Standardization of food smoking production within the framework of environmental engineering

Abstract

Currently, there is a fairly large number of methods and systems of production management that are successfully applied all over the world. Their use in the modern enterprise management is due to the fact that in its daily activities it must ensure not only the production of high-quality and safe products, but also take into account the environmental aspects of production, ensure the safety of workers, and look for ways to improve their production activities. This situation contributes to the convergence and unification of controls among different management systems. The development of a methodological approach to the standardization of production activities based on the integration of the requirements of general management systems, which is the basis for documenting the elements of these systems at the enterprise, undoubtedly corresponds to modern trends in production management.

Locating Defects and Image Preprocessing: Deep Learning in Automated Tobacco Production

“…In the field of computer speech fast recognition algorithms, Jérémy et al advocated the use of computer deep machine learning algorithm technical results to improve the algorithmic accuracy of computer speech fast recognition methods [5]. The use of unsupervised learning models and deep learning, not only to learn image data features from image data models that can be unlabeled but also to establish parallelized image models that can be massively parallelized, points out another new direction to explore for the systematic in-depth analysis and research development of deep machine learning algorithms [6]. Among them, the most advanced representative image network target recognition residual network model, whose recognition performance, at present, has significantly exceeded the human cognitive ability.…”

Research for Rural Development 2022 : annual 28th International scientific conference proceedings

“…To assess these different traits, different ground-based HTPPs have been developed, equipped with one or most commonly several combined spectral sensors. The most widely used of which are visible spectrum or RGB (red, green, blue) cameras (Deery et al, 2021), light detection and ranging or LIDAR (Deery et al, 2021;Lin, 2015), multispectral or hyperspectral cameras and thermal cameras (Bai et al, 2016;Kim et al, 2021).…”

“…One of the most widely used phenotyping tools in the breeding programmes of cereal varieties are RGB image-based phenotyping platforms (Zhang & Zhang, 2018), which are typically used as a base sensor in combination with other types of additional sensors (Kim et al, 2021). The main advantages of using RGB cameras compared to spectral sensors are their relative ease of use, low acquisition and maintenance costs (Kim et al, 2021;Prey, von Bloh, & Schmidhalter, 2018). Multispectral and hyperspectral cameras, on the other hand, can provide higher image resolution, but their costs are considerably higher than for RGB cameras (Morgounov et al, 2014).…”

“…Multispectral and hyperspectral cameras, on the other hand, can provide higher image resolution, but their costs are considerably higher than for RGB cameras (Morgounov et al, 2014). Hyperspectral cameras can detect various stress symptoms in plants, including early drought stress symptoms that cannot be assessed visually (Kim et al, 2021).…”

“…More accurate LAI measurements can be obtained with optical hand-held instruments, but it is labour and time consuming (González-Sanpedro et al, 2008;Nie et al, 2016). Ground-based HTPPs such as RGB cameras (Kim et al, 2021) and LIDAR or spectral sensors for NDVI detection can be used (Deery et al, 2014).…”