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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…mehr

Produktbeschreibung
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.
Autorenporträt
Hui 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 analysis. He also lectures and supervises doctor and master degree students; and has to his credit several publications and awards.