با همکاری انجمن مهندسان مکانیک ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه بوعلی سینا، همدان، ایران

10.22067/jam.2024.88013.1248

چکیده

بیماری‌های درخت بِه یکی از نگرانی‌های عمده باغداران می‌باشد و شناسایی آن‌ها در پایش درختان ضروری است چرا که زیآن‌های اقتصادی قابل‌توجهی وارد می‌کند. از این رو، تشخیص به‌موقع و موثر بیماری‌های برگی درختان بِه، نقش مهمی در جلوگیری از این ضرر اقتصادی دارد. بیشتر علائم بیماری این درخت در برگ ظاهر می‌شود و تشخیص آن‌ها نیاز به متخصصان خبره داشته و از طرفی زمان‌بر بوده و هزینه آزمایشگاهی بالایی دارد. اصلی‌ترین بیماری‌های این محصول شامل آتشک، زخم برگ و سفیدک پودری است. با پیشرفت الگوریتم‌های هوش مصنوعی، شبکه‌های عصبی مختلفی برای طبقه‎بندی معرفی شده‌اند که از مهم‌ترین آن‌ها می‌توان به شبکه‌های عصبی پیچشی (کانولوشنی) اشاره کرد. هدف اصلی این مطالعه بهینه‌سازی و تنظیم پارامترهای اصلی این شبکه‌ها به‌منظور افزایش دقت تشخیص بیماری‌های برگی درخت بِه می‌باشد. در این مطالعه در رویکرد اول با استفاده از یادگیری انتقالی،‌ دو الگوریتم مهم Inception-ResNet-v2 و ResNet-101 و در رویکرد دوم یک الگوریتم بهینه‌شده پیشنهادی برای طبقه‌بندی بیماری‌ها استفاده شد. نتایج مدل‌ها نشان داد که حذف تصادفی باعث اصلاح دقت بعضی مدل‌ها گردید و بیشترین عملکرد با 64 نورون در لایه مخفی حاصل گردید. مدل پیشنهادی دقت بالاتری نسبت به روش انتقالی داشت. با بررسی نتایج کلی، مدل پبشنهادی با چهار لایه پیچشی در بلوک کانولوشنی، یک لایه مخفی در بلوک شبکه عصبی و ضریب دراپ‌اوت 0.5 بیشترین عملکرد را ارایه داد. 

کلیدواژه‌ها

موضوعات

©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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