9 Ocak 2023 Konferans bildirisi Açık Erişim

Güzel Mustafa; Turan Bülent; Şin Bahadır; Baştürk Alper

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    <subfield code="a">COUNTING OF WEED NUMBERS IN FARMS BY DEEP LEARNING-STRONGSORT</subfield>
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    <subfield code="a">&lt;p&gt;The knowledge of weed numbers is very helpful for many studies due to minimizing weed harm&lt;br&gt;
on the crops as well as knowing the weed species and classes. In this study, we used a deep learning&lt;br&gt;
architecture that was capable of detecting some weeds to count the weed numbers instead of classical&lt;br&gt;
manual weed counting methods. The pre-trained deep learning weight belongs to YOLOv5 which&lt;br&gt;
is used in this study, can detect 5 different phenological terms (cotyledon leaves period, 3-5 leaves&lt;br&gt;
period, pre-flowering period, flowering period, and fruit and seed setting period) of some harmful weeds&lt;br&gt;
(sherlock mustard-Sinapis arvensis L., creeping thistle-Cirsium arvense L. Scop, and forking larkspur-&lt;br&gt;
Consolida regalis Gray) in wheat production and other crops with 98% highest accuracy. StrongSORT&lt;br&gt;
with the OSNet tool is used as the multi-object tracker. The weeds successfully counted from any image&lt;br&gt;
resources (image, video, webcam, etc.) while avoiding recounting the same object by computer vision. It&lt;br&gt;
plays an important role in the studies aimed to understand weeds population spread, resistance gaining to&lt;br&gt;
herbicides by weeds, the economical threshold of weeds, etc. It also provides these parameters cheaper&lt;br&gt;
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