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| Moscetti, Roberto; Massaro, Simone; Monarca, Danilo; Cecchini, Massimo; Massantini, Riccardo. |
| Smart drying is one of the newest and most promising techniques. It is a multi- and inter-disciplinary sector which has potential to guarantee high value end-products by implementing innovative and reliable sensors, resources, tools and practices. Its recent developments embrace various R&D areas, such as computer vision (CV) and deep learning, which deal with allowing computers to understand digital images and videos better than humans. Conventional machine-learning techniques suffer several limitations, mainly due to their inability to process raw data. In fact, in the last few decades, machine learning required considerable domain expertise to mine raw data and extract features from which an algorithm could identify patterns in the input. Deep... |
| Tipo: Conference paper, poster, etc. |
Palavras-chave: Food security; Food quality and human health Processing; Packaging and transportation. |
| Ano: 2019 |
URL: http://orgprints.org/36556/1/Abstract2_full_text_v4.pdf |
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| Moscetti, Roberto; Massaro, Simone; Chillemi, Giovanni; Sanna, Nico; Sturm, Barbara; Nallan Chakravatula, Swathi Sirisha; Massantini, Riccardo. |
| Deep learning was tested for its feasibility as CV tool for the analysis of inlet wet food into the drying process. In detail, convolutional neural networks (CNNs) were successfully applied for addressing the following tasks: (i) the semantic image segmentation of the inlet product; (ii) the inlet product classification for automatic selection of drying parameters. As a result, CNNs have been shown to be used for the development of smart dryers able to monitor and control the process. |
| Tipo: Conference paper, poster, etc. |
Palavras-chave: Food security; Food quality and human health Processing; Packaging and transportation. |
| Ano: 2019 |
URL: http://orgprints.org/36552/1/Eurodrying2019_full_paper_167_revision.pdf |
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