Investigation of laser-induced fluorescence of several natural leaves for application to lidar vegetation monitoring

Utilizing laser spectrochemical analytical methods for assessing the ripening progress of tomato

To meet market demands and minimize losses, the tomato crop (Solanum Lycopersicum L.) requires a simple, rapid, and cost-effective method to distinguish between different maturity stages with high accuracy. This study aimed at evaluating two spectrochemical analytical techniques, namely laser-induced fluorescence (LIF) and laser-induced breakdown spectroscopy (LIBS), to discriminate three different maturity stages of tomato fruit (‘Green/Breaker’; ‘Turning/Pink’; and ‘Light-red/Red’). The simple linear regression confirmed the obtained LIF results with chlorophyll content (mg/100 g), hue angle (h°), and firmness (kg/cm2) of the different maturity stages (measured by conventional methods). Furthermore, the findings showed that the peak intensities of LIF spectra decreased with the chlorophyll content depletion during ripening. Moreover, the data exposed a reasonably good association between LIF spectra and chlorophyll content with a regression coefficient of 0.85. On the other hand, firmness and skin hue have shown an excellent predictor for the spectra with a high regression coefficient of 0.94. For LIBS spectra of each maturity stage, the ratios of Ca’s ionic-to-atomic spectral lines intensities have followed the same trend as conventionally measured firmness. The results demonstrated that LIF and LIBS are accurate, easy, and fast techniques used to define tomatoes’ different ripening stages. Both methods are useable in situ without any prior laboratory work.

Quantitative Detection of Extra Virgin Olive Oil Adulteration, as Opposed to Peanut and Soybean Oil, Employing LED-Induced Fluorescence Spectroscopy

As it is high in value, extra virgin olive oil (EVOO) is frequently blended with inferior vegetable oils. This study presents an optical method for determining the adulteration level of EVOO with soybean oil as well as peanut oil using LED-induced fluorescence spectroscopy. Eight LEDs with central wavelengths from ultra-violet (UV) to blue are tested to induce the fluorescence spectra of EVOO, peanut oil, and soybean oil, and the UV LED of 372 nm is selected for further detection. Samples are prepared by mixing olive oil with different volume fractions of peanut or soybean oil, and their fluorescence spectra are collected. Different pre-processing and regression methods are utilized to build the prediction model, and good linearity is obtained between the predicted and actual adulteration concentration. This result, accompanied by the non-destruction and no pre-treatment characteristics, proves that it is feasible to use LED-induced fluorescence spectroscopy as a way to investigate the EVOO adulteration level, and paves the way for building a hand-hold device that can be applied to real market conditions in the future.

Estimating leaf chlorophyll content by laser-induced fluorescence technology at different viewing zenith angles

Leaf chlorophyll content (LCC) is a key indicator of a plant's physiological status. Fast and non-destructive monitoring of chlorophyll content in plants through remote sensing is very important for accurate diagnosis and assessment of plant growth. Through the use of laser-induced fluorescence (LIF) technology, this study aims to compare the predictive ability of different single fluorescence characteristic and fluorescence characteristic combinations at various viewing zenith angles (VZAs) combined with multivariate analysis algorithms, such as principal component analysis (PCA) and support vector machine (SVM), for estimating the LCC of plants. The SVM models of LCC estimation were proposed, and fluorescence characteristics-fluorescence peak (FP), fluorescence ratio (FR), PCA, and first-derivative (FD) parameter-and fluorescence characteristic combinations (FP+FR, FP+FD, FR+FD, FP+FR+FD) were used as input variables for the models. Experimental results demonstrated that the effect of single fluorescence characteristics on the predictive performance of SVM models was: FR>FD>FP>PCA. Compared with other models, 0° SVM was the optimal model for estimating LCC by higher R². The fluorescence spectra and FD spectra observed at 0° and 30° were superior to those observed at 15°, 45°, and 60°. Thus, appropriate VZA must also be considered, as it can improve the accuracy of LCC monitoring. In addition, compared with single fluorescence characteristic, the FP+FR+FD was the optimal combination of fluorescence characteristics to estimate the LCC for the SVM model by higher R², indicating better predictive performance. The experimental results show that the combination of LIF technology and multivariate analysis can be effectively used for LCC monitoring and has broad development prospects.

Active 3D Imaging of Vegetation based on Multi-Wavelength Fluorescence LiDAR

Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system's performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.

Estimating leaf nitrogen concentration based on the combination with fluorescence spectrum and first-derivative

Leaf nitrogen concentration (LNC) is a major indicator in the estimation of the crop growth status which has been diffusely applied in remote sensing. Thus, it is important to accurately obtain LNC by using passive or active technology. Laser-induced fluorescence can be applied to monitor LNC in crops through analysing the changing of fluorescence spectral information. Thus, the performance of fluorescence spectrum (FS) and first-derivative fluorescence spectrum (FDFS) for paddy rice (Yangliangyou 6 and Manly Indica) LNC estimation was discussed, and then the proposed FS + FDFS was used to monitor LNC by multivariate analysis. The results showed that the difference between FS (R ² = 0.781, s.d. = 0.078) and FDFS (R ² = 0.779, s.d. = 0.097) for LNC estimation by using the artificial neural network is not obvious. The proposed FS + FDFS can improved the accuracy of LNC estimation to some extent (R ² = 0.813, s.d. = 0.051). Then, principal component analysis was used in FS and FDFS, and extracted the main fluorescence characteristics. The results indicated that the proposed FS + FDFS exhibited higher robustness and stability for LNC estimation (R ² = 0.851, s.d. = 0.032) than that only using FS (R ² = 0.815, s.d. = 0.059) or FDFS (R ² = 0.801, s.d. = 0.065).

Selection of the optimal bands of first-derivative fluorescence characteristics for leaf nitrogen concentration estimation

Laser-induced fluorescence technology provides a nondestructive and rapid method for monitoring leaf nitrogen concentration (LNC) based on its optical characteristics. Crop growth status can be efficiently diagnosed and quality evaluated by monitoring LNC. In this study, the first-derivative fluorescence spectrum (FDFS) was proposed and calculated based on the fluorescence spectra excited by 355, 460, and 556 nm excitation lights for rice LNC estimation. Then, the performance of each band FDFS characteristics and the FDFS ratio for LNC estimation were comprehensively discussed using principal component analysis and backpropagation neural network (BPNN). We analyzed the number of FDFS characteristics' influence on the accuracy of LNC monitoring. Results showed that R² does not clearly improve for the LNC monitoring based on the BPNN model when the number of extracted FDFS features exceeds 4 or 5. Therefore, the FDFS optimal band combination of different excitation light wavelengths mentioned was selected for LNC monitoring. The selected band combinations contained the majority of FDFS characteristics and could effectively be applied in monitoring LNC (for 355, 460, and 556 nm excitation lights, with R² of 0.764, 0.625, and 0.738, respectively) based on the BPNN model.

Analyzing the effect of the incidence angle on chlorophyll fluorescence intensity based on laser-induced fluorescence lidar

Laser-induced fluorescence (LIF) technology has been widely applied to monitor vegetation growth status and biochemical concentrations. Thus, it is important to accurately acquire the fluorescence information for the quantitative monitoring of vegetation growth status. In this study, firstly, the incidence angle's effect on chlorophyll fluorescence intensity was analyzed by using the FluorMODleaf model. Then, comprehensive experimental data on the angle dependence of the fluorescence intensity to vegetation leaf surface were collected. Numerical and experimental results showed that proposed corrected cosine expression could be used to describe the relationship between the incidence angle and the fluorescence intensity in the LIF-Lidar. Lastly, fluorescence signals at 685 and 740 nm extracted at different incident angles of excitation lights were fitted with the corrected cosine expression. The coefficient of determination (R²) of the fitting results reached a maximum value of 0.93 for Salix babylonica.

Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation

Leaf nitrogen concentration (LNC) is a significant indicator of crops growth status, which is related to crop yield and photosynthetic efficiency. Laser-induced fluorescence is a promising technology for LNC estimation and has been widely used in remote sensing. The accuracy of LNC monitoring relies greatly on the selection of fluorescence characteristics and the number of fluorescence characteristics. It would be useful to analyze the performance of fluorescence intensity and ratio characteristics at different wavelengths for LNC estimation. In this study, the fluorescence spectra of paddy rice excited by different excitation light wavelengths (355 nm, 460 nm, and 556 nm) were acquired. The performance of the fluorescence intensity and fluorescence ratio of each band were analyzed in detail based on back-propagation neural network (BPNN) for LNC estimation. At 355 nm and 460 nm excitation wavelengths, the fluorescence characteristics related to LNC were mainly located in the far-red region, and at 556 nm excitation wavelength, the red region being an optimal band. Additionally, the effect of the number of fluorescence characteristics on the accuracy of LNC estimation was analyzed by using principal component analysis combined with BPNN. Results demonstrate that at least two fluorescence spectral features should be selected in the red and far-red regions to estimate LNC and efficiently improve the accuracy of LNC estimation.

Merging Unmanned Aerial Systems (UAS) Imagery and Echo Soundings with an Adaptive Sampling Technique for Bathymetric Surveys

Bathymetric surveying to gather information about depths and underwater terrain is increasingly important to the sciences of hydrology and geomorphology. Submerged terrain change detection, water level, and reservoir storage monitoring demand extensive bathymetric data. Despite often being scarce or unavailable, this information is fundamental to hydrodynamic modeling for imposing boundary conditions and building computational domains. In this manuscript, a novel, low-cost, rapid, and accurate method is developed to measure submerged topography, as an alternative to conventional approaches that require significant economic investments and human power. The method integrates two types of Unmanned Aerial Systems (UAS) sampling techniques. The first couples a small UAS (sUAS) to an echosounder attached to a miniaturized boat for surveying submerged topography in deeper water within the range of accuracy. The second uses Structure from Motion (SfM) photogrammetry to cover shallower water areas no detected by the echosounder where the bed is visible from the sUAS. The refraction of light passing through air–water interface is considered for improving the bathymetric results. A zonal adaptive sampling algorithm is developed and applied to the echosounder data to densify measurements where the standard deviation of clustered points is high. This method is tested at a small reservoir in the U.S. southern plains. Ground Control Points (GCPs) and checkpoints surveyed with a total station are used for properly georeferencing of the SfM photogrammetry and assessment of the UAS imagery accuracy. An independent validation procedure providing a number of skill and error metrics is conducted using ground-truth data collected with a leveling rod at co-located reservoir points. Assessment of the results shows a strong correlation between the echosounder, SfM measurements and the field observations. The final product is a hybrid bathymetric survey resulting from the merging of SfM photogrammetry and echosoundings within an adaptive sampling framework.

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft-rot, caused by Dickeya dadantii. Several statistical models -based on logistic regression analysis (LRA) and artificial neural networks (ANN)- were obtained for the experimental system zucchini-D. dadantii, which classify new samples as "healthy" or "infected." The LRA worked best in identifying high dose-infiltrated leaves (in infiltrated and non-infiltrated areas) whereas ANN offered a higher accuracy at identifying low dose-infiltrated areas. To assess the applicability of these results to cucurbits in a more general way, these models were validated for melon infected by the same pathogen, achieving accurate predictions for the infiltrated areas. The values of accuracy achieved are comparable to those found in the literature for classifiers identifying other infections based on data obtained by different techniques. Thus, MCFI in combination with thermography prove useful at providing data at lab scale that can be analyzed by machine learning. This approach could be scaled up to be applied in plant phenotyping.

The characterization of plant species using first-derivative fluorescence spectra

Plants are one of the most important parts of the ecological system and demand a reliable method for accurate classification. In this study, the first-derivative fluorescence spectral curves (FDFSCs) based on laser-induced fluorescence technology were proposed for the characterization of plant species. The measurement system is mainly composed of a spectrometer, an excitation light source (the two excitation wavelengths are 460 and 556 nm, respectively), and an intensified charge-coupled device camera. FDFSCs were calculated from the deviation between the fluorescence values at each wavelength, plus and minus one band, divided by the wavelength range. Principal component analysis was utilized to analyze the FDFSCs by extracting the main attributes and reducing the dimensionality of variables. A support vector machine was used to evaluate FDFSC performance for the identification of plant species. Plant species that are difficult to distinguished by the naked eye, can be identified effectively using the proposed FDFSCs. For the 556 nm and 460 nm excitation wavelengths, the overall identification rates of the six plant species evaluated were 93.3% and 91.7%, respectively. Experimental results demonstrated that the combination of the FDFSCs with multivariate analysis could provide a simple and reliable method for the characterization of plant species.

Vegetation identification based on characteristics of fluorescence spectral spatial distribution

The PCIFSD were firstly utilized in plant species analysis. Plant species can be effortless distinguished using PCIFSD in this paper.

Detection of bacterial infection of agave plants by laser-induced fluorescence

Greenhouse-grown plants of Agave tequilana Weber var. azul were inoculated with Erwinia carotovora, the causal agent of stem soft rot. We investigated the laser-induced fluorescence (LIF) of agave plants to determine whether LIF can be used as a noninvasive sensing tool for pathological studies. The LIF technique was also investigated as a means of detecting the effect of the polyamine biosynthesis inhibitor beta-hydroxyethylhydrazine as a bactericide against the pathogenic bacterium Erwinia carotovora. A He-Ne laser at 632.8 nm was used as the excitation source, and in vivo fluorescence emission spectra were recorded in the 660-790-range. Fluorescence maxima were at 690 and 740 nm. The infected plants that were untreated with the bactericide showed a definite increase in fluorescence intensity at both maxima within the first three days after infection. Beginning on the fifth day, a steady decrease in fluorescence intensity was observed, with a greater effect at 740 than at 690 nm. After 30 days there was no fluorescence. The infected plants that had been treated with the bactericide showed no significant change in fluorescence compared with that of the uninfected plants. The ratio of fluorescence intensities was determined to be F 690 nm/F 740 nm for all treatments. These studies indicate that LIF measurements of agave plants may be used for the early detection of certain types of disease and for determining the effect of a bactericide on bacteria. The results also showed that fluorescence intensity ratios can be used as a reliable indicator of the progress of disease.

Remote Mapping of Vegetation and Geological Features by Lidar in the 9-11-mum Region

We report examples of the use of a scanning tunable CO(2) laser lidar system in the 9-11-mum region to construct images of vegetation and rocks at ranges as far as 5 km from the instrument. Range information is combined with horizontal and vertical distances to yield an image with three spatial dimensions simultaneous with the classification of target type. Object classification is based on reflectance spectra, which are sufficiently distinct to allow discrimination between several tree species, between trees and scrub vegetation, and between natural and artificial targets. Limitations imposed by laser speckle noise are discussed.