این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Agricultural Science and Technology، جلد ۲۵، شماره ۳، صفحات ۵۹۷-۶۰۸
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Apricot Position Determination Using Deep Learning for Apricot Stone Extraction Machine
چکیده انگلیسی مقاله Despite the developing technology, extraction of Sulfured Dried Apricot (Prunus armeniaca) (SDA) stones is still done manually and thus requires a significant amount of labor and time and also causes serious problems in terms of hygiene. According to International Food Standards (CXS 130-1981) and Turkish Standard 485, the SDA stones must be extracted from the peduncle side of the apricot. Therefore, the correct position of the apricot peduncle and style side must be determined. In this study, a deep learning architecture was improved for the first time to determine the position of SDA stones as a component of the agricultural machine developed to extract SDA stones. In this study, a new Capsule Network architecture was used. With the original capsule network, SDA images were classified with 86.23% accuracy, while it increased to 94.47%with the improved capsule network. Also, the processing time of the developed network architecture was about twice as fast as the original. The result clearly demonstrates that the SDA stone positions are easily determined. Therefore, the designed agricultural machine can extract the SDA stones hygienically and rapidly, without any need for human power.
کلیدواژه‌های انگلیسی مقاله Capsule networks, Deep features, Prunus armeniaca, Sulfured dried apricots.
نویسندگان مقاله | Ö. O. Dursun
Avionics Department, Civil Aviation High School, Firat University, 23119 Elazig, Turkey.


| S. Toraman
Air Traffic Control Department, Civil Aviation High School, Firat University, 23119 Elazig, Turkey.


| E. Oksuztepe
Avionics Department, Civil Aviation High School, Firat University, 23119 Elazig, Turkey.


| E. Oksuztepe
Avionics Department, Civil Aviation High School, Firat University, 23119 Elazig, Turkey.
نشانی اینترنتی http://jast.modares.ac.ir/browse.php?a_code=A-10-68395-1&slc_lang=en&sid=23
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده پژوهشی اصیل
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات