Open Access
Add to BibSonomy
Abstract
Nitrogen (N) is an essential nutrient for crop growth. The rapid and non-destructive monitoring of N nutrition in crops through remote sensing is important for the accurate diagnosis and quality evaluation of crop growth status. Leaf nitrogen concentration (LNC), which has been widely utilized in remote sensing, serves as a crucial indicator for the monitoring of crops growth status. In this study, the first-derivative fluorescence spectrum (FDFS) based on laser-induced fluorescence (LIF) was proposed for LNC estimation in paddy rice. First, the correlation between the LNC and FDFS at each wavelength was analyzed in detail using different excitation light wavelengths (ELWs; 355, 420, and 556 nm). Then, FDFS was used as an input parameter to train a back-propagation neural networks (BPNN) model for LNC estimation. The coefficients of determination (R2) of the linear regression analysis between the measured and predicted LNC were 0.823, 0.743, and 0.837, corresponding to 355, 420, and 556 nm ELWs, respectively. Second, the principal components analysis was performed for the extraction of the main characteristics of FDFS, and the calculated variables were used for LNC inversion. The R2 values were 0.891, 0.815, and 0.907 for 355, 420, and 556 nm ELWs, respectively. In addition, the correlation between the ratio of FDFS and LNC was also analyzed, which can provide a reference for the selection of optimal wavelengths for LNC monitoring. The experimental results exhibited the promising potential of FDFS combined with multivariate analysis for LNC monitoring, which can allow additional fluorescence characteristics to improve the accuracy of LNC monitoring.
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
Nitrogen (N) is the most desired nutrient for plant growth, favorably affecting the growth and quality of crops. Leaf nitrogen concentration (LNC) is a major indicator of the N nutrition in plants, and scientific N fertilization measurements can be obtained by monitoring the LNC in crops [1,2]. Therefore, the rapid and non-destructive monitoring of crop’s LNC is significant for the accurate diagnosis and quality evaluation of plant growth status [3,4]. However, traditional method, which are based on chemical analysis, for N nutrition monitoring cause severe damage to crops and are thus unsuitable for large-scale application. As an alternative monitoring method, remote sensing technology has become an important tool for monitoring plant growth and quantifying the biophysical and biochemical properties of plants at the leaf, canopy, and landscape levels [5–8].
To establish a more accurate and practical plant N estimation model, numerous researchers have done amount of studies on hyperspectral remote sensing and found that a certain difference exists among the sensitive bands of LNC of different crops [9–11]. Moreover, specific prediction bands will also change in the different growth stages of crops [12]. Therefore, chlorophyll fluorescence was proposed and utilized for monitoring crop growth status. Chlorophyll fluorescence is that chlorophyll can ray part of its absorbed energy at longer wavelengths when it is exposed to photons of a certain wavelength.
Currently, fluorescence technology mainly includes fast fluorescence kinetics [13], laser-induced fluorescence transient (LIFT) [14,15], and laser-induced fluorescence (LIF) [16]. Fast fluorescence kinetics is that fluorescence intensity changes with time at a certain wavelength. These fluorescence parameters can be used to analyze photosystem I (PSI) and II (PSII) photochemical parameters [17–19]. However, this method cannot be used in large-scale application because it requires that leaf sample need to be dark adapted 15 min in a dark box before to the measurements [20]. LIFT is the pulsed laser excitation signal with a variable duty cycle is used to both manipulate the level of photosynthetic activity and to measure the corresponding changes in the chlorophyll fluorescence yield [21]. LIFT is widely used in monitoring the biochemical parameters and photosynthetic performance of plants [22]. LIF is that the fluorescence spectrum can be obtained by using a laser to excite. Compared with fast fluorescence kinetics and LIFT, LIF spectra included more spectral information. Different nutrient stresses will result in different fluorescence intensity [18]. Owing to its rapid, non-destructive, and highly sensitivity properties, LIF technology has been extensively employed in monitoring N fertilizer levels in crops [8,13,23,24].
For LIF technology, Gunther et al. studied the performance of LIF for remote sensing monitoring vegetation status and pointed out that fluorescence ratio F685/F730 is only dependent on chlorophyll concentration [25]. Subhash and Mohanan reported that the laser-induced red chlorophyll fluorescence spectrum (650nm-800nm) can be served as nutrient stress indicator in rice [26]. Gu et al. discussed the effect of the flooding and waterlogging stress on the fluorescence parameters [27]. Anderson et al. used LIF spectra to assess the crop yield of cowpea (Vigna unguiculata (L) Walp) and found that the fluorescence parameters were sensitive to the change in photosynthetic activity [28]. Yang et al. detailly studied the performance of fluorescence parameters combined with multivariate analysis in the monitoring of nitrogen stress in paddy rice [29,30]. In addition, the estimation of LNC on the basis of the reflectance spectrum combined with fluorescence parameters was also discussed [31,32]. Nevertheless, most of the current studies focused on fluorescence parameters by analyzing fluorescence spectrum alone. The research on the performance of the first-derivative fluorescence spectrum (FDFS) and calculated parameters on the basis of the FDFS for estimating LNC remains limited.
In this study, the FDFS of paddy rice was calculated on the basis of fluorescence spectra which were excited by three kinds of excitation light wavelengths (ELWs). Then, the correlation between the FDFS and LNC was discussed, and back-propagation neural network (BPNN) was used for the estimation of the LNC according to the FDFS. Principal components analysis (PCA) combined with BPNN was utilized for the extraction of the main characteristics of FDFS and was used for LNC estimation. Additionally, the correlation between the FDFS ratio and LNC was analyzed, which can afford additional fluorescence characteristics to improve the accuracy of LNC monitoring.
2. Materials and experiment
2.1 Study areas and experimental design
The experimental area was located in Wuhan City and Wuxue City in the province of Hubei, China, respectively. The variety of paddy rice were Yangliangyou 6 and Victory Indica, which were sown on April 30, 2015, and April 27, 2016, respectively. Four (0, 120, 180, and 240 kg/ha) and two (0, and 150 kg/ha) N fertilization levels of urea were used in the experimental fields during the entire growth period in 2015 and 2016, respectively. The most optimal doses of N fertilization were 180 and 150 kg/ha in 2015 and 2016, respectively, in according with the advice of the local farm extension service. Each experimental area had an absolute block design with three replications under the same cultivation conditions. Furthermore, other management processes were conducted according to the advice of the local farm extension service. Paddy rice foliar samples, which were the second leaves from the top, were fully expanded, and were destructively sampled by randomly cutting nine leaves with three replicates for each experimental field, then there are 81 foliar samples for each fertilization level and the total samples number is 486. These samples were sealed in plastic sacks, stored in an ice chest, and immediately transported to the laboratory for fluorescence spectra measurements [9]. Foliar samples were collected on July 26, 2015 and July 23, 2016, which corresponded to the tillering stage of rice.
2.2 Measurement of fluorescence spectra
The fluorescence spectra were excited by different ELWs and then measured by using the LIF system developed in our laboratory [29]. Due to the spectral ranges of chlorophyll fluorescence main located from 650 to 800 nm, then three ELWs (355, 420 and 556 nm) were selected to induce chlorophyll fluorescence in this study. A neodymium-doped yttrium aluminum garnet laser emits 355 nm laser, which responds to the UV bands. The 460 and 556 nm lasers were made by Spectra-Physics and represented the blue and green bands, respectively. All fluorescence spectra were normalized to eliminate the influence of the geometry of the optical fiber and system. The fluorescence spectral ranges of 355, 460 and 556 nm ELWs were 360-800 nm, 640-790 nm, and 640-800 nm, respectively. The sample interval of the fluorescence spectra was 0.5 nm. All foliar samples were shortly sent to the Wuhan Academy of Agricultural Science and Technology for LNC measurement after the fluorescence spectra measurement. In this study, LNC was obtained through the Kjeldahl method [3].
2.3 First-derivative calculation
According to the concepts of first-derivative and previous research [33], the FDFS at was calculated from the difference between the values at each wavelength, plus and minus one band, divided by the range of wavelength. FDFS () can be written as:
where represents the excitation wavelength, and are the fluorescence intensities at emission fluorescence wavelengths and , respectively.
3. Analytical methods
3.1 Principal component analysis
PCA is a statistical multivariate analysis method that can efficiently reduce spectral dimensionality by extracting the main spectral feature variables [34]. That is the number of original variables can be reduced by removing lower-level components without any notable loss of information from in the original data set by PCA. The calculated variables, which are called principal components (PC), were obtained by the linear combinations of the original variables [35].
where, Yi corresponds to the measured values at ith wavelength, Xj represents the PC, is the sum of the kth PC for the ith wavelength, and P denotes the loadings weight of the latent variables. Then, the analysis process can be efficiently simplified by using fewer calculated variables than the original ones [36]. FDFS usually contains many irrelevant, redundant information. Therefore, the FDFS was analyzed by using PCA in this study.
3.2 Back-propagation neural network
BPNN is an efficient supervised classification method for solving various nonlinear problems. It can build the correlation among inner neural units by a series of trials, with respect to multiple tasks and is trained by repeatedly providing a series of input and output pattern sets to the neural network. Generally, BPNN includes three layers, namely, the input, hidden, and output layers. It has been widely used in biological and agricultural study, pattern recognition for classification or revision [37,38]. A brief introduction about BPNN can be referenced [38,39]. A three layers back-propagation neural network was applied in this work to model rice LNC and fluorescence characteristics. The training function was selected “trainlm” and the hidden layer sizes was set as three.
3.3 Statistical parameters
The FDFS characteristics of each ELW were randomly divided into two data sets: 70% as the training data set and another 30% as the validation set for LNC prediction. In this study, the coefficient of determination (R2), root mean square error (RMSE), and relative error (RE) were used to assess the performance of the BPNN model based on different characteristic parameters. The RMSE and RE were calculated as follows:
where Mi, and Pi represents the measured and predicted paddy rice LNC, respectively; is the average measured; and n denotes the number of samples. Low RMSE and RE and high R2 represent a good performance of estimation model in monitoring the LNC [4]. In this study, MATLAB R2015b (Mathworks Inc., Natick, MA, USA), where many regression toolboxes are available, was applied to perform all data PCA calculation, BPNN model building and evaluation.
4. Results and discussion
4.1 First-derivation fluorescence spectrum
The FDFS values were calculated through Eq. (1) for different ELWs and are shown in Fig. 1.
Fig. 1 First-derivative fluorescence spectrum of paddy rice leaf with different excitation light wavelengths (355, 460, and 556 nm).
As shown in Fig. 1, the number of FDFS characteristic peak more than that the fluorescence spectra. The black solid line corresponds to the 355 nm ELW, which showed more characteristics than that other ELWs. The reason is that chlorophyll fluorescence mainly contains two fluorescence peaks: one at 680-690 nm, and the other at 735-745 nm. Thus, fluorescence spectral characteristics are limited to the analysis of nutrition stress in crops. However, the characteristics peaks of FDFS are mainly located at 660, 690, 720 and 750 nm for different ELWs. These characteristics denote the fastest changing position. The FDFS values changed from positive to negative denotes the position of fluorescence crest, and whereas that from negative to positive corresponds to the position of fluorescence valley. In the investigation of the fluorescence spectrum, the fluorescence valley is usually disregarded in LNC estimation. Thus, the FDFS shows more fluorescence characteristics for studying the biochemical content of leaves than the fluorescence spectrum.
4.2 Correlation between the FDFS and LNC
To analyze the performance of the FDFS for LNC estimation, the correlation between LNC and FDFS at each wavelength were established for different ELWs. The correlation coefficients between LNC and FDFS for different ELWs are shown in Fig. 2.
Fig. 2 Correlation coefficients between the leaf nitrogen concentration and first-derivative fluorescence spectrum with different excitation light wavelengths. (A): 355 nm, (B): 460 nm, (C): 556 nm.
As shown in Fig. 2, the fluorescence characteristics of FDFS displayed a significant correlation with the LNC. The sensitive bands in the first-derivative of fluorescence spectrum are mainly concentrated within the red regions at 650-800 nm for the three ELWs. The results demonstrated that the sensitive bands are mainly concentrated near 660, 695, 710, and 750 nm for the 355 nm excitation light shown in Fig. 2(A), the sensitive bands are located near 660, 710, and 755 nm for the 460 nm excitation light shown in Fig. 2(B), and the sensitive bands are located near 675, and 710 nm for the 556 nm excitation light shown in Fig. 2(C). Thus, the results demonstrate that the FDFS can be further analysis and used for LNC estimate.
4.3 Correlation between the ratio of FDFS and LNC
For the analysis of the optimal characteristic bands of FDFS for the estimation of paddy rice LNC, further analysis was performed on the correlation between FDFS ratios and LNC by using data sets with different ELWs, as shown in Fig. 3.
Fig. 3 Equipotential graphs of coefficient of determination between leaf nitrogen concentration and the ratio of the first-derivative fluorescence spectrum with different excitation light wavelengths. (A): 355 nm; (B): 460 nm; (C): 556 nm.
Figure 3 presents the equipotential graphs of R2 between the FDFS ratios and LNC with the two wavelengths on the abscissa and vertical axis for different ELWs. An overview of the statistical consequence for all FDFS ratios was also provided. As shown in Fig. 3, the correlation between the fluorescence emission wavelengths at the characteristic peaks and LNC has a higher R2 than those of the other wavelength bands. Thus, the characteristics of FDFS main focus on the red regions which can be extracted for the estimation of LNC in paddy rice.
4.4 Estimation of LNC based on FDFS
To analyze the predictive ability of the FDFS for monitoring LNC in paddy rice, the BPNN algorithm was used on the basis of the FDFS to inverse LNC. The calculated FDFS were randomly divided into two data sets: 70% (n = 340) was used as the train data set and the other 30% (n = 146) was served as the validation data set. In the BPNN model, the FDFS served as input parameter for the training of the model, and the LNC corresponded to the output parameters. Then, the remaining data set was utilized for the validation of the trained model. The relationship between the measured and predicted LNC were established by linear regression analysis and are illustrated in Fig. 4.
Fig. 4 Relationship between the predicted LNC using BPNN based on FDFS and the measured LNC for different excitation light wavelengths. (A): 355 nm, (B): 460 nm, (C): 556 nm. The red solid line represents the linear regression.
The results of the comparison among the R2 values in Fig. 4 shows that the FDFS exhibits a promising potential for revising LNC and the overall R2 exceeds 0.74. The red solid line denotes the linear regression analysis results between the predicted and measured LNC. For the 355 nm (R2 = 0.823) and 556 nm (R2 = 0.837) excitation light, the inversion results show better predictive ability, having higher R2 and lower RMSE and RE than that of the 460 nm (R2 = 0.743) excitation light. The main reason may be that the FDFS with the 355 nm excitation light contains more fluorescence characteristics (360-800 nm) than that other excitation lights. The 556 nm excitation light can penetrate deeper in the leaf tissue than the other ELWs.
4.5 Estimation of LNC based on PCA
PCA was used for the analysis of the internal correlation and reduction of the dimensionality of FDFS. Most of significant characteristic variables were extracted because the FDFS contains amounts of verbose information which may influence on the its performance for the estimation of LNC. Then, the calculated variables served as input parameters for the training of the BPNN model. The scatter plots between the measured and predicted LNC were established by linear regression analysis and are illustrated in Fig. 5.
Fig. 5 Relationship between the predicted LNC using PCA combined with BPNN and the measured LNC for different excitation light wavelengths. (A): 355 nm, (B): 460 nm, (C): 556 nm. The red solid line represents the linear regression.
As shown in Fig. 5, the performance of new variables calculated through PCA for monitoring LNC was analyzed for different ELWs. The solid line represents the linear regression analysis that denotes the correlation between the predicted and measured LNC. The inversion results demonstrate that PCA can efficiently extract the characteristics of FDFS for LNC estimation by comparing the R2, RMSE, and RE values. All the R2 values exceed 0.8 and reach 0.90. Therefore, the experimental results demonstrate that the proposed FDFS combined with the multivariate analysis can be efficiently used to monitor the paddy rice LNC, and the 355 nm and 556 nm excitation lights is superior to 460 nm excitation light for LNC monitoring based on LIF technology.
5. Discussion
LNC is served as a significant indicator for monitoring the growth status of crops and has been widely studied by numerous researchers using passive and active remote sensing technologies. The genotype of different crops varieties is different and will result in the different nutrient requirements for crops growth. In addition, unequal phenology may influence on the crops growth status. These factors may influence on the biochemical content of crops which lead to different spectral characteristics. Plant fluorescence emitted by the chlorophyll in leaf which is closely related to photosynthetic pigments. Fluorescence technology has been widely utilized in analyzing the photosynthetic mechanism and process [40,41]. At present, numerous investigations have been conducted on fast fluorescence kinetics [13,42], LIFT [14], and LIF [16]. In this study, we mainly discussed the performance of FDFS combined with multivariate analysis in the estimation of LNC in paddy rice.
The plant chlorophyll fluorescence mainly includes two fluorescence characteristic peaks at 685 and 740 nm [43,44]. The FDFS displays more characteristic peaks than the fluorescence spectrum in the estimation of the biochemical content of leaves. The possible reason is that the characteristic peak of FDFS appears when the change rate of fluorescence intensity changes, which can be calculated with Eq. (1). Thus, FDFS has at least four characteristic peaks for chlorophyll fluorescence. Moreover, additional fluorescent spectral details may be obtained through the first-derivative process shown in Fig. 1. The zero position of the first-derivative denotes the fluorescence characteristic peak. Thus, FDFS was a new kind of characteristic variable that is based on LIF, and its performance in LNC estimation was analyzed in this study.
The correlation between the LNC and FDFS at each wavelength was analyzed. In the 355 nm ELW, a certain correlation was observed at the blue and green regions (450-530 nm) possibly because fluorescence characteristics are related to xanthophyll, which may be affected by LNC [45]. A negative correlation was observed between the LNC and FDFS near 660nm for the 460 and 556 nm ELWs, and a positive correlation was obtained for the 355 nm ELW. The results may be attributed to the increase in fluorescence intensity with the increasing of LNC for the 355 nm ELW. An opposite trend was observed for the 460 and 556 nm ELWs [29]. The negative or positive correlation shows that the change rate of fluorescence intensity, which is related to LNC and can be served as a characteristic variable for estimating LNC. The ratio of the red bands and blue bands exhibited higher a correlation than the other band ratios for the 355 nm ELW shown in Fig. 3(A). For the 460 and 556 nm ELWs, the ratio near 750 nm and near 670 nm displayed higher a correlation than the other ratios shown in Figs. 3(B) and 3(C). Thus, the FDFS characteristics mainly focus on the red region, which is related to chlorophyll concentration.
To analyze the performance of the FDFS characteristics for LNC estimation, the FDFS was served as input parameter for the training of the BPNN model for LNC estimation in this study. The FDFS displayed different predictive performance in LNC estimation for different ELWs, as indicated by their R2, RMSE, and RE values. As indicated by the inversion results, the 355 nm and 556 nm ELWs showed better predictive ability with high R2 and low RMSE and RE than that the 460 nm ELW possibly because the FDFS excited by the 355 nm laser contained more fluorescence characteristics than the other ELWs shown in Fig. 1, and the 556 nm excitation light has a stronger penetration ability in the leaf tissue than the other ELWs [46–48]. The green-excited fluorescence receives contributions from deeper layers than the ultraviolet-excited and blue-excited fluorescence [48]. Thus, when 355 and 556 nm lasers were served as excitation light sources for the acquisition of FDFS, and approximate accuracies of R2 = 0.823 and R2 = 0.837 were obtained during LNC estimation shown in Fig. 4.
The sample interval of FDFS was 0.5nm and may have contained many verbose information and influenced the precision of LNC estimation. Then, PCA was used for the analysis of the FDFS and extraction of the main characteristics through the reduction of the dimensionality of the data sets. The revision results shown in Fig. 5 displayed a better LNC estimation performance (R2 = 0.891, 0.815, 0.907 are correspond to 355, 460, and 556 nm ELWs, respectively.) than the results shown in Fig. 4 in which FDFS was used as input parameter (R2 = 0.823, 0.743, 0.837 correspond to 355, 460, and 556 nm ELWs, respectively). The possible reason is that the multivariate analysis efficiently reduced the effect of data autocorrelation on BPNN model [35]. The experimental results demonstrated that multivariate analysis can be efficiently used for analyzing FDFS by improving the revision precision in LNC estimation.
In this study, the use of FDFS was proposed for the estimation of paddy rice LNC through BPNN and PCA. A detailed analysis of the correlation between FDFS and LNC was conducted, and the performance of FDFS in LNC estimation was discussed. However, some limitations should be considered for further studies. For the FDFS, the effect of FDFS oscillation on the accuracy of LNC estimation should be analyzed in the following work. In the BPNN model, the optimal training approaches and network architecture were not analyzed. The results obtained through the use of BPNN were trained by using our experiences and limited data set. Ideally, the optimal situation should be compared by varying the numbers of network architectures and hidden neurons. Therefore, further studies should be conducted on the effectiveness of the BPNN in LNC estimation. Moreover, the effect of different algorithms on revision precision should also be analyzed [49]. To improve the robustness of the inversion model and to obtain a solid conclusion, different growth seasons, suboptimal condition, paddy rice cultivars, and other crops should also be discussed in future research [36].
6. Conclusion
In conclusion, the use of FDFS was proposed for the monitoring of paddy rice LNC in this study. Then, the performance of FDFS in LNC estimation was analyzed in detail for three different ELWs (355, 420, and 556 nm) through the combination of BPNN with PCA. In the inversion results on the basis of the FDFS, the R2 values of the linear regression analysis between the measured and predicted LNC were 0.823, 0.743, and 0.837 correspond to 355, 420, and 556 nm ELWs, respectively. Moreover, the combination of PCA with BPNN was performed for the prediction of LNC based on FDFS. The R2 values of linear regression analysis between the measured and predicted LNC were 0.891, 0.815 and 0.907 for 355, 420, and 556 nm ELWs, respectively. The experimental results showed the promising potential of the FDFS combined with multivariate analysis for the accurate LNC monitoring, and the 355 nm and 556 nm excitation lights are superior to 460 nm excitation light for LNC monitoring by using LIF technology. Nevertheless, to obtain a solid conclusion, studies using other algorithm, rice cultivars, and other crops are still necessary in the following works.
Funding
National Key R&D Program of China (2018YFB0504500), the National Natural Science Foundation of China (41801268), the Natural Science Foundation of Hubei Province (2018CFB272), the Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University (17R05), Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUG170661).
References
1. F. Li, B. Mistele, Y. Hu, X. Chen, and U. Schmidhalter, “Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression,” Eur. J. Agron. 52, 198–209 (2014). [CrossRef]
2. C. Gameiro, A. Utkin, P. Cartaxana, J. M. da Silva, and A R.. Matos, “The use of laser induced chlorophyll fluorescence (LIF) as a fast and non‑destructive method to investigate water deficit in Arabidopsi,” Agr. Water Manage. 164, 127–136 (2016). [CrossRef]
3. Y. C. Tian, X. Yao, J. Yang, W. X. Cao, D. B. Hannaway, and Y. Zhu, “Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance,” Field Crops Res. 120(2), 299–310 (2011). [CrossRef]
4. W. Feng, X. Yao, Y. Zhu, Y. Tian, and W. Cao, “Monitoring leaf nitrogen status with hyperspectral reflectance in wheat,” Eur. J. Agron. 28(3), 394–404 (2008). [CrossRef]
5. G. Cecchi, P. Mazzinghi, L. Pantani, R. Valentini, D. Tirelli, and P. De Angelis, “Remote sensing of chlorophyll a fluorescence of vegetation canopies: 1. Near and far field measurement techniques,” Remote Sens. Environ. 47(1), 18–28 (1994). [CrossRef]
6. P. J. Zarco-Tejada, C. A. Rueda, and S. L. Ustin, “Water content estimation in vegetation with MODIS reflectance data and model inversion methods,” Remote Sens. Environ. 85(1), 109–124 (2003). [CrossRef]
7. D. Stroppiana, M. Boschetti, P. A. Brivio, and S. Bocchi, “Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry,” Field Crops Res. 111(1-2), 119–129 (2009). [CrossRef]
8. M. P. Cendrero-Mateo, M. S. Moran, S. A. Papuga, K. R. Thorp, L. Alonso, J. Moreno, G. Ponce-Campos, U. Rascher, and G. Wang, “Plant chlorophyll fluorescence: active and passive measurements at canopy and leaf scales with different nitrogen treatments,” J. Exp. Bot. 67(1), 275–286 (2016). [CrossRef] [PubMed]
9. S. Song, W. Gong, B. Zhu, and X. Huang, “Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance,” ISPRS J. Photogramm. 66(5), 672–682 (2011). [CrossRef]
10. M. Diacono, P. Rubino, and F. Montemurro, “Precision nitrogen management of wheat. A review,” Agron. Sustain. Dev. 33(1), 219–241 (2013). [CrossRef]
11. L. He, X. Song, W. Feng, B.-B. Guo, Y.-S. Zhang, Y.-H. Wang, C.-Y. Wang, and T.-C. Guo, “Improved remote sensing of leaf nitrogen concentration in winter wheat using multi-angular hyperspectral data,” Remote Sens. Environ. 174, 122–133 (2016). [CrossRef]
12. S. L. Osborne, J. S. Schepers, D. D. Francis, and M. R. Schlemmer, “Detection of Phosphorus and Nitrogen Deficiencies in Corn Using Spectral Radiance Measurements,” Agron. J. 94(6), 1215–1221 (2002). [CrossRef]
13. H. M. Kalaji, G. Schansker, M. Brestic, F. Bussotti, A. Calatayud, L. Ferroni, V. Goltsev, L. Guidi, A. Jajoo, P. Li, P. Losciale, V. K. Mishra, A. N. Misra, S. G. Nebauer, S. Pancaldi, C. Penella, M. Pollastrini, K. Suresh, E. Tambussi, M. Yanniccari, M. Zivcak, M. D. Cetner, I. A. Samborska, A. Stirbet, K. Olsovska, K. Kunderlikova, H. Shelonzek, S. Rusinowski, and W. Bąba, “Frequently asked questions about chlorophyll fluorescence, the sequel,” Photosynth. Res. 132(1), 13–66 (2017). [PubMed]
14. Z. Kolber, D. Klimov, G. Ananyev, U. Rascher, J. Berry, and B. Osmond, “Measuring photosynthetic parameters at a distance: laser induced fluorescence transient (LIFT) method for remote measurements of photosynthesis in terrestrial vegetation,” Photosynth. Res. 84(1-3), 121–129 (2005). [CrossRef] [PubMed]
15. W. Huang, Y. J. Yang, S. B. Zhang, and T. Liu, “Cyclic Electron Flow around Photosystem I Promotes ATP Synthesis Possibly Helping the Rapid Repair of Photodamaged Photosystem II at Low Light,” Front. Plant Sci. 9, 239 (2018). [CrossRef] [PubMed]
16. F. E. Hoge, R. N. Swift, and J. K. Yungel, “Feasibility of airborne detection of laser-induced fluorescence emissions from green terrestrial plants,” Appl. Opt. 22(19), 2991–3000 (1983). [CrossRef] [PubMed]
17. H. M. Kalaji, A. Oukarroum, V. Alexandrov, M. Kouzmanova, M. Brestic, M. Zivcak, I. A. Samborska, M. D. Cetner, S. I. Allakhverdiev, and V. Goltsev, “Identification of nutrient deficiency in maize and tomato plants by in vivo chlorophyll a fluorescence measurements,” Plant Physiol. Biochem. 81, 16–25 (2014). [CrossRef] [PubMed]
18. M. Živčák, K. Olšovská, P. Slamka, J. Galambošová, V. Rataj, H. B. Shao, and M. Brestič, “Application of chlorophyll fluorescence performance indices to assess the wheat photosynthetic functions influenced by nitrogen deficiency,” Plant Soil Environ. 60(5), 210–215 (2014). [CrossRef]
19. M. Zivcak, M. Brestic, K. Kunderlikova, K. Olsovska, and S. I. Allakhverdiev, “Effect of photosystem I inactivation on chlorophyll a fluorescence induction in wheat leaves: Does activity of photosystem I play any role in OJIP rise?” J. Photochem. Photobiol. B 152(Pt B), 318–324 (2015). [CrossRef] [PubMed]
20. M. Zivcak, M. Brestic, K. Kunderlikova, O. Sytar, and S. I. Allakhverdiev, “Repetitive light pulse-induced photoinhibition of photosystem I severely affects CO2 assimilation and photoprotection in wheat leaves,” Photosynth. Res. 126(2-3), 449–463 (2015). [CrossRef] [PubMed]
21. Z. Kolber, D. Klimov, G. Ananyev, U. Rascher, J. Berry, and B. Osmond, “Measuring photosynthetic parameters at a distance: laser induced fluorescence transient (LIFT) method for remote measurements of photosynthesis in terrestrial vegetation,” Photosynth. Res. 84(1-3), 121–129 (2005). [CrossRef] [PubMed]
22. A. Raesch, O. Muller, R. Pieruschka, and U. Rascher, “Field Observations with Laser-Induced Fluorescence Transient (LIFT) Method in Barley and Sugar Beet,” Agriculture 4(2), 159–169 (2014). [CrossRef]
23. S. Apostol, A. A. Viau, and N. Tremblay, “A comparison of multiwavelength laser-induced fluorescence parameters for the remote sensing of nitrogen stress in field-cultivated corn,” Can. J. Rem. Sens. 33(3), 150–161 (2007). [CrossRef]
24. J. Yang, W. Gong, S. Shi, L. Du, J. Sun, Y.-Y. Ma, and S.-L. Song, “Accurate identification of nitrogen fertilizer application of paddy rice using laser-induced fluorescence combined with support vector machine,” Plant Soil Environ. 61(11), 501–506 (2015). [CrossRef]
25. K. Günther, H.-G. Dahn, and W. Lüdeker, “Remote sensing vegetation status by laser-induced fluorescence,” Remote Sens. Environ. 47(1), 10–17 (1994). [CrossRef]
26. N. Subhash and C. N. Mohanan, “Laser-induced red chlorophyll fluorescence signatures as nutrient stress indicator in rice plants,” Remote Sens. Environ. 47(1), 45–50 (1994). [CrossRef]
27. X. Gu, P. Xu, H. Qiu, and H. Feng, “Monitoring the chlorophyll fluorescence parameters in rice under flooding and waterlogging stress based on remote sensing,” in World Automation Congress, 848–854 (2014).
28. B. Anderson, P. K. Buah-Bassuah, and J. P. Tetteh, “Using violet laser-induced chlorophyll fluorescence emission spectra for crop yield assessment of cowpea (Vigna unguiculata (L) Walp) varieties,” Meas. Sci. Technol. 15(7), 1255–1265 (2004). [CrossRef]
29. J. Yang, J. Sun, L. Du, B. Chen, Z. Zhang, S. Shi, and W. Gong, “Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laser-induced fluorescence lidar in paddy rice,” Opt. Express 25(4), 3743–3755 (2017). [CrossRef] [PubMed]
30. J. Yang, W. Gong, S. Shi, L. Du, J. Sun, S. Song, B. Chen, and Z. Zhang, “Analyzing the performance of fluorescence parameters in the monitoring of leaf nitrogen content of paddy rice,” Sci. Rep. 6, 28787 (2016). [CrossRef] [PubMed]
31. J. McMurtrey III, E. Chappelle, M. Kim, J. Meisinger, and L. Corp, “Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements,” Remote Sens. Environ. 47(1), 36–44 (1994). [CrossRef]
32. J. Yang, L. Du, W. Gong, S. Shi, J. Sun, and B. Chen, “Potential of vegetation indices combined with laser-induced fluorescence parameters for monitoring leaf nitrogen content in paddy rice,” PLoS One 13(1), e0191068 (2018). [CrossRef] [PubMed]
33. B. J. Yoder and R. E. Pettigrew-Crosby, “Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales,” Remote Sens. Environ. 53(3), 199–211 (1995). [CrossRef]
34. L. S. Galvão, M. A. Pizarro, and J. C. N. Epiphanio, “Variations in reflectance of tropical soils: spectral-chemical composition relationships from AVIRIS data,” Remote Sens. Environ. 75(2), 245–255 (2001). [CrossRef]
35. R. Bro and A. K. Smilde, “Principal component analysis,” Anal. Methods 6(9), 2812–2831 (2014). [CrossRef]
36. Q.-X. Yi, J.-F. Huang, F.-M. Wang, X.-Z. Wang, and Z.-Y. Liu, “Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network,” Environ. Sci. Technol. 41(19), 6770–6775 (2007). [CrossRef] [PubMed]
37. V. Goltsev, I. Zaharieva, P. Chernev, M. Kouzmanova, H. M. Kalaji, I. Yordanov, V. Krasteva, V. Alexandrov, D. Stefanov, S. I. Allakhverdiev, and R. J. Strasser, “Drought-induced modifications of photosynthetic electron transport in intact leaves: analysis and use of neural networks as a tool for a rapid non-invasive estimation,” Biochim. Biophys. Acta 1817(8), 1490–1498 (2012). [CrossRef] [PubMed]
38. A. I. Samborska, V. Alexandrov, L. Sieczko, B. Kornatowska, V. Goltsev, D. C. Magdalena, and H. M. Kalaji, “Artificial neural networks and their application in biological and agricultural research,” J. NanoPhotoBioSciences 2, 14–30 (2014).
39. L. E. Keiner and X.-H. Yan, “A neural network model for estimating sea surface chlorophyll and sediments from thematic mapper imagery,” Remote Sens. Environ. 66(2), 153–165 (1998). [CrossRef]
40. R. Pieruschka, D. Klimov, Z. S. Kolber, and J. A. Berry, “Monitoring of cold and light stress impact on photosynthesis by using the laser induced fluorescence transient (LIFT) approach,” Funct. Plant Biol. 37(5), 395–402 (2010). [CrossRef]
41. H. M. Kalaji, A. Jajoo, A. Oukarroum, M. Brestic, M. Zivcak, I. A. Samborska, M. D. Cetner, I. Łukasik, V. Goltsev, and R. J. Ladle, “Chlorophyll a fluorescence as a tool to monitor physiological status of plants under abiotic stress conditions,” Acta Physiol. Plant. 38(4), 102 (2016). [CrossRef]
42. X. Zhou, C. Sun, P. Zhu, and F. Liu, “Effects of Antimony Stress on Photosynthesis and Growth of Acorus calamus,” Front. Plant Sci. 9, 579 (2018). [CrossRef] [PubMed]
43. N. Tremblay, Z. Wang, and Z. G. Cerovic, “Sensing crop nitrogen status with fluorescence indicators. A review,” Agron. Sustain. Dev. 32(2), 451–464 (2012). [CrossRef]
44. E. W. Chappelle, F. M. Wood, J. E. McMurtrey, and W. W. Newcomb, “Laser-induced fluorescence of green plants. 1: A technique for the remote detection of plant stress and species differentiation,” Appl. Opt. 23(1), 134–138 (1984). [CrossRef] [PubMed]
45. N. Subhash, O. Wenzel, and H. K. Lichtenthaler, “Changes in blue-green and chlorophyll fluorescence emission and fluorescence ratios during senescence of tobacco plants,” Remote Sens. Environ. 69(3), 215–223 (1999). [CrossRef]
46. J. Schweiger, M. Lang, and H. K. Lichtenthaler, “Differences in Fluorescence Excitation Spectra of Leaves between Stressed and Non-Stressed Plants,” J. Plant Physiol. 148(5), 536–547 (1996). [CrossRef]
47. M. E. Ramos and M. G. Lagorio, “True fluorescence spectra of leaves,” Photochem. Photobiol. Sci. 3(11-12), 1063–1066 (2004). [CrossRef] [PubMed]
48. G. Agati, “Response of the in vivo chlorophyll fluorescence spectrum to environmental factors and laser excitation wavelength,” Pure Appl. Opt. 7(4), 797–807 (1998). [CrossRef]
49. J. Wang, T. Wang, A. K. Skidmore, T. Shi, and G. Wu, “Evaluating Different Methods for Grass Nutrient Estimation from Canopy Hyperspectral Reflectance,” Remote Sens. 7(5), 5901–5917 (2015). [CrossRef]
