کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6539281 | 1421096 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
SeeFruits: Design and evaluation of a cloud-based ultra-portable NIRS system for sweet cherry quality detection
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کلمات کلیدی
TSSIoTNIRSDMDRTOSWANtitratable acidity - اسیدیته قابل تیتراسیونInternet of Things - اینترنت چیزها یا اینترنت اشیاءCloud computing - رایانش ابریmaturity level - سطح بلوغSOC - سیستم روی یک تراشهLocal area network - شبکه محلیWide area network - شبکه گستردهLAn - لنNear Infrared spectroscopy - نزدیک به طیف سنجی مادون قرمزtotal soluble solids - کل مواد جامد محلول
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: SeeFruits: Design and evaluation of a cloud-based ultra-portable NIRS system for sweet cherry quality detection SeeFruits: Design and evaluation of a cloud-based ultra-portable NIRS system for sweet cherry quality detection](/preview/png/6539281.png)
چکیده انگلیسی
Recent researches have shown that spectroscopy is a valid non-destructive technique for fruit quality detection. Yet the high cost, large volume, and complicated operation of the traditional spectral system makes it hard to be adapted to real field applications. In this paper, a low-cost, cloud-based portable Near Infrared (NIR) system called 'SeeFruits' was designed for fruit quality detection. The system was developed based on two integrated modules, DLP® NIRscan Nano EVM and ESP12-F. Main structures of hardware and software as well as the operation and workflow of the system were described in detail. A total of 240 sweet cherries were chosen as our fruit samples in order to evaluate the performance of 'SeeFruits'. By targeting maturity level as a qualitative index and total soluble solids content as a quantitative index, we compared the results between 'SeeFruits' and a benchtop NIR-hyperspectral imaging system. The 'SeeFruits' system achieved F1-score of 0.89 on qualitative task and R2 of 0.83 on quantitative task. Overall, with the features of ultra-portability, cloud computing and Internet of things feasibility, 'SeeFruits' can provide a fast, flexible and friendly application for sweet cherry quality detection to nonprofessionals with satisfactory accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 152, September 2018, Pages 302-313
Journal: Computers and Electronics in Agriculture - Volume 152, September 2018, Pages 302-313
نویسندگان
Tao Wang, Jian Chen, Yangyang Fan, Zhengjun Qiu, Yong He,