طبقه‌بندی توت‌فرنگی رقم پاروس با ترکیب تکنیک پردازش تصویر و روش‌های هوشمند

نوع مقاله : مقاله پژوهشی لاتین

نویسندگان

• فرهاد فاتحی ¹
• هادی صمیمی اخیجهانی ²

¹ گروه مکانیک بیوسیستم، دانشگاه تبریز.

² گروه مهندسی بیوسیستم، دانشگاه کردستان.

https://doi.org/10.22067/ifstrj.v17i3.84187

چکیده

امروزه در کشاورزی مدرن ترکیبی از تکنیک‌­های پردازش تصویر و روش‌­های هوشمند برای جایگزینی ماشین‌­های هوشمند به‌جای انسان استفاده می‌­شود. در این مطالعه از روش پردازش تصویر مصنوعی و شبکه عصبی مصنوعی (ANN) برای طبقه‌­بندی میوه توت­‌فرنگی رقم پاروس استفاده شده است. در گام اول این میوه توسط یک متخصص به شش کلاس طبقه‌بندی شد (به‌عنوان خروجی ANN) و از هر کلاس 100 نمونه به‌طور تصادفی جمع‌­آوری گردید. در گام بعد تصاویر نمونه­‌ها ضبط شده و سه خصوصیت هندسی با 12 ویژگی رنگ (به‌عنوان ورودی‌های ANN) استخراج گردید. ساختار شبکه عصبی بهینه با توجه به خطای میانگین مربعات (RMSE) و ضریب هبستگی (2R) برای فرآیند طبقه‌بندی نمونه­‌های توت‌فرنگی درنظر گرفته شد. درنهایت شبکه عصبی پرسپترون با ساختار 15-18-6 با دقت 83/83٪ انتخاب گردید.

کلیدواژه‌ها

• طبقه‌بندی توت‌فرنگی
• رقم پاروس
• پردازش تصویر
• شبکه عصبی مصنوعی

عنوان مقاله [English]

Classification of Parus strawberry fruit by combining image processing techniques and intelligent methods

نویسندگان [English]

• Farhad Fatehi ¹
• Hadi Samimi Akhijahani ²

¹ Department of Mechanics of Biosystem Engineering, University of Tabriz

² Department of Biosystem Engineering, University of Kurdistan.

چکیده [English]

Nowadays, in modern agriculture, the combination of image processing techniques and intelligent methods has been used to replace smart machine instead of humans. In this study, an artificial image processing and artificial neural network (ANN) method was used to classify strawberry fruit of Parus variety. In the first step, the fruit was divided into 6 classes (ANN outputs) by the expert, and 100 samples were randomly collected from each class. In the next step, the images of the samples were captured and three geometric properties with twelve color properties (as ANN inputs) were extracted. Optimum artificial neural network structures considering root mean squared error (RMSE) and correlation coefficient (R2) were investigated to classification process of the strawberry samples. Finally, the perceptron neural network with a structure of 6-18-15 was selected with an average accuracy of 83.83%.

کلیدواژه‌ها [English]

• Artificial neural network
• Image processing
• Parus variety
• Strawberry classification

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