Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

Analysis of Photosynthetic Characteristics and Screening High Light-Efficiency Germplasm in Sugarcane

Sugarcane is a globally significant crop for sugar and energy production, and developing high light-efficiency sugarcane varieties is crucial for enhancing yield and quality. However, limited research is available on the screening of sugarcane germplasm with high photosynthetic efficiency, especially with different leaf positions. The present study, conducted in Guangxi, China, aimed to analyze the photosynthetic characteristics of 258 sugarcane varieties at different leaf positions over three consecutive years in field experiments. The results showed significant differences in photosynthetic characteristics among genotypes, years, and leaf positions. Heritability estimates for various photosynthetic parameters ranged from 0.76 to 0.88. Principal component analysis revealed that the first three principal components accounted for over 99% of the cumulative variance. The first component represented photosynthetic efficiency and light utilization, the second focused on electron transfer and reaction center status, and the third was associated with chlorophyll content. Cluster and discriminant analysis classified sugarcane genotypes into three categories: high photosynthetic efficiency (HPE) with 86 genotypes, medium photosynthetic efficiency (MPE) with 60 genotypes, and low photosynthetic efficiency (LPE) with 112 genotypes. Multi-year trials confirmed that HPE sugarcane genotypes had higher single-stem weight and sucrose content. This study provides valuable insights into the photosynthetic physiological characteristics of different sugarcane varieties, which can contribute to further research regarding high yields and sugar breeding.

Decreased Photosynthetic Efficiency in Nicotiana tabacum L. under Transient Heat Stress

Heat stress is an abiotic factor that affects the photosynthetic parameters of plants. In this study, we examined the photosynthetic mechanisms underlying the rapid response of tobacco plants to heat stress in a controlled environment. To evaluate transient heat stress conditions, changes in photochemical, carboxylative, and fluorescence efficiencies were measured using an infrared gas analyser (IRGA Licor 6800) coupled with chlorophyll a fluorescence measurements. Our findings indicated that significant disruptions in the photosynthetic machinery occurred at 45 °C for 6 h following transient heat treatment, as explained by 76.2% in the principal component analysis. The photosynthetic mechanism analysis revealed that the dark respiration rate (Rd and Rd*CO2) increased, indicating a reduced potential for carbon fixation during plant growth and development. When the light compensation point (LCP) increased as the light saturation point (LSP) decreased, this indicated potential damage to the photosystem membrane of the thylakoids. Other photosynthetic parameters, such as AMAX, VCMAX, JMAX, and ΦCO2, also decreased, compromising both photochemical and carboxylative efficiencies in the Calvin-Benson cycle. The energy dissipation mechanism, as indicated by the NPQ, qN, and thermal values, suggested that a photoprotective strategy may have been employed. However, the observed transitory damage was a result of disruption of the electron transport rate (ETR) between the PSII and PSI photosystems, which was initially caused by high temperatures. Our study highlights the impact of rapid temperature changes on plant physiology and the potential acclimatisation mechanisms under rapid heat stress. Future research should focus on exploring the adaptive mechanisms involved in distinguishing mutants to improve crop resilience against environmental stressors.

Chlorophyll a fluorescence as a tool to monitor physiological status in the leaves of Artemisia ordosica under root cutting conditions

Background

Root cutting caused by underground coal mining subsidence is among the leading causes of plant damage in western China. Detection of root cutting stress is of great importance in evaluating the degree of plant damage and changes in physiological conditions in underground coal mining disturbance conditions.

Methods

The present study assessed the use of chlorophyll fluorescence OJIP transient data to evaluate the disturbance characteristics of root cutting stress on leaf photosynthetic mechanisms in the typical shrub Artemisia ordosica Krasch. Different root cutting ratios (10%, 20%, 30%, 50%, 75%, and 100%) were established on the roots of A. ordosica in the field, and the OJIP transient and JIP parameters of the leaves were measured.

Results

The overall OJIP curves and each OJIP step in leaves decreased as the root cutting ratio increased, but the impact was relatively small for root cutting ratios of less than 30%. Through the analysis of JIP parameters and the established energy pipeline model, it was found that the energy capture efficiency and electron transfer efficiency of photosystem II decreased as the root cutting ratio increased. Therefore, we also inferred that the threshold for the plant root cutting ratio at which leaf photosynthetic mechanisms begin to change is 30-50%.

Conclusion

These results indicate that OJIP transient analysis can serve as a non-destructive, rapid technique for detecting plant root cutting stress in coal mining subsidence areas, which is of great value for non-destructive monitoring of plant root damage.

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms-such as PLS, VIP, iPLS-VIP, GA, RF, and CARS-the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops

Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R² values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C₃ and C₄ plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.