دانلود مقاله انگلیسی رایگان:پیش بینی خواص مکانیکی برگ با داده کاوی - 2019
تبریک 1399
دانلود مقاله انگلیسی داده کاوی رایگان
  • A prediction of leaf mechanical properties with data mining A prediction of leaf mechanical properties with data mining
    A prediction of leaf mechanical properties with data mining

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    A prediction of leaf mechanical properties with data mining


    ترجمه فارسی عنوان مقاله:

    پیش بینی خواص مکانیکی برگ با داده کاوی


    منبع:

    Sciencedirect - Elsevier - Computers and Electronics in Agriculture, 162 (2019) 669-676: doi:10:1016/j:compag:2019:05:006


    نویسنده:

    Choo Wooi Hng, Wei Ping Loh


    چکیده انگلیسی:

    The mechanical properties of the leaf are typically determined by mechanical testing approaches to study the leaf lifespan, plant anti-herbivore defences and ecological functions by considering habitat influences, environmental resources variation and species diversity. While the leaf morphology features were commonly used for plant recognition and plant disease detection with the aid of an automated inspection system. However, the influence of morphology features on leaf mechanical properties is vague. In this research, we investigated the effect of various morphological features on mechanical properties of leaf, followed by proposed a novel leaf mechanical properties prediction model using data mining techniques. A 600×22 feature vector was collected and examined using Pearson correlation analysis and Welch’s test to select the relevant features. The prediction on four mechanical properties indicators was performed with LinearRegression, KStar, DecisionTable and M5P algorithms in the Waikato Environment for Knowledge Analysis (WEKA). The experimental results show that numeric prediction for Tearing Force (FT) and Tearing Strength (ST) (RRSE ≈ 25%) were about two folds better as compared to Work-to-tear (WT) and Specific Work-to-tear (SWT) (RRSE≈50%) in four algorithms tested. The best results achieved was the FT indicator prediction with M5P algorithms (RRSE=23.12%). FT indicator prediction model adopted from M5P algorithms output was constructed.
    Keywords: Data mining | Leaf geometry | Leaf morphology | Regression


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 8
    حجم فایل: 1797 کیلوبایت

    قیمت: رایگان


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