Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy

Hyperspectral Indices Developed from Fractional-Order Derivative Spectra Improved Estimation of Leaf Chlorophyll Fluorescence Parameters

Chlorophyll fluorescence (ChlF) parameters offer valuable insights into quantifying energy transfer and allocation at the photosystem level. However, tracking their variation based on reflectance spectral information remains challenging for large-scale remote sensing applications and ecological modeling. Spectral preprocessing methods, such as fractional-order derivatives (FODs), have been demonstrated to have advantages in highlighting spectral features. In this study, we developed and assessed the ability of novel spectral indices derived from FOD spectra and other spectral transformations to retrieve the ChlF parameters of various species and leaf groups. The results obtained showed that the empirical spectral indices were of low reliability in estimating the ChlF parameters. In contrast, the indices developed from low-order FOD spectra demonstrated a significant improvement in estimation. Furthermore, the incorporation of species specificity enhanced the tracking of the non-photochemical quenching (NPQ) of sunlit leaves (R2 = 0.61, r = 0.79, RMSE = 0.15, MAE = 0.13), the fraction of PSII open centers (qL) of shaded leaves (R2 = 0.50, r = 0.71, RMSE = 0.09, MAE = 0.08), and the fluorescence quantum yield (ΦF) of shaded leaves (R2 = 0.71, r = 0.85, RMSE = 0.002, MAE = 0.001). Our study demonstrates the potential of FOD spectra in capturing variations in ChlF parameters. Nevertheless, given the complexity and sensitivity of ChlF parameters, it is prudent to exercise caution when utilizing spectral indices for tracking them.

Integrating leaf spectral and water status information to effectively track chlorophyll a fluorescence parameters during dehydration

Monitoring changes in chlorophyll a (ChlFa) fluorescence during dehydration can provide insights into plant photosynthetic responses to climate change challenges, which are predicted to increase drought frequency. However, the limited knowledge of how ChlFa parameters respond to water deficit hinders the exploration of the photochemical mechanism of the photosynthetic process and the simulation of photosynthetic fluorescence models. Furthermore, how to track such responses of ChlFa parameters, especially at large scales, remains a challenge. In this study, we attempted to use spectral information reflected from leaves to follow the dynamic response patterns of ChlFa parameters of seven species under prolonged dehydration. The results showed that the investigated ChlFa parameters exhibited significant changes as dehydration progressed, with considerable variability among the different species as well as under different water conditions. This study also demonstrated that the integration of both spectral and water content information can provide an effective method for tracking ChlFa parameters during dehydration, explaining over 90% of the total variance in the measured ChlFa parameters. Collectively, these results should serve as a valuable reference for predicting the response of ChlFa parameters to dehydration and offer a potential method for estimating ChlFa parameters under drought conditions.

Ectomycorrhiza and ethylenediurea reduced the impact of high nitrogen and ozone stresses and increased the growth of Cedrus deodara

Cedrus deodara is the central conifer plant affected by ozone and nitrogen pollutants among forest species worldwide. The growth of C. deodara depends upon the ectomycorrhizal (ECM) association, which is usually disturbed by these factors. This study aims to understand how these factors affect plants at physiological and biochemical levels. Three fungal strain consortiums were inoculated with two-year-old C. deodara seedlings. The stresses of 100 kg N h-1and 100 ppb O3 were applied for six months to study their impact on chlorophyll and antioxidant enzymes (SOD, CAT, and APX). The results showed that C2 (Consortium of Cedrus deodara) positively impacted the growth of selected plant species. The high photosynthesis rate was determined by enhanced chlorophyll content, and C2-treated plants showed high chlorophyll content. Relatively, chlorophyll a and b contents increased significantly in the seedlings treated with Ethylenediurea (EDU) alone and with ozone stress. In addition, a significant difference was observed between EDU and O3-treated plants (14% EDU400-O3 and 23% EDU600-O3) and the control. Overall, antioxidant activities were higher in the treated samples than in the control. The order of SOD activity was C2 (448 U/gFW) and lowest (354.7 U/gFW) in control. APX also showed higher activity in treated plants in C1 ≥ C2 ≥ C3+O3, whereas CAT activity was the highest in C2 treatments. Ozone and nitrogen-stressed plants showed higher activities than EDU-treated plants compared to non-treated ones. Our findings highlight the importance of understanding the signaling effects of numerous precursors. Moreover, an extended investigation of seedlings developing into trees must be conducted to verify the potential of ectomycorrhizal strains associated with C. deodara and comprehend EDU's role as a direct molecular scavenger of reactive toxicants.

Sequencing and analysis of the complete mitochondrial genomes of Toona sinensis and Toona ciliata reveal evolutionary features of Toona

BACKGROUND

Toona is a critical genus in the Meliaceae, and the plants of this group are an asset for both restorative and restorative purposes, the most flexible of which are Toona sinensis and Toona ciliata. To concentrate on the advancement of mitochondrial(Mt) genome variety in T.sinensis and T.ciliata, the Mt genomes of the two species were sequenced in high throughput independently, after de novo assembly and annotation to construct a Mt genome map for comparison in genome structure. Find their repetitive sequences and analyze them in comparison with the chloroplast genome, along with Maximum-likelihood(ML) phylogenetic analysis with 16 other relatives.

RESULTS

(1) T. sinensis and T.ciliata are both circular structures with lengths of 683482 bp and 68300 bp, respectively. They share a high degree of similarity in encoding genes and have AT preferences. All of them have the largest Phe concentration and are the most frequently used codons. (2) Both of their Mt genome are highly preserved in terms of structural and functional genes, while the main variability is reflected in the length of tRNA, the number of genes, and the value of RSCU. (3) T. siniensis and T. ciliata were detected to have 94 and 87 SSRs, respectively, of which mononucleotides accounted for the absolute proportion. Besides, the vast majority of their SSRs were found to be poly-A or poly-T. (4)10 and 11 migrating fragments were identified in the comparison with the chloroplast genome, respectively. (5) In the ML evolutionary tree, T.sinensis and T.ciliata clustered individually into a small branch with 100% support, reflecting two species of Toona are very similarly related to each other.

CONCLUSIONS

This research provides a basis for the exploitation of T.sinensis and T.ciliata in terms of medicinal, edible, and timber resources to avoid confusion; at the same time, it can explore the evolutionary relationship between the Toona and related species, which does not only have an important practical value, but also provides a theoretical basis for future hybrid breeding of forest trees, molecular markers, and evolutionary aspects of plants, which has great scientific significance.

Classification of Toona sinensis Young Leaves Using Machine Learning and UAV-Borne Hyperspectral Imagery

Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In this study, UAV hyperspectral imaging technology was used to obtain canopy hyperspectral data of biennial seedlings of different varieties of T. sinensis to distinguish young and old leaves. Five classification models were trained, namely Random Forest (RF), Artificial Neural Network (ANN), Decision Tree (DT), Partial Least Squares Discriminant Analysis (PLSDA), and Support Vector Machine (SVM). Raw spectra and six preprocessing methods were used to fit the best classification model. Satisfactory accuracy was obtained from all the five models using the raw spectra. The SVM model showed good performance on raw spectra and all preprocessing methods, and yielded higher accuracy, sensitivity, precision, and specificity than other models. In the end, the SVM model based on the raw spectra produced the most reliable and robust prediction results (99.62% accuracy and 99.23% sensitivity on the validation set only, and 100.00% for the rest). Three important spectral regions of 422.7~503.2, 549.2, and 646.2~687.2 nm were found to be highly correlated with the identification of young leaves of T. sinensis. In this study, a fast and effective method for identifying young leaves of T. sinensis was found, which provided a reference for the rapid identification of young leaves of T. sinensis in the wild.