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 trilobata that 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.

中文翻译：

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人工神经网络估计入侵植物Wedelia trilobata的叶面积

叶面积是了解不同环境因素下入侵植物的生长和生理反应的重要参数。进行这项研究以建立在温室中生长的Wedelia trilobata的无损叶面积模型。借助回归分析和人工神经网络（ANN）方法，借助262个植物样品的叶长和叶宽来开发叶面积模型。在选择这两种技术的最佳方法时，均应考虑较低的平均绝对误差（MAE），均方根误差（RMSE），平均绝对百分比误差（MAPE）和较高的R ²值。根据结果​​，发现神经网络具有较高的（R ² = 0.96）和较低的误差值（MAE = 0.023，RMSE = 0.379，MAPE = 0.001）比回归分析（R ²  = 0.94，MAE = 0.111，RMSE = 1.798，MAPE = 0.0005）。结论是，在人工神经网络下，预测值和实际值之间的误差较小。因此，可以将人工神经网络模型方法用作估计叶面积的替代方法。通过估计叶面积，入侵植物的生长可以预测不同环境条件下的入侵。