Computer vision by unsupervised machine learning in seed drying process

Pinheiro, Romário de Mesquita; Gadotti, Gizele Ingrid; Bernardy, Ruan; Tim, Rafael Rico; Pinto, Karine Von Ahn; Buck, Graciela

doi:10.1590/1413-7054202347018922

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…The use of some earlier unusual methods, such as images, does not obviously improve energy-related issues, but there are also some indirect implications, in addition to quality checking and process improvement by applying computer vision [74][75][76][77][78][79], such as other machine learning algorithms, e.g., kNN and random forest regression [80].…”

Section: Energy Efficiency Issues Of Dryingmentioning

confidence: 99%

Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković,

Kljajić,

Milivojević

et al. 2023

Energies

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Drying processes are among the most energy-intensive industrial processes. There is a need for development of the efficient methods needed for estimating, measuring, and reducing energy use. Different machine learning algorithms might provide some of the answers to these issues in a faster and less costly way, without the need for time-consuming and expensive experiments done at different scales of the dryers. The aim of this paper was to provide a comprehensive overview of machine learning applications for addressing energy-related challenges by exploration of different energy types and energy reduction opportunities. Also, the analysis of the applied algorithms, their specific applications and a critical evaluation of the obtained results are provided. The paper is based on the necessity of the improvements in energy use needed for drying related on the existing data. The overview of the ways for such achievements, and a general classification of machine learning algorithm are the background of the paper. The methods used are the machine learning techniques employed in different energy-related issues for drying processes. The paper focuses on the applications of artificial neural networks and other machine learning algorithms and models for different energy-related issues, including different energy types applications, challenges associated with energy consumption, and opportunities for energy reduction. Not only the applied algorithms, but also their specific applications, and the statistical analysis of the obtained results are also overviewed. Finally, a critical evaluation of the findings highlighting the potentials of machine learning algorithms in addressing energy-related challenges (such as estimation of energy consumption, opportunities for energy reduction, and use of different energy sources) is provided. The presented analysis underscored the effectiveness of machine learning applications for these purposes.

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