Identification of Houseplants Using Neuro-vision Based Multi-stage Classification System
محورهای موضوعی : مجله گیاهان زینتی
Narges Ghanei Ghoushkhaneh
1
(Department of Biosystems Engineering, Ferdowsi University of Mashhad, Iran)
Abbas Rohani
2
(Department of Biosystems Engineering, Ferdowsi University of Mashhad, Iran)
Mahmood Reza Golzarian
3
(Department of Biosystems Engineering, Ferdowsi University of Mashhad, Iran)
Fatemeh Kazemi
4
(Department of Horticultural Sciences and Landscape Engineering, Ferdowsi University of Mashhad, Iran)
کلید واژه: image processing, Artificial Neural Networks, Houseplants, identification, Multi-stage classification,
چکیده مقاله :
In this paper, we present a machine vision system that was developed on the basis of neural networks to identify twelve houseplants. Image processing system was used to extract 41 features of color, texture and shape from the images taken from front and back of the leaves. The features were fed into the neural network system as the recognition criteria and inputs. Multilayer perceptron (MLP) neural network with Declining Learning-Rate Factor algorithm (BDLRF) training algorithm was used as a classifier. Classification was done in three stages based on eligibility and strength of characteristics in identifying the plants. Eligibility criteria were assessed at each stage using plants class resolution power. In this classification method, each step requires a small number of attributes and for this reason its speed and accuracy can be very high. The results showed that the accuracy of classification of plants in three steps reaches 100%. Also, the optimal features for classification included three inputting steps of morphological features, HSI color features extracted from back of the leaves, and HSI texture features of the back of the leaves.
در این مقاله سامانه بینایی ماشینی مبتنی بر شبکه عصبی برای شناسایی 12 گیاه آپارتمانی توسعه داده شد. از سامانه پردازش تصویر برای استخراج 41 ویژگی رنگی، بافتی و شکلی از تصاویر رو و پشت برگ گیاه استفاده گردید. ویژگی­های استخراج یافته به عنوان معیار تشخیص و ورودی به شبکه عصبی داده شد. شبکه عصبی پرسپترون چند لایه (MLP) با الگوریتم آموزش، الگوریتم فاکتور کاهش نرخ یادگیری (BDLRF) به عنوان طبقه­بندی کننده استفاده گردید. طبقه­بندی در سه مرحله براساس قابلیت و قدرت ویژگیها در شناسایی گیاهان انجام شد. معیار قابلیت داشتن در هر مرحله با استفاده از قدرت تفکیک پذیری کلاسی گیاهان بررسی گردید. در این روش طبقه­بندی، هر مرحله نیاز به تعداد کمی از ویژگیها دارد؛ در نتیجه سرعت و دقت آن می‌تواند بسیار بالا باشد. نتایج نشان داد که دقت طبقه­بندی گیاهان در سه مرحله به 100% می‌رسد. همچنین ویژگیهای بهینه برای طبقه­بندی شامل سه مرحله‌ی ورودی از ویژگیهای موفولوژیکی (شکلی)، ویژگیهای رنگی HSI استخراج یافته از پشت برگ و ویژگیهای بافتی HSI استخراج یافته از پشت برگ‌ها می‌شود.
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