Qin [8] built a state prediction fusion model integrating long-short term memory (LSTM) and extreme gradient boosting (XGBoost) to predict the state of the tobacco curing, and make timely adjustments to the curing process.…”

Section: Related Workmentioning

confidence: 99%

Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method

Yang

2021

IEEE Access

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The research of the bulk tobacco flue-curing process is helpful to model the intelligent bulk curing system, which is designed to implement the curing procedure without manual operation. An intelligent bulk curing method based on the convolutional neural network (CNN) named TobaccoNet was proposed, which could set the target dry-bulb temperature (TD) and the target wet-bulb temperature (TW) of the bulk curing barn according to the tobacco leaves image. The performance of the TobaccoNet is compared with the traditional manual image feature extraction method, the stacked-sparse-autoencoder (SSAE)-based deep learning method, and the other two methods applied in related references. The test results show that TobaccoNet outperforms the comparison methods in predicting TD and TW. Specifically, the correlation coefficient reaches 0.9965 and 0.9683, the mean relative error is 1.62% and 1.77%, and the root mean squared error achieves 1.061 °C and 0.8581 °C respectively. The promising results demonstrate that the TobaccoNet is effective and reliable for modeling the intelligent bulk tobacco flue-curing process. The influence of different CNN structures on the prediction accuracy of TD and TW was analyzed. From the perspective of the computational complexity and the prediction performance, the proposed sequential CNN structure is more suitable for analyzing bulk tobacco curing in this study.INDEX TERMS Convolutional neural network, bulk tobacco flue-curing, tobacco leaves, deep learning.

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“…The target DBT curve is a standard curve designed by tobacco technological curers according to the conditions of fresh tobacco leaves [37]. An ideal curing process for tobacco leaves is achieved when the actual DBT controlled in the loading chamber is completely consistent with the target DBT [38].…”

Section: Comparison Between Conventional and Biomass Curing Techniquesmentioning

confidence: 99%

Performance of an Intelligent Biomass Fuel Burner as an Alternative to Coal-Fired Heating for Tobacco Curing

He¹,

Wei²,

Ma³

et al. 2020

Pol. J. Environ. Stud.

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In recent years, excessive carbon dioxide (CO 2) emissions have already led to a series of environmental problems. As the largest flue-cured/Virginia tobacco (Nicotiana tobacum L.) producer in the world, China currently grows about one-third of the world's tobacco annually [1], and tobacco curing (TC) is the most energy-intensive phase of tobacco production [2]. Traditionally, coal is used to directly burn and heat fluecured tobacco in a bulk curing barn [3, 4]. During the annual TC season, millions of tons of CO 2 and fly dust are emitted into the atmosphere from the chimneys, which causes serious environmental pollution [5, 6];

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Intelligent tobacco flue-curing method based on leaf texture feature analysis

Cited by 16 publications

References 17 publications

Postharvest Operations of Cannabis and Their Effect on Cannabinoid Content: A Review

Postharvest Operations of Cannabis and Their Effect on Cannabinoid Content: A Review

Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method

Performance of an Intelligent Biomass Fuel Burner as an Alternative to Coal-Fired Heating for Tobacco Curing

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