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Fast Measurement of Fruit Tree Nitrogen Status Based on Multi-color LEDs and BP-ANN

机译:基于多色LED和BP-ANN的果树氮现状快速测量

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A new optical instrument for fast measurement of fruit tree nitrogen status was designed and fabricated. Multi-color LEDs were used as light source, a portable spectrophotometer as optical detector, and an optic fiber as light signal transmission medium. In the paper, the principle of using multi-color LEDs for the measurement of fruit tree nitrogen status was first introduced. Then, the method of using error back propagation artificial neural network (BP-ANN) for calibration modeling was elaborated. Reflective light intensities of Multi-color LEDs were taken as the incoming signals to BP-ANN. The structure of BP-ANN with three layers has been optimized to minimize its calibration error. In the test, total 200 leave samples picked from Huang-hua pear trees planted in three orchards with different levels of nitrogen fertilizing. Among them, 150 samples were selected randomly out for calibration set with the remaining 50 for prediction set. The result shows that correlation coefficient of R~2 between predicted and measured values of nitrogen content reaches 0.93 with maximum prediction error less than 3.36 (SPAD). The study suggests that the new optical method integrating multi-color LEDs with BP-ANN is promising for fast diagnosis of fruit tree mineral nutrition status.
机译:设计和制造了一种用于快速测量果树氮现状的新型光学仪器。多色LED用作光源,便携式分光光度计作为光学检测器,以及光纤作为光信号传输介质。本文首先介绍了使用用于测量果树氮地位的多色LED的原理。然后,详细阐述了使用误差反向传播人工神经网络(BP-ANN)进行校准建模的方法。将多色LED的反射光强度作为输入信号到BP-ANN。已经优化了具有三层的BP-ANN的结构以最小化其校准误差。在测试中,总共200次留下样品从黄华梨树挑选,种植在三个果园,具有不同水平的氮肥。其中,随机选择150个样品,用于用剩余的50进行校准,用于预测集合。结果表明,在预测和测量值之间的氮含量与测量值之间的相关系数达到0.93,最大预测误差小于3.36(Spad)。该研究表明,与BP-ANN的多色LED集成的新光学方法很有希望快速诊断果树矿物营养状态。

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