Visible and near-infrared (VNIR) spectroscopy is an eco-friendly method used for estimating plant nutrient deficiencies. The aim of this study was to investigate the possibility of using VNIR method for estimating Zn content in cherry orchard leaves under field conditions. The study was conducted in 3 different locations in Isparta region of Turkey. Fifteen cherry orchards containing normal and Zn deficient plants were chosen, and 60 leaf samples were collected from each location. The reflectance spectra of the leaves were measured with an ASD FieldSpec HandHeld spectroradiometer and a plant probe. The Zn contents of leaf samples were predicted through laboratory analysis. The spectral reflectance measurements were used to estimate the Zn levels using stepwise multiple linear regression analysis method. Prediction models were created using the highest coefficient of determination value. The results show that Zn content of cherry trees can be estimated using the VNIR spectroscopic method (87.5<r2<96.79). Moreover, plant nutrient contents can be estimated without using chemicals. However, further research is necessary to develop a standard method for field conditions. Because spectral reflectance is affected by ecological conditions, agricultural applications and nutrient interactions, more effective models must be developed depending on the geographical location, period and plant type.

为快速、准确地实现水稻氮素营养诊断,以中嘉早17水稻为试验对象,设置4种施氮水平的水稻栽培试验,利用便携式地物波谱仪获取240组水稻分蘖期顶三叶在350~2 500 nm的光谱数据。随机将样本划分为训练集(160个样本)和测试集(80个样本)。首先,通过多元散射校正(MSC)、变量标准化校正(SNV)、平滑算法(SG)3种方法分别对原始光谱进行预处理;然后,采用主成分分析(PCA)和连续投影算法(SPA)对预处理后的光谱进行特征降维,选取累积贡献率超过99.98%的前24个主成分作为模型的输入变量,对于经过MSC、SNV和SG处理后的光谱数据,还分别筛选出12、15、19个特征波长;最后,应用支持向量机(SVM)基于上述处理分别建立水稻氮素营养诊断模型。结果表明,采用MSC-PCA-SVM模型进行水稻氮素营养诊断的识别准确率最高,其在训练集和预测集上的准确率分别达99.38%和97.50%。

In order to realize rice nitrogen nutrition diagnosis quickly and accurately, rice cultivation experiments with 4 nitrogen application levels were conducted on Zhongjiazao 17 rice cultivar. A total of 240 rice spectral data were collected at tillering stage within 350 to 2 500 nm from top trilobate by using portable earth mass spectrometry. All the samples were randomly divided into training set (160 samples) and prediction set (80 samples). The original spectrum was pretreated by multiplicative scatter correction (MSC), standard normal variate (SNV) and Savitzky-Golay smoothing (SG) methods, respectively. Then, principal component analysis (PCA) and successive projection algorithm (SPA) were used for feature reduction and feature selection of the pre-processed spectra. After principal component analysis, the first 24 principal components, of which the accumulative contribution exceeded 99.98%, were used as the input variables of the model, and 12, 15 and 19 characteristic wavelengths were selected for the spectrum after MSC, SNV and SG treatments, respectively. Finally, rice nitrogen nutrition diagnosis models were established with support vector machine (SVM). It was shown that the MSC-PCA-SVM model was the best method for rice nitrogen nutrition diagnosis, of which the accuracy rate on training set and prediction set was 99.38% and 97.50%, respectively.

【目的】分析芜菁营养生长期叶片光谱反射率对N的响应,研究采用叶片光谱指数诊断N敏感时期,为新疆芜菁简便快捷的非破坏性N营养诊断提供最佳时间窗。【方法】利用Unispec-SC光谱仪测定不同芜菁幼苗期、叶片生长旺盛期、肉质根生长盛期、成熟期叶片的光谱反射率。【结果】芜菁营养生长期叶片光谱反射率波动取决于波长,在可见光波段的变异最小。芜菁营养生长期不同发育阶段光谱指数(ND₇₀₅)之间均存在显著差异(P＜0.05)或者极显著差异(P＜0.01)。【结论】叶片生长旺盛期为长黄蔓菁、新星圆蔓菁、美国小芜菁、天地禾芜菁氮素光谱营养诊断的敏感时期,叶片生长旺盛期与肉质根生长盛期为新星圆蔓菁(二代)氮素光谱营养诊断的敏感时期。幼苗期和成熟期可以作为区分几个品种芜菁氮素光谱营养诊断的敏感生育期。

【Objective】 The sensitive period of leaf spectral index was studied by analyzing the leaf spectral reflectance in the response to Nitrogen (N) fertilizer at different growing stages of different turnip cultivars in Xinjiang aiming to provide a non-invasive,simple and rapid nutrition diagnosis of N.【Method】The spectral reflectance of different turnip seedling stages, leaf growth exuberant stage, fleshy root growth peak stage and mature stage were measured by Unispec-SC spectrometer with different turnip cultivars as the test material.【Result】The fluctuation of leaf spectral reflectance of different turnip cultivars depended on the wavelengths at all the growing stages,and the least variation was in the range of visible wavelength. The leaf spectrum indexes (ND₇₀₅) of different turnip cultivars were significant (<i>P</i> < 0.05) and extremely significant (<i>P</i> < 0.01) different at all growing stages.【Conclusion】Spectral sensitive period of foliar N concentration varied during different growing stages. The sensitive period for leaf spectral nutrition diagnosis in response to N of long and yellow turnip, Xinxing round turnip, American turnip and Tiandihe turnip were leaf exuberant growth period. The sensitive period for leaf spectral nutrition diagnosis in response to N of Xinxing round turnip (second generation) turnip are leaf exuberant growth period and fleshy root growth stage. The seedling stage and fruit mature period can be used to distinguish several varieties of turnip diagnosis of nitrogen sensitive growth stage.

Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.

In recent years, the introduction of real-time simulators (RTS) has changed the way of researching the power network. In particular, researchers and system operators (SOs) are now capable of simulating the complete network and of making it interact with the real world thanks to the hardware-in-the-loop (HIL) and digital twin (DT) concepts. Such tools create infinite scenarios in which the network can be tested and virtually monitored to, for example, predict and avoid faults or energy shortages. Furthermore, the real-time monitoring of the network allows estimating the status of the electrical assets and consequently undertake their predictive maintenance. The success of the HIL and DT application relies on the fact that the simulated network elements (cables, generation, accessories, converters, etc.) are correctly modeled and characterized. This is particularly true if the RTS acquisition capabilities are used to enable the HIL and the DT. To this purpose, this work aims at emphasizing the role of a preliminary characterization of the virtual elements inside the RTS system, experimentally verifying how the overall performance is significantly affected by them. To this purpose, a virtual phasor measurement unit (PMU) is tested and characterized to understand its uncertainty contribution. To achieve that, firstly, the characterization of a virtual PMU calibrator is described. Afterward, the virtual PMU calibration is performed, and the results clearly highlight its key role in the overall uncertainty. It is then possible to conclude that the characterization of the virtual elements, or models, inside RTS systems (omitted most of the time) is fundamental to avoid wrong results. The same concepts can be extended to all those fields that exploit HIL and DT capabilities.

【目的】氮、磷均为作物必需的大量营养元素，其丰缺诊断直接关系到合理科学施肥，进而影响产量、效益以及环境。本文旨在研究准确、快捷、无损地区分水稻缺氮和缺磷信息的光谱识别方法，从而指导田间施肥决策，精确作物管理、节约种植成本并控制农田面源污染。【方法】基于水稻6个氮素及两个磷素营养水平交互下的盆栽试验，分别在分蘖、拔节和抽穗期测定水稻冠层的可见近红外反射光谱（350&mdash;1 330 nm）及植株全氮（TN）和全磷（TP）含量等数据，分析氮磷互作对水稻植株体内TN和TP含量以及冠层反射光谱的影响，并运用概率神经网络(PNN)分别对不同生育时期的冠层光谱进行氮水平、磷水平、氮磷交互水平和缺素水平4个尺度下的分类识别。为避免光谱测量时仪器误差和光照、风力、温度、水分等环境条件所造成光谱数据批次间的差异，PNN分类识别前对光谱数据进行标准化处理，并将其中2/3作为训练集，另外1/3作为测试集。【结果】植株全氮含量受氮肥、磷肥和氮磷交互作用的影响显著；植株全磷含量则主要受磷肥和氮肥水平的双重影响，但不存在氮磷交互作用。水稻冠层光谱对氮肥的响应规律不受磷肥水平的影响，缺氮使可见光区反射率升高，近红外区反射率下降。缺磷使近红外区反射率下降，但可见光区的响应则受氮肥水平的影响，施氮处理呈上升趋势，氮胁迫处理则呈现分蘖期下降、拔节期上升、抽穗期下降的趋势。利用冠层光谱PNN模型可以对各个生育时期氮水平、磷水平、氮磷交互水平和缺素水平等不同施肥尺度进行识别，拔节期分类精度最高，抽穗期分类精度相对最低。4种分类尺度下PNN模型对磷素水平的分类精度最高，分蘖期和拔节期分别为83%和94%；其次是缺素水平，分别为78%和88%；对氮素水平以及氮磷交互水平等有较多个分类输出的识别精度较低，为61%&mdash;75%。值得一提的是，PNN模型对水稻施肥关键生育时期分蘖期和拔节期水稻植株缺氮缺磷、缺氮不缺磷、缺磷不缺氮、不缺氮不缺磷等4种缺素水平的分类中，所有只缺氮处理没有被预测为只缺磷处理，所有只缺磷处理也没有被误判为只缺氮处理，表明冠层光谱PNN模型能有效区分开氮磷胁迫。【结论】水稻的冠层光谱受到氮、磷水平的共同影响，利用水稻冠层光谱建立的PNN模型不仅能分别辨识各氮素、磷素施肥水平，并且能有效地区分开水稻缺磷和缺氮处理，避免混淆，对有目的性的指导施肥具有重要的意义和价值，可避免不恰当的施肥策略造成的环境、产量和经济损失。

Based on Savitzky-Golay (SG) smoothing screening, principal component analysis (PCA) combined with separately supervised linear discriminant analysis (LDA) and unsupervised hierarchical clustering analysis (HCA) were used for non-destructive visible and near-infrared (Vis-NIR) detection for breed screening of transgenic sugarcane. A random and stability-dependent framework of calibration, prediction, and validation was proposed. A total of 456 samples of sugarcane leaves planting in the elongating stage were collected from the field, which was composed of 306 transgenic (positive) samples containing Bt and Bar gene and 150 non-transgenic (negative) samples. A total of 156 samples (negative 50 and positive 106) were randomly selected as the validation set; the remaining samples (negative 100 and positive 200, a total of 300 samples) were used as the modeling set, and then the modeling set was subdivided into calibration (negative 50 and positive 100, a total of 150 samples) and prediction sets (negative 50 and positive 100, a total of 150 samples) for 50 times. The number of SG smoothing points was ex- panded, while some modes of higher derivative were removed because of small absolute value, and a total of 264 smoothing modes were used for screening. The pairwise combinations of first three principal components were used, and then the optimal combination of principal components was selected according to the model effect. Based on all divisions of calibration and prediction sets and all SG smoothing modes, the SG-PCA-LDA and SG-PCA-HCA models were established, the model parameters were optimized based on the average prediction effect for all divisions to produce modeling stability. Finally, the model validation was performed by validation set. With SG smoothing, the modeling accuracy and stability of PCA-LDA, PCA-HCA were signif- icantly improved. For the optimal SG-PCA-LDA model, the recognition rate of positive and negative validation samples were 94.3%, 96.0%; and were 92.5%, 98.0% for the optimal SG-PCA-LDA model, respectively.Vis-NIR spectro- scopic pattern recognition combined with SG smoothing could be used for accurate recognition of transgenic sugarcane leaves, and provided a convenient screening method for transgenic sugarcane breeding.

The present paper proposed how to select characteristic near-infrared wavelength for soil total nitrogen by using successive projection algorithm (SPA). Spectral data are compressed by SPA in the first place to obtain the raw wavelengths. Then the group of wavelengths derived from SPA is screened by their contributions to the total nitrogen. The insensitive wavelengths for total nitrogen are eliminated, improving the parsimony of the calibration model. For the 85 soil samples in total nitrogen, SPA was used to select the raw wavelengths. After screening on contribution, the number of wavelengths dropped from 12 by direct SPA to 6. Finally, the calibration model using wavelengths selected by screening on contribution after SPA showed the correlation coefficient (R(p)) of 0.913 and the root mean square error of prediction (RMSEP) of 0.011%. This model is as precise as the one before screening on contribution, and more precise than the result derived from partial least square (PLS) for the whole spectrum. The results demonstrate that the number of wavelengths selected by SPA can be reduced without significantly compromising prediction performance using the screening on contribution. The 6 selected total nitrogen wavelengths in this paper can be a reference for designing smart filter NIR spectrometer.