Assessing different regression algorithms for paddy rice leaf nitrogen concentration estimations from the first-derivative fluorescence spectrum

Wavelength selection of dual-mechanism LiDAR with reflection and fluorescence spectra for plant detection

With the continuous expansion and refinement in plant detection range, reflection, and fluorescence spectra present great research potentials and commercial values. Referring technical advantages with hyperspectral and fluorescence lidar for monitoring plants, the synchronous observation with reflection and fluorescence signals achieved by one lidar system has attracted wide attention. This paper plans to design and construct a dual-mechanism lidar system that can obtain spatial information, reflection, and fluorescence signals simultaneously. How to select the optimal detected bands to the dual-mechanism lidar system for monitoring plants is an essential step. Therefore, this paper proposes a two-step wavelength selection method to determine the optimal bands combination by considering the spectral characteristic of reflection and fluorescence signals themselves, and the hardware performance of lidar units comprehensively. The optimal bands combination of 4 reflection bands of 481 nm, 541 nm, 711.5 nm, 775.5 nm, and 2 fluorescence bands of 686.5 nm, 737 nm was determined. Besides, compared with the original reflection or fluorescence bands, the overall accuracy and average accuracy of the optimal band combination were respectively improved by 2.51%, 15.45%, and 7.8%, 29.06%. The study demonstrated the reliability and availability of the two-step wavelength selection method, and can provide references for dual-mechanism lidar system construction.

Application of Two-Dimensional Fluorescence Spectroscopy for the On-Line Monitoring of Teff-Based Substrate Fermentation Inoculated with Certain Probiotic Bacteria

There is increasing demand for cereal-based probiotic fermented beverages as an alternative to dairy-based products due to their limitations. However, analyzing and monitoring the fermentation process is usually time consuming, costly, and labor intensive. This research therefore aims to apply two-dimensional (2D)-fluorescence spectroscopy coupled with partial least-squares regression (PLSR) and artificial neural networks (ANN) for the on-line quantitative analysis of cell growth and concentrations of lactic acid and glucose during the fermentation of a teff-based substrate. This substrate was inoculated with mixed strains of Lactiplantibacillus plantarum A6 (LPA6) and Lacticaseibacillus rhamnosus GG (LCGG). The fermentation was performed under two different conditions: condition 1 (7 g/100 mL substrate inoculated with 6 log cfu/mL) and condition 2 (4 g/100 mL substrate inoculated with 6 log cfu/mL). For the prediction of LPA6 and LCGG cell growth, the relative root mean square error of prediction (pRMSEP) was measured between 2.5 and 4.5%. The highest pRMSEP (4.5%) was observed for the prediction of LPA6 cell growth under condition 2 using ANN, but the lowest pRMSEP (2.5%) was observed for the prediction of LCGG cell growth under condition 1 with ANN. A slightly more accurate prediction was found with ANN under condition 1. However, under condition 2, a superior prediction was observed with PLSR as compared to ANN. Moreover, for the prediction of lactic acid concentration, the observed values of pRMSEP were 7.6 and 7.7% using PLSR and ANN, respectively. The highest error rates of 13 and 14% were observed for the prediction of glucose concentration using PLSR and ANN, respectively. Most of the predicted values had a coefficient of determination (R2) of more than 0.85. In conclusion, a 2D-fluorescence spectroscopy combined with PLSR and ANN can be used to accurately monitor LPA6 and LCGG cell counts and lactic acid concentration in the fermentation process of a teff-based substrate. The prediction of glucose concentration, however, showed a rather high error rate.

Nitric oxide detection using principal component analysis spectral structure matching to the UV derivative spectrum

Ultraviolet (UV) spectroscopy is widely applied in real-time environmental monitoring, especially in diesel vehicle nitrogen monoxide (NO) emissions. However, in field experiments, UV absorption spectrum may exist for different degrees of drifts. Spectral jitters may exist for various reasons such as optical power variation, electrical signal drift, and the refractive index jitters of the optical path for an extended period of time, which causes the detection system to be calibrated. And the pulse xenon lamps as the UV source are characterized by specific emission lines that interfere in spectral analysis directly. For these problems, we proposed the spectral structure matching method based on principal component analysis (PCA), which was compared with the conventional polynomial fitting method to observe feasibility and variability. Further, the UV derivative spectrum was applied to the system appropriately, due to the variation of the absorption peak, and was only related to the target gas by using the above method. We validated our method experimentally by performing the NO UV detection system with the calibration and the comparison test. The results suggested that the calibration relative error was less than 9% and the measurement relative error was less than 6% for this wide range by the proposed processes, which optimized the interference of spectral structures and fluctuation to the system and therefore provided better monitoring. This study may provide an alternative spectral analysis method that is unaffected on the specific emission lines of lamps and is not limited to the spectral region and the target gas.

Analyzing the Effects of Hyperspectral ZhuHai-1 Band Combinations on LAI Estimation Based on the PROSAIL Model

Leaf area index (LAI) is a key biophysical variable to characterize vegetation canopy. Accurate and quantitative LAI estimation is significant for monitoring vegetation growth status. ZhuHai-1 (ZH-1), which is a commercial remote sensing micro-nano satellite, provides a possibility for quantitative detection of vegetation with high spatial and spectral resolution. However, the band characteristics of ZH-1 are closely related to the accuracy of vegetation monitoring. In this study, a simulation dataset containing 32 bands of ZH-1 was generated by using the PROSAIL model, which was used to analyze the performance of 32 bands for LAI estimation by using the hybrid inversion method. Meanwhile, the effect of different band combinations on LAI estimation was discussed based on sensitivity analysis and the correlation between bands. Then, the optimal band combination from ZH-1 hyperspectral satellite data for LAI estimation was obtained. LAI estimation was performed based on the selected optimal band combination of ZH-1 satellite images in Xiantao city, Hubei province, and compared with the Sentinel-2 normalized difference vegetation index (NDVI) values and LAI product. The results demonstrated that the obtained LAI map based on the optimal band combination of ZH-1 was generally consistent with the overall distribution of Sentinel-2 NDVI and the LAI product, but had a moderate correlation with Sentinel-2 LAI (R = 0.60), which may not favorably indicate the validity of indirect validation. However, the method of this study on the analysis of hyperspectral data bands has application potential to provide a reference for selecting appropriate bands of hyperspectral satellite data to estimate LAI and improve the application of hyperspectral data such as ZH-1 in vegetation monitoring.

Improving characteristic band selection in leaf biochemical property estimation considering interrelations among biochemical parameters based on the PROSPECT-D model

At present, many studies have mainly focused on analyzing the sensitivity and correlation to select characteristic bands. However, the interrelations between biochemical parameters were ignored, which may significantly influence the accuracy of biochemical concentration retrieval. The study aims to propose a new band selection method and to focus on the improving magnitude of characteristic band combination in leaf trait estimation when taking interrelations among different traits into consideration. Thus, in this study, firstly a ranking- and searching-based method considering the sensitivity and correlation between different wavelengths, which can enhance the reliability of spectral band selection, was proposed to select a subset of characteristic bands for leaf structure index and five leaf biochemical parameters (including chlorophyll (Chl), carotenoid (Car), leaf dry matter per area (LMA), equivalent water thickness (EWT), and anthocyanin (Anth)) based on the PROSPECT-D model. These characteristic bands were then validated based on a physical model for retrieving five biochemical properties using one synthetic dataset and six experimental datasets on leaf-level spectra. Secondly, and more innovatively, to explore interrelations among different biochemical parameters, trait-trait band combinations were adopted to retrieve and analyze how the five biochemical participants above affected each other. The results demonstrated that the combination of LMA (809 and 2278 nm), EWT (1386, 1414, and 1894 nm) is more beneficial in LMA and EWT estimation than respective retrieval: LMA-EWT band combination retrieval improves R² by 0.5782 and 0.1824 in two datasets, respectively, compared with solely LMA characteristic bands retrieval. What's more, the accuracy of Chl, EWT, Car, and Anth estimation can be also improved when considering interrelations between biochemical parameters. The experimental results show that the ranking- and searching-based method is an effective and efficient way to select a set of spectral bands related to the foliar information about plant traits, and trait-trait combinations, which focus on exploring latent interrelations between leaf traits, are useful in furthering improve retrieval accuracy. This research will provide notably advanced insight into identifying the spectral responses of biochemical traits in foliage and canopies.

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.