نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکترا دانشکده فیزیک، دانشگاه شهید باهنر کرمان، کرمان، ایران
2 دانشکده فیزیک، دانشگاه شهید باهنر کرمان، کرمان، ایران
چکیده
در سالهای اخیر استفاده از یادگیری عمیق در کشاورزی دقیق بهمنظور تشخیص و شمارش آفات و یا بیماریهای گیاهان، سمپاشی هوشمند، تخمین سطح زیر کشت و نظارت بر روند رشد گیاهان جهت مقابله با عوامل بازدارنده و یا کاهشدهنده رشد و با هدف افزایش بهرهوری محصولات کشاورزی به سرعت رو به افزایش است. در این مقاله، به طراحی الگوریتمی برگرفته از شبکه عصبی عمیق YOLOv5s جهت تشخیص و شمارش خودکار کاکلهای گیاه ذرت پرداخته شده است. برای این منظور، از تصاویر اخذ شده توسط پهپاد از مزرعه ذرت در دو تاریخ متفاوت جهت آموزش و ارزیابی شبکه استفاده گردیده و با توجه به نوع و اندازه داده به اعمال تغییراتی در معماری و تابع فعالسازی الگوریتم اصلی YOLOv5s با هدف افزایش تعداد پارامترهای شبکه، کاهش بیش برازش و افزایش دقت تشخیص پرداخته شد و الگوریتم Modified YOLOv5s که به اختصار MYOLOv5s نام دارد بهعنوان نسخه بهبودیافته YOLOv5s با قابلیت شناسایی و شمارش کاکلهای ذرت با مقادیر ضریب تبیین (R2) 99.28 درصد و دقت متوسط (AP) 95.30 درصد حاصل شد. همچنین، عملکرد روش پیشنهادی بهکار گرفته شده در این مقاله با الگوریتمهای معتبر معرفی شده در این زمینه ,TasselNetv2+ Faster R-CNN و RetinaNet مقایسه گردید. نتایج بهدستآمده نشان میدهد که مقادیر ضریب تبیین برای این سه شبکه بهترتیب 77.86، 86.83 و 95.53 درصد میباشد. همچنین برای الگوریتمهای Faster R-CNN و RetinaNet مقادیر دقت متوسط 76.99 و 77.66 درصد بهدست آمد. این نتایج نشان میدهند که الگوریتم MYOLOv5s حداکثر مقادیر ضریب تبیین R2 و دقت متوسط (AP)، دقت (Precision) و یادآوری (Recall) را دارد که بیانگر کارایی بالای روش پیشنهادی در تشخیص کاکل ذرت است. شایان ذکر است MYOLOv5s با دارا بودن سرعت پردازش 84 فریم بر ثانیه سریعترین روش در تشخیص کاکل ذرت محسوب میگردد.
کلیدواژهها
موضوعات
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