9 Ocak 2023 Konferans bildirisi Açık Erişim

Güzel Mustafa; Turan Bülent; Şin Bahadır; Baştürk Alper

JSON-LD (schema.org)

{
  "@context": "https://schema.org/", 
  "@id": 252239, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "Tokat Gaziosmanpa\u015fa \u00dcniversitesi", 
      "name": "G\u00fczel Mustafa"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Tokat Gaziosmanpa\u015fa \u00dcniversitesi", 
      "name": "Turan B\u00fclent"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Sakarya Uygulamal\u0131 Bilimler \u00dcniversitesi", 
      "name": "\u015ein Bahad\u0131r"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Erciyes \u00dcniversitesi", 
      "name": "Ba\u015ft\u00fcrk Alper"
    }
  ], 
  "datePublished": "2023-01-09", 
  "description": "<p>The knowledge of weed numbers is very helpful for many studies due to minimizing weed harm<br>\non the crops as well as knowing the weed species and classes. In this study, we used a deep learning<br>\narchitecture that was capable of detecting some weeds to count the weed numbers instead of classical<br>\nmanual weed counting methods. The pre-trained deep learning weight belongs to YOLOv5 which<br>\nis used in this study, can detect 5 different phenological terms (cotyledon leaves period, 3-5 leaves<br>\nperiod, pre-flowering period, flowering period, and fruit and seed setting period) of some harmful weeds<br>\n(sherlock mustard-Sinapis arvensis L., creeping thistle-Cirsium arvense L. Scop, and forking larkspur-<br>\nConsolida regalis Gray) in wheat production and other crops with 98% highest accuracy. StrongSORT<br>\nwith the OSNet tool is used as the multi-object tracker. The weeds successfully counted from any image<br>\nresources (image, video, webcam, etc.) while avoiding recounting the same object by computer vision. It<br>\nplays an important role in the studies aimed to understand weeds population spread, resistance gaining to<br>\nherbicides by weeds, the economical threshold of weeds, etc. It also provides these parameters cheaper<br>\nand faster than classical methods.</p>", 
  "headline": "COUNTING OF WEED NUMBERS IN FARMS BY DEEP LEARNING-STRONGSORT", 
  "identifier": 252239, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "inLanguage": {
    "@type": "Language", 
    "alternateName": "eng", 
    "name": "English"
  }, 
  "keywords": [
    "Weeds counting", 
    "deep learning", 
    "StrongSORT"
  ], 
  "license": "https://creativecommons.org/licenses/by-nc-nd/4.0/", 
  "name": "COUNTING OF WEED NUMBERS IN FARMS BY DEEP LEARNING-STRONGSORT", 
  "url": "https://aperta.ulakbim.gov.tr/record/252239"
}