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Recognition of inlet wet food in drying process through a deep learning approach

Moscetti, Roberto; Massaro, Simone; Monarca, Danilo; Cecchini, Massimo and Massantini, Riccardo (2019) Recognition of inlet wet food in drying process through a deep learning approach. Lecture at: 6th International Symposium on Modelling in Horticulture Supply Chain, Molfetta (Italy), 9-12 June 2019. [Submitted]

[thumbnail of Abstract2_full_text_v4.pdf] PDF - Accepted Version - English
[thumbnail of Model-IT_v2.pdf] PDF - Presentation - English


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 learning is a novel subfield of machine learning, which embraces methods that allow to discover patterns for detection or classification purposes by using raw data. Consequently, CV in combination with deep learning has the potential to be a powerful Process Analytical Technology tool useful for enhancing the understanding and control of critical process parameters that impact on quality of the final product.
Deep learning was tested for its feasibility as CV tool for the analysis of inlet wet food to drying process. In details, convolutional neural networks (CNNs) were successfully applied for addressing the following tasks: (i) the semantic image segmentation of the inlet product (i.e., recognition between background and product pixels); (ii) the inlet product classification through its segmented image; (iii) the automated selection of optimal settings of drying process parameters.
Results obtained not only represent a step forward in the development of smart dryers able to recognise the inlet wet product, and to set the proper process parameters on its own or as decision support system, but also lay the foundation for further researches on using a computer vision system as PAT tool for smart drying processes.

EPrint Type:Conference paper, poster, etc.
Type of presentation:Lecture
Keywords:Python, Jupyter, artificial intelligence, machine learning, convolutional neural networks, SusOrgPlus
Subjects: Food systems > Food security, food quality and human health
Food systems > Processing, packaging and transportation
Research affiliation: European Union > CORE Organic Cofund > SusOrgPlus
Italy > Univ. Tuscia
Horizon Europe or H2020 Grant Agreement Number:727495
Related Links:https://projects.au.dk/coreorganiccofund/core-organic-cofund-projects/susorgplus/, https://www.susorgplus.eu/
Deposited By: Moscetti, Ass. Prof. Roberto
ID Code:36556
Deposited On:12 Nov 2019 09:33
Last Modified:29 Jan 2020 11:11
Document Language:English
Refereed:Peer-reviewed and accepted

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