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首页> 外文期刊>Nordic Journal of Botany >Artificial neural networking to estimate the leaf area for invasive plant Wedelia trilobata
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Artificial neural networking to estimate the leaf area for invasive plant Wedelia trilobata

机译：人工神经网络估计叶地区入侵植物菊trilobata

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摘要

Leaf area are very important parameter for the understanding of growth and physiological responses of invasive plant species under different environmental factors. This study was conducted to build non‐destructive leaf area model of Wedelia trilobatathat were grown in greenhouse. Regression analysis and artificial neural network (ANN) approaches were used for the development of leaf area model with the help of leaf length and width of 262 plants samples. In selection of best method under both techniques, the lower value of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and higher value of R2 were considered. According to the results it was found that ANN have higher value of (R2=0.96) and lower value of error (MAE=0.023, RMSE=0.379, MAPE=0.001) than regression analysis (R2=0.94, MAE=0.111, RMSE=1.798, MAPE=0.0005). It was concluded that error between predicted and actual value was less under ANN. Therefore, ANN model approach can be used as an alternating method for the estimation of leaf area. Through estimation of leaf area, invasive plant growth can predict under different environment conditions.
机译：叶面积是非常重要的参数对生长和生理的理解下入侵植物物种的反应不同的环境因素。构建进行非破坏性的叶面积菊trilobatathat种植模式温室。神经网络(ANN)方法被用于叶面积发展模型的帮助下叶的长度和宽度262种植物样品。选择最好的方法在这两种技术,较低的平均绝对误差(MAE)的价值,均方根误差(RMSE),意思是绝对的R2的百分比误差(日军)和更高的价值被认为是。发现安(R2 = 0.96)和更高的价值低价值的错误(美= 0.023,RMSE = 0.379,日军= 0.001)比回归分析(R2 = 0.94,美= 0.111,RMSE = 1.798,日军= 0.0005)。得出结论,预测和实际之间的误差值下少安。方法可以作为交流的方法叶面积的估算。叶面积,侵入性植物的生长可以预测在不同的环境条件下。

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