با همکاری انجمن مهندسان مکانیک ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مکانیک بیوسیستم، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، اهواز، ایران

2 گروه باغبانی، دانشکده کشاورزی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، اهواز، ایران

چکیده

تشخیص خودکار و به‌موقع بیماری‌های گیاهی، یک موضوع اساسی در نظارت و تولید محصولات سالم و باکیفیت است. لذا طراحی و توسعه روشی سریع، خودکار، ارزان و دقیق به‌منظور تشخیص بیماری گیاهان در مراحل اولیه از اهمیت به‌سزایی برخوردار است. در این پژوهش تصاویر از 40 لیلیوم آلوده به بیماری آتشک و 40 گیاه سالم توسط دوربین دیجیتال اخذ و پس از تقسیم‌بندی تصاویر تعداد 9 ویژگی رنگی از سه کانال RGB، Lab و HSV از ساقه و برگ گیاه و همچنین یک ویژگی مورفولوژیکی (طول ساقه) از گیاه استخراج شد. با اعمال الگوریتم پرچین‌های زبانی طی 100 هزار تکرار موثرترین این ویژگی‌ها (L برگ، L ساقه، a برگ، b برگ، H برگ، b ساقه، H ساقه، V برگ و طول ساقه) انتخاب و به‌وسیله خوشه‌بند k-means گروه‌بندی شدند. در نهایت نشان داده شد که دقت خوشه‌بند برای دو گونه بیمار، سالم و دقت کلی به‌ترتیب برابر با 42/96 و 100 و 63/97 درصد به‌دست آمد.

کلیدواژه‌ها

Open Access

©2021 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

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