Yang Jian, Sun Jia, Du Lin, Chen Biwu, Zhang Zhenbing, Shi Shuo, Gong Wei
Opt Express. 2017 Feb 20;25(4):3743-3755. doi: 10.1364/OE.25.003743.
Paddy rice is one of the most significant food sources and an important part of the ecosystem. Thus, accurate monitoring of paddy rice growth is highly necessary. Leaf nitrogen content (LNC) serves as a crucial indicator of growth status of paddy rice and determines the dose of nitrogen (N) fertilizer to be used. This study aims to compare the predictive ability of the fluorescence spectra excited by different excitation wavelengths (EWs) combined with traditional multivariate analysis algorithms, such as principal component analysis (PCA), back-propagation neural network (BPNN), and support vector machine (SVM), for estimating paddy rice LNC from the leaf level with three different fluorescence characteristics as input variables. Then, six estimation models were proposed. Compared with the five other models, PCA-BPNN was the most suitable model for the estimation of LNC by improving R and reducing RMSE and RE. For 355, 460 and 556 nm EWs, R was 0.89, 0.80 and 0.88, respectively. Experimental results demonstrated that the fluorescence spectra excited by 355 and 556 nm EWs were superior to those excited by 460 nm for the estimation of LNC with different models. BPNN algorithm combined with PCA may provide a helpful exploratory and predictive tool for fluorescence spectra excited by appropriate EW based on practical application requirements for monitoring the N status of crops.
水稻是最重要的食物来源之一，也是生态系统的重要组成部分。因此，准确监测水稻生长非常必要。叶片氮含量（LNC）是水稻生长状况的关键指标，决定了氮肥的使用剂量。本研究旨在比较不同激发波长（EWs）激发的荧光光谱结合主成分分析（PCA）、反向传播神经网络（BPNN）和支持向量机（SVM）等传统多元分析算法，以三种不同荧光特征作为输入变量从叶片水平估算水稻LNC的预测能力。然后，提出了六个估算模型。与其他五个模型相比，PCA-BPNN通过提高R并降低RMSE和RE，是最适合估算LNC的模型。对于355、460和556nm的EWs，R分别为0.89、0.80和0.88。实验结果表明，对于不同模型估算LNC，355和556nm EWs激发的荧光光谱优于460nm激发的荧光光谱。基于监测作物氮素状况的实际应用需求，结合PCA的BPNN算法可能为适当EW激发的荧光光谱提供有用的探索和预测工具。
