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ارزیابی معماریهای مختلف شبکۀ یادگیری عمیق در تشخیص تازگی تخممرغ بر اساس سیگنالهای صدا | ||
| تحقیقات مهندسی صنایع غذایی | ||
| دوره 20، شماره 1 - شماره پیاپی 70، شهریور 1400، صفحه 183-194 اصل مقاله (844.99 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/fooder.2020.341923.1258 | ||
| نویسندگان | ||
| مجید لشگری* 1؛ حامد توکلی2؛ رضا محمدی گل3؛ ولی اله لطفی4 | ||
| 1گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی و محیط زیست، دانشگاه اراک | ||
| 2استادیار دانشگاه اراک | ||
| 3دانشکده کشاورزی. دانشگاه اراک | ||
| 4گروه مهندسی مکانیک بیوسیستم | ||
| چکیده | ||
| امنیت مواد غذایی که به طور مستقیم با سلامت افراد جامعه در ارتباط است همواره مورد توجه تمامی کشورها بوده است. با توجه به اینکه تخممرغ در بسیاری از صنایع غذایی مصرف می شود و در برنامه غذایی روزانه بسیاری از مردم نیز قرار دارد، تشخیص تازگی آن نیز اهمیت بالایی خواهد داشت . در این تحقیق، قابلیت سامانه آکوستیک به عنوان روشی غیرمخرب برای تشخیص تازگی تخم مرغ بررسی شده است. نمونه ها در دمای محیط به مدت 1، 4، 7، 10، 13 و 16 روز نگهداری شدند. پس از دادهبرداری، تمامی سیگنالهای صدا با استفاده از طیف اسپکتروگرام به تصویر تبدیل شدند. در این تحقیق با استفاده از آزمون مخرب و با در نظر گرفتن دو معیار واحدِ هاو و ارتفاع کیسۀ هوا، میزان تازگی نمونه ها ارزیابی شد. نتایج آزمون مخرب نشان داد که تمامی نمونه های مربوط به روزهای 16و 13 و همچنین 80 درصد نمونه های مربوط به روز 10 با افت کیفیت همراه بودهاند که از نظر معیار درجه بندی، جزو گروه غیرتازه و به عبارتی بی کیفیت به شمار می آیند. بنابراین، نمونه ها به دو گروه تازه (روزهای 1، 4 و 7) و غیرتازه (روزهای 10، 13 و 16) تقسیم شدند. از چهار شبکۀ یادگیری عمیق از پیش آموزش دیده AlexNet، VGGNet، GoogLeNet و ResNet در این تحقیق استفاده شد. در بین این شبکه ها، شبکۀ ResNet با میانگین دقت طبقه بندی 71.5 درصد بهترین دقت را داشته است. | ||
| کلیدواژهها | ||
| آکوستیک؛ تازگی؛ شبکه عصبی کانولوشن؛ طبقه بندی | ||
| عنوان مقاله [English] | ||
| Evaluation of Different Deep Learning Network Architectures in Egg Freshness Detection Based On Sound Signals | ||
| چکیده [English] | ||
| Food security, which is directly related to the health of people, has always been a concern of all nations. Eggs are consumed in many food industries and are in the daily diet of many people, so detection their freshness is very important. In this study, the capability of the acoustic system as a non-destructive method for egg freshness detection was investigated. Samples were stored at room temperature for 1, 4, 7, 10, 13 and 16 days. After data collection, all audio signals were converted to images, using spectrogram. In this study, the freshness of samples was evaluated using two criteria; Haugh unit and air cell height as a destructive test. The results of destructive test showed that all samples stored for 16 and 13 days and also 80% of samples stored for 10 days faced with quality lossaes during storage. According to grading criteria, these samples were considered as unfresh eggs. Therefore, the samples were divided into two groups: fresh eggs (stored for 1, 4 and 7 days) and unfresh (stored for 10, 13 and 16 days). Four pre-trained deep learning networks AlexNet, VGGNet, GoogLeNet and ResNet were used in this study among which ResNet had the best classification accuracy with an average of 71.5%. | ||
| کلیدواژهها [English] | ||
| acoustic, freshness, convolutional neural network, classification | ||
| مراجع | ||
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