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Sinopsis
The deep learning approach combined with spectroscopic sensing techniques has shown great potential for quality evaluation of food and agro-products. Current advances in deep learning-based qualitative analysis include variety identification, geographical origin detection, adulteration recognition, and bruise detection, whereas quantitative analysis includes multiple component content prediction for fruits, grains, and crops. The main advantage of deep learning approach is the decreasing the dependence on human domain knowledge by end-to-end analysis and the improved precision and generalizability. This book discusses the current challenges of conventional chemometric methods and the emerging deep learning approach for spectral analysis. The research on exploring the learning mechanism of the 'black box' deep learning model is discussed. This book focuses on the application of deep learning approaches on quality evaluation of food and agro-products, lessons from current studies, and future perspectives.
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Application of Deep Learning in Spectral Analysis (Paperback)
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LAP Lambert Academic Publishing, 2024
ISBN 10: 6208223814
ISBN 13: 9786208223816
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Paperback. Condición: new. Paperback. The deep learning approach combined with spectroscopic sensing techniques has shown great potential for quality evaluation of food and agro-products. Current advances in deep learning-based qualitative analysis include variety identification, geographical origin detection, adulteration recognition, and bruise detection, whereas quantitative analysis includes multiple component content prediction for fruits, grains, and crops. The main advantage of deep learning approach is the decreasing the dependence on human domain knowledge by end-to-end analysis and the improved precision and generalizability. This book discusses the current challenges of conventional chemometric methods and the emerging deep learning approach for spectral analysis. The research on exploring the learning mechanism of the 'black box' deep learning model is discussed. This book focuses on the application of deep learning approaches on quality evaluation of food and agro-products, lessons from current studies, and future perspectives. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9786208223816
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Application of Deep Learning in Spectral Analysis
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LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6208223814
ISBN 13: 9786208223816
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Application of Deep Learning in Spectral Analysis
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ISBN 10: 6208223814
ISBN 13: 9786208223816
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Application of Deep Learning in Spectral Analysis (Paperback)
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LAP Lambert Academic Publishing, 2024
ISBN 10: 6208223814
ISBN 13: 9786208223816
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Paperback. Condición: new. Paperback. The deep learning approach combined with spectroscopic sensing techniques has shown great potential for quality evaluation of food and agro-products. Current advances in deep learning-based qualitative analysis include variety identification, geographical origin detection, adulteration recognition, and bruise detection, whereas quantitative analysis includes multiple component content prediction for fruits, grains, and crops. The main advantage of deep learning approach is the decreasing the dependence on human domain knowledge by end-to-end analysis and the improved precision and generalizability. This book discusses the current challenges of conventional chemometric methods and the emerging deep learning approach for spectral analysis. The research on exploring the learning mechanism of the 'black box' deep learning model is discussed. This book focuses on the application of deep learning approaches on quality evaluation of food and agro-products, lessons from current studies, and future perspectives. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9786208223816
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Application of Deep Learning in Spectral Analysis
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LAP LAMBERT Academic Publishing Nov 2024, 2024
ISBN 10: 6208223814
ISBN 13: 9786208223816
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Application of Deep Learning in Spectral Analysis
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Jiang HuiHui Jiang is a full professor in Jiangsu University and holds a PhD in Control Science and Engineering from the same university. His area of research includes the fabrication of olfactory and optical sensors for food analysi. Nº de ref. del artículo: 1959908231
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Application of Deep Learning in Spectral Analysis (Paperback)
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LAP Lambert Academic Publishing, 2024
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ISBN 13: 9786208223816
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Paperback. Condición: new. Paperback. The deep learning approach combined with spectroscopic sensing techniques has shown great potential for quality evaluation of food and agro-products. Current advances in deep learning-based qualitative analysis include variety identification, geographical origin detection, adulteration recognition, and bruise detection, whereas quantitative analysis includes multiple component content prediction for fruits, grains, and crops. The main advantage of deep learning approach is the decreasing the dependence on human domain knowledge by end-to-end analysis and the improved precision and generalizability. This book discusses the current challenges of conventional chemometric methods and the emerging deep learning approach for spectral analysis. The research on exploring the learning mechanism of the 'black box' deep learning model is discussed. This book focuses on the application of deep learning approaches on quality evaluation of food and agro-products, lessons from current studies, and future perspectives. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9786208223816
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Application of Deep Learning in Spectral Analysis
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ISBN 10: 6208223814
ISBN 13: 9786208223816
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Taschenbuch. Condición: Neu. Neuware -The deep learning approach combined with spectroscopic sensing techniques has shown great potential for quality evaluation of food and agro-products. Current advances in deep learning-based qualitative analysis include variety identification, geographical origin detection, adulteration recognition, and bruise detection, whereas quantitative analysis includes multiple component content prediction for fruits, grains, and crops. The main advantage of deep learning approach is the decreasing the dependence on human domain knowledge by end-to-end analysis and the improved precision and generalizability. This book discusses the current challenges of conventional chemometric methods and the emerging deep learning approach for spectral analysis. The research on exploring the learning mechanism of the 'black box' deep learning model is discussed. This book focuses on the application of deep learning approaches on quality evaluation of food and agro-products, lessons from current studies, and future perspectives.Books on Demand GmbH, Überseering 33, 22297 Hamburg 284 pp. Englisch. Nº de ref. del artículo: 9786208223816
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Application of Deep Learning in Spectral Analysis
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ISBN 10: 6208223814
ISBN 13: 9786208223816
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Application of Deep Learning in Spectral Analysis
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LAP LAMBERT Academic Publishing, 2024
ISBN 10: 6208223814
ISBN 13: 9786208223816
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