کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
83974 158856 2016 5 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
A feasibility cachaca type recognition using computer vision and pattern recognition
ترجمه فارسی عنوان
شناخت امکان سنجی نوع cachaca با استفاده از بینایی کامپیوتر و تشخیص الگو
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تولید محتوا
با 10 درصد تخفیف ویژه دانشیاری
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• The problem of recognition of aging time and wood type in chacaca is presented.
• A new approach is introduced using a computer vision system.
• The developed image capture device and information processing method is presented.
• Results show that the new technique is cheaper and better than previous approaches.

Brazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1–3 years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3 years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 123, April 2016, Pages 410–414
نویسندگان
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