مقایسه دقت شناسایی بذر علف‌های‌هرز با استفاده از رهیافت پردازش تصویر و روش‌های تشخیص الگو

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مهندسی تولید و ژنتیک گیاهی، پردیس کشاورزی و منابع طبیعی، دانشگاه رازی، کرمانشاه

2 دانش آموخته کارشناسی ارشد گروه مهندسی تولید و ژنتیک گیاهی، پردیس کشاورزی و منابع طبیعی، دانشگاه رازی، کرمانشاه

چکیده

به‌منظور شناسایی بذر علف‌های‌هرز به کمک رهیافت بینایی ماشین، از دو روش شبکه عصبی مصنوعی و آنالیز تشخیص متعارف استفاده شد. بذرهای تاج‌خروس ریشه‌قرمز (Amaranthus retroflexus)، تاج‌خروس خوابیده (Amaranthus blitoides)، سلمه‌تره
(Chenopodium album)، قدومه (Alyssum hirsutum) و خردل وحشی (Sinapis arvensis) جمع‌آوری‌ شد و پس از ثبت تصاویر آن‌ها، خصوصیات مربوط به شکل هر بذر استخراج شد. داده‌های استخراج شده، به‌صورت خام و استاندارد شده درآمدند. همچنین با استفاده از روش رگرسیون گام‌به‌گام، مهم‌ترین خصوصیات شکلی بذرها نیز شناسایی شدند. نتایج نشان داد که دقت شناسایی شبکه عصبی ساخته ‌شده از داده‌های خام و استاندارد مربوط به 13 متغیر پیشگو، به‌ترتیب 36/84 و 34/83  درصد و برای شبکه عصبی ساخته ‌شده از داده‌های ورودی خام و استاندارد حاصل از رگرسیون گام‌به‌گام، به‌ترتیب 30/84 و 39/83 درصد بود. دقت شناسایی روش آنالیز تشخیص متعارف نیز بر اساس کل داده‌های خام و استاندارد ورودی، به‌ترتیب 90/84 و 70/84 درصد بود. همچنین دقت شناسایی بذرها با استفاده از هر دو داده‌ خام و استاندارد حاصل از رگرسیون گام‌به‌گام در این روش، 6/82 درصد بود. بیشترین دقت شناسایی در هر دو روش (با بیش از 95 درصد دقت)، به علف‌های‌هرز قدومه و تاج‌خروس ریشه‌قرمز تعلق داشت. علاوه بر این، بالاترین دقت شناسایی بذرهای سلمه‌تره در هر دو روش، بالاتر از 87 درصد بود. این امر نشان می‌دهد که استفاده از خصوصیات شکلی در مدل‌های تشخیص الگو، دارای پتانسیل خوبی در شناسایی بذرهای این علف‌های‌هرز بود.

کلیدواژه‌ها


عنوان مقاله [English]

Classification of weed seeds using image processing and pattern recognition methods

نویسندگان [English]

  • Alireza Bagheri 1
  • Zohreh Heydari 2
1 Department of Agronomy and Plant Breeding, Razi University, Kermanshah, Iran
2 Department of Agronomy and Plant Breeding, Razi University, Kermanshah, Iran
چکیده [English]

In order to identify weed seeds by machine vision approach, two methods of artificial neural network (ANN) and canonical discriminant analysis (CDA) were applied. The seeds of Amaranthus retroflexus, Amaranthus blitoides, Chenopodium album, Alyssum hirsutum, and Sinapis arvensis were collected and the images of these seeds were recorded. The obtained images were processed and then characteristics related to the shape of each seed were extracted. The extracted data were in in the raw and standardized forms. In addition, main shape characteristics of the seeds were identified by stepwise regression. The results showed that the accuracy of the ANN constructed from the raw and the standard data were 84.30% and 83.39%, respectively. Identification accuracy of ANN was 84.30% and 83.39% for the raw and standard data extracted from stepwise regression. The results of CDA method showed that the identification accuracy of total raw and standard data were 84.9% and 84.7% respectively. Identification accuracy of this method was 82.6% for both raw and standard data extracted from stepwise regression. The highest identification accuracy in both methods (more than 95%) was belonged to A. retroflexus and A. hirsutum. In addition, the seed identification accuracy of C. album in both methods was higher than 87%. This suggests that the use of shape features in pattern recognition models had reliable potential in identifying the seeds of these weeds.

کلیدواژه‌ها [English]

  • Artificial intelligence
  • machine learning
  • seed identification
  • seed shape
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