Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet's randomized t-test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding R 2 [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments.

Effect of Paenibacillus favisporus CHP14 inoculation on selenium accumulation and tolerance of Pakchoi (Brassica chinensis L.) under exogenous selenite treatments

The effects of Paenibacillus favisporus CHP14 inoculation on selenium (Se) accumulation and Se tolerance of Pakchoi were studied by a pot experiment conducted in greenhouse. The results revealed that the growth traits such as plant height, root length, and biomass were significantly elevated during CHP14 treatment at 0 ∼ 8.0 mg·kg-1 Se(IV) levels. CHP14-inoculated plants accumulated more Se in root and shoot, which were 24.1%∼57.3% and 7.5%∼50.9% higher than those of non-inoculated plants. The contents of leaf nitrogen (N), phosphorus (P), magnesium (Mg), and iron (Fe), as well as the ratio of indoleacetic acid and abscisic acid contents (IAA/ABA) were increased by CHP14 inoculation, and positively associated with photosynthetic pigment contents (p < 0.05). At ≥ 4.0 mg·kg-1 Se(IV) levels, superoxide dismutase, peroxidase, and glutathione peroxidase activities of Pakchoi roots were increased with CHP14 inoculation, by 9.9%∼17.1%, 28.4%∼40.7%, and 7.4%∼15.3%, respectively. Moreover, CHP14 inoculation enhanced ascorbate-glutathione (AsA-GSH) metabolism in roots by upregulating the related enzymes activities and antioxidant contents under excess Se(IV) stress. These findings suggest that CHP14 is beneficial to improve plant growth and enhance Se(IV) resistance of Pakchoi, and can be exploited as potential inoculants for phytoremediation process in Se contaminated soil.

Ecophysiological responses of mangrove Kandelia obovata seedlings to bed-cleaning sludge from coastline shrimp ponds

Cumulative effect of bed-cleaning sludge (BCS) from shrimp ponds on the physiology of Kandelia obovata seedling were investigated. Based on the accumulation rate of BCS discharged from shrimp ponds in mangrove forests, four types of sediment coverage thicknesses (SCT) of 0, 2, 4, and 8 cm were set up. With the increases in SCTs, photosynthetic rate, stomatal conductance, transpiration rates were lowest in SCT8; intercellular CO2 concentrations were lowest in SCT4. Leaf superoxide dismutase and peroxidase activities rose and then fell with the increases in SCTs, and Leaf malonaldehyde contents significantly increased. However, contents of leaf free proline, soluble protein and soluble sugar were lowest for SCT4. Root activity was highest for SCT4. Leaves had high N contents, while roots had high P contents. Overall, as for physiological parameters of K. obovata seedlings, SCTs <4 cm were suitable and the values up to 8 cm formed some stresses.

Transcriptome dynamics in Artemisia annua provides new insights into cold adaptation and de-adaptation

Plants adapt to cold stress through a tightly regulated process involving metabolic reprogramming and tissue remodeling to enhance tolerance within a short timeframe. However, the precise differences and interconnections among various organs during cold adaptation remain poorly understood. This study employed dynamic transcriptomic and metabolite quantitative analyses to investigate cold adaptation and subsequent de-adaptation in Artemisia annua, a species known for its robust resistance to abiotic stress. Our findings revealed distinct expression patterns in most differentially expressed genes (DEGs) encoding transcription factors and components of the calcium signal transduction pathway within the two organs under cold stress. Notably, the long-distance transport of carbon sources from source organs (leaves) to sink organs (roots) experienced disruption followed by resumption, while nitrogen transport from roots to leaves, primarily in the form of amino acids, exhibited acceleration. These contrasting transport patterns likely contribute to the observed differences in cold response between the two organs. The transcriptomic analysis further indicated that leaves exhibited increased respiration, accumulated anti-stress compounds, and initiated the ICE-CBF-COR signaling pathway earlier than roots. Differential expression of genes associated with cell wall biosynthesis suggests that leaves may undergo cell wall thickening while roots may experience thinning. Moreover, a marked difference was observed in phenylalanine metabolism between the two organs, with leaves favoring lignin production and roots favoring flavonoid synthesis. Additionally, our findings suggest that the circadian rhythm is crucial in integrating temperature fluctuations with the plant's internal rhythms during cold stress and subsequent recovery. Collectively, these results shed light on the coordinated response of different plant organs during cold adaptation, highlighting the importance of inter-organ communication for successful stress tolerance.

Elevated CO2 and ammonium nitrogen promoted the plasticity of two maple in great lakes region by adjusting photosynthetic adaptation

Introduction

Climate change-related CO2 increases and different forms of nitrogen deposition are thought to affect the performance of plants, but their interactions have been poorly studied.

Methods

This study investigated the responses of photosynthesis and growth in two invasive maple species, amur maple (Acer ginnala Maxim.) and boxelder maple (Acer negundo L.), to elevated CO2 (400 µmol mol-1 (aCO2) vs. 800 µmol mol-1 (eCO2) and different forms of nitrogen fertilization (100% nitrate, 100% ammonium, and an equal mix of the two) with pot experiment under controlled conditions.

Results and discussion

The results showed that eCO2 significantly promoted photosynthesis, biomass, and stomatal conductance in both species. The biochemical limitation of photosynthesis was switched to RuBP regeneration (related to Jmax) under eCO2 from the Rubisco carboxylation limitation (related to Vcmax) under aCO2. Both species maximized carbon gain by lower specific leaf area and higher N concentration than control treatment, indicating robust morphological plasticity. Ammonium was not conducive to growth under aCO2, but it significantly promoted biomass and photosynthesis under eCO2. When nitrate was the sole nitrogen source, eCO2 significantly reduced N assimilation and growth. The total leaf N per tree was significantly higher in boxelder maple than in amur maple, while the carbon and nitrogen ratio was significantly lower in boxelder maple than in amur maple, suggesting that boxelder maple leaf litter may be more favorable for faster nutrient cycling. The results suggest that increases in ammonium under future elevated CO2 will enhance the plasticity and adaptation of the two maple species.

Rapid quantification of biological nitrogen fixation using optical spectroscopy

Biological nitrogen fixation (BNF) provides a globally important input of nitrogen (N); its quantification is critical but technically challenging. Leaf reflectance spectroscopy offers a more rapid approach than traditional techniques to measure plant N concentration ([N]) and isotopes (δ15N). Here we present a novel method for rapidly and inexpensively quantifying BNF using optical spectroscopy. We measured plant [N], δ15N, and the amount of N derived from atmospheric fixation (Ndfa) following the standard traditional methodology using isotope ratio mass spectrometry (IRMS) from tissues grown under controlled conditions and taken from field experiments. Using the same tissues, we predicted the same three parameters using optical spectroscopy. By comparing the optical spectroscopy-derived results with traditional measurements (i.e. IRMS), the amount of Ndfa predicted by optical spectroscopy was highly comparable to IRMS-based quantification, with R2 being 0.90 (slope=0.90) and 0.94 (slope=1.02) (root mean square error for predicting legume δ15N was 0.38 and 0.43) for legumes grown in glasshouse and field, respectively. This novel application of optical spectroscopy facilitates BNF studies because it is rapid, scalable, low cost, and complementary to existing technologies. Moreover, the proposed method successfully captures the dynamic response of BNF to climate changes such as warming and drought.

Machine learning in photosynthesis: Prospects on sustainable crop development

Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.

The role of remote sensing in tropical grassland nutrient estimation: a review

The carbon (C) and nitrogen (N) ratio is a key indicator of nutrient utilization and limitations in rangelands. To understand the distribution of herbivores and grazing patterns, information on grass quality and quantity is important. In heterogeneous environments, remote sensing offers a timely, economical, and effective method for assessing foliar biochemical ratios at varying spatial and temporal scales. Hence, this study provides a synopsis of the advancement in remote sensing technology, limitations, and emerging opportunities in mapping the C:N ratio in rangelands. Specifically, the paper focuses on multispectral and hyperspectral sensors and investigates their properties, absorption features, empirical and physical methods, and algorithms in predicting the C:N ratio in grasslands. Literature shows that the determination of the C:N ratio in grasslands is not in line with developments in remote sensing technologies. Thus, the use of advanced and freely available sensors with improved spectral and spatial properties such as Sentinel 2 and Landsat 8/9 with sophisticated algorithms may provide new opportunities to estimate C:N ratio in grasslands at regional scales, especially in developing countries. Spectral bands in the near-infrared, shortwave infrared, red, and red edge were identified to predict the C:N ratio in plants. New indices developed from recent multispectral satellite imagery, for example, Sentinel 2 aided by cutting-edge algorithms, can improve the estimation of foliar biochemical ratios. Therefore, this study recommends that future research should adopt new satellite technologies with recent development in machine learning algorithms for improved mapping of the C:N ratio in grasslands.

Nitrogen supply alleviates cold stress by increasing photosynthesis and nitrogen assimilation in maize seedlings

Cold stress inhibits the early growth of maize, leading to reduced productivity. Nitrogen (N) is an essential nutrient that stimulates maize growth and productivity, but the relationship between N availability and cold tolerance is poorly characterized. Therefore, we studied the acclimation of maize under combined cold stress and N treatments. Exposure to cold stress caused a decline in growth and N assimilation, but increased abscisic acid (ABA) and carbohydrate accumulation. The application of different N concentrations from the priming stage to the recovery period resulted in the following observations: (i) high N supply alleviated cold stress-dependent growth inhibition, as shown by increased biomass, chlorophyll and Rubisco content and PSII efficiency; (ii) cold stress-induced ABA accumulation was repressed under high N, presumably due to enhanced stomatal conductance; (iii) the mitigating effects of high N on cold stress could be due to the increased activities of N assimilation enzymes and improved redox homeostasis. After cold stress, the ability of maize seedlings to recover increased under high N treatment, indicating the potential role of high N in the cold stress tolerance of maize seedlings.

Predicting leaf traits across functional groups using reflectance spectroscopy

Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R²  > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R²  = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

Pyridinic Nitrogen Doped Carbon Dots Supply Electrons to Improve Photosynthesis and Extracellular Electron Transfer of Chlorella pyrenoidosa

Optimizing photosynthesis is imperative for providing energy and organics for all life on the earth. Here, carbon dots doped with pyridinic nitrogen (named lev-CDs) are synthesized by the one-pot hydrothermal method, and the structure-function relationship between functional groups on lev-CDs and photosynthesis of Chlorella pyrenoidosa (C. pyrenoidosa) is proposed. Pyridinic nitrogen plays a key role in the positive effect on photosynthesis caused by lev-CDs. In detail, lev-CDs act as electron donors to supply photo-induced electrons to P680⁺ and QA⁺ , causing electron transfer from lev-CDs to the photosynthetic electron transport chain in the photosystems. In return, the recombination efficiency of electron-hole pairs on lev-CDs decreases. As a result, the electron transfer rate in the electron transport chain, the activity of photosystem II, and the Calvin cycle are enhanced. Moreover, the electron transfer rate between C. pyrenoidosa and external circumstances enhanced by lev-CDs is about 50%, and electrons exported from C. pyrenoidosa can be used to reduce iron(III). This study is of great significance for engineering nanomaterials to improve photosynthesis.

Transcriptome Profiling and Chlorophyll Metabolic Pathway Analysis Reveal the Response of Nitraria tangutorum to Increased Nitrogen

To identify genes that respond to increased nitrogen and assess the involvement of the chlorophyll metabolic pathway and associated regulatory mechanisms in these responses, Nitraria tangutorum seedlings were subjected to four nitrogen concentrations (N0, N6, N36, and N60: 0, 6, 36, and 60 mmol·L^-1 nitrogen, respectively). The N. tangutorum seedling leaf transcriptome was analyzed by high-throughput sequencing (Illumina HiSeq 4000), and 332,420 transcripts and 276,423 unigenes were identified. The numbers of differentially expressed genes (DEGs) were 4052 in N0 vs. N6, 6181 in N0 vs. N36, and 3937 in N0 vs. N60. Comparing N0 and N6, N0 and N36, and N0 and N60, we found 1101, 2222, and 1234 annotated DEGs in 113, 121, and 114 metabolic pathways, respectively, classified in the Kyoto Encyclopedia of Genes and Genomes database. Metabolic pathways with considerable accumulation were involved mainly in anthocyanin biosynthesis, carotenoid biosynthesis, porphyrin and chlorophyll metabolism, flavonoid biosynthesis, and amino acid metabolism. N36 increased δ-amino levulinic acid synthesis and upregulated expression of the magnesium chelatase H subunit, which promoted chlorophyll a synthesis. Hence, N36 stimulated chlorophyll synthesis rather than heme synthesis. These findings enrich our understanding of the N. tangutorum transcriptome and help us to research desert xerophytes' responses to increased nitrogen in the future.

Diaporthe atlantica enhances tomato drought tolerance by improving photosynthesis, nutrient uptake and enzymatic antioxidant response

Functional symbiosis with fungal endophytes can help plants adapt to environmental stress. Diaporthe atlantica is one of the most abundant fungal taxa associated with roots of Festuca rubra subsp. pruinosa, a grass growing in sea cliffs. This study aimed to investigate the ability of a strain of this fungus to ameliorate the impact of drought stress on tomato plants. In a greenhouse experiment, tomato plants were inoculated with Diaporthe atlantica strain EB4 and exposed to two alternative water regimes: well-watered and drought stress. Several physiological and biochemical plant parameters were evaluated. Inoculation with Diaporthe promoted plant growth in both water treatments. A significant interactive effect of Diaporthe-inoculation and water-regime showed that symbiotic plants had higher photosynthetic capacity, water-use efficiency, nutrient uptake (N, P, K, Fe and Zn), and proline content under drought stress, but not under well-watered conditions. In addition, Diaporthe improved the enzymatic antioxidant response of plants under drought, through an induced mechanism, in which catalase activity was modulated and conferred protection against reactive oxygen species generation during stress. The results support that Diaporthe atlantica plays a positive role in the modulation of tomato plant responses to drought stress by combining various processes such as improving photosynthetic capacity, nutrient uptake, enzymatic antioxidant response and osmo-protectant accumulation. Thus, drought stress in tomato can be enhanced with symbiotic fungi.

Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information

Partial least squares regression (PLSR) is applied increasingly often to predict plant photosynthesis from reflectance spectra. While its applicability across different areas has been examined in previous studies, its stability across time has yet to be evaluated. In this study, we assessed a series of PLSR models built upon three different band selection approaches (iterative stepwise, genetic algorithm, and uninformative variable elimination), in combination with different spectral transforms (original and first-order derivative spectra), for their stabilities in predicting the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) from hyperspectral reflectance spectra at different temporal scales (seasonal and interannual). The results showed that both photosynthetic parameters can be estimated from leaf hyperspectral reflectance with moderate to good accuracy across different growing stages (R² = 0.45-0.84) and years (R² = 0.37-0.97). We further found that the iterative stepwise selection of informative bands when building PLSR models could greatly improve its predictive capacity compared with that of other PLSR models, especially those based on first-order derivative spectra. However, the selected bands of the models for both photosynthetic parameters were, unfortunately not consistent. Furthermore, we could not have identified any model with fixed spectra performed consistently across different seasonal stages and across different years. However, the blue spectral regions were popularly selected throughout the growing stages and in different years. The results demonstrate that leaf spectra-trait estimation using PLSR models varies with time and thus cast doubt over the use of a specific PLSR model to infer leaf traits across different temporal-spatial contexts. The development of a general applicable PLSR model is still in the works.

Unsupervised analysis of NIRS spectra to assess complex plant traits: leaf senescence as a use case

BACKGROUND

As a rapid and non-destructive method, Near Infrared Spectroscopy is classically proposed to assess plant traits in many scientific fields, to observe enlarged genotype panels and to document the temporal kinetic of some biological processes. Most often, supervised models are used. The signal is calibrated thanks to reference measurements, and dedicated models are generated to predict biological traits. An alternative unsupervised approach considers the whole spectra information in order to point out various matrix changes. Although more generic, and faster to implement, as it does not require a reference data set, this latter approach is rarely used to document biological processes, and does requires more information of the process.

METHODS

In our work, an unsupervised model was used to document the flag leaf senescence of durum wheat (Triticum turgidum durum). Leaf spectra changes were observed using Moving Window Principal Component Analysis (MWPCA). The dates related to earlier and later spectra changes were compared to two key points on the senescence time course: senescence onset (T0) and the end of the leaf span (T1) derived from a supervised strategy.

RESULTS

For almost all leaves and whatever the signal pre-treatments and window size considered, the MWPCA found significant spectral changes. The latter was highly correlated with T1 (0.59 ≤ r ≤ 0.86) whereas the correlations between the first significant spectrum changes and T0 were lower (0.09 ≤ r ≤ 0.56). These different relationships are discussed below since they define the potential as well as the limitations of MWPCA to model biological processes.

CONCLUSION

Overall, our study demonstrates that the information contained in the spectra can be used when applying an unsupervised method, here the MWPCA, to characterize a complex biological phenomenon such leaf senescence. It also means that using whole spectra may be relevant in agriculture and plant biology.

Machine learning methods for assessing photosynthetic activity: environmental monitoring applications

Monitoring of the photosynthetic activity of natural and artificial biocenoses is of crucial importance. Photosynthesis is the basis for the existence of life on Earth, and a decrease in primary photosynthetic production due to anthropogenic influences can have catastrophic consequences. Currently, great efforts are being made to create technologies that allow continuous monitoring of the state of the photosynthetic apparatus of terrestrial plants and microalgae. There are several sources of information suitable for assessing photosynthetic activity, including gas exchange and optical (reflectance and fluorescence) measurements. The advent of inexpensive optical sensors makes it possible to collect data locally (manually or using autonomous sea and land stations) and globally (using aircraft and satellite imaging). In this review, we consider machine learning methods proposed for determining the functional parameters of photosynthesis based on local and remote optical measurements (hyperspectral imaging, solar-induced chlorophyll fluorescence, local chlorophyll fluorescence imaging, and various techniques of fast and delayed chlorophyll fluorescence induction). These include classical and novel (such as Partial Least Squares) regression methods, unsupervised cluster analysis techniques, various classification methods (support vector machine, random forest, etc.) and artificial neural networks (multilayer perceptron, long short-term memory, etc.). Special aspects of time-series analysis are considered. Applicability of particular information sources and mathematical methods for assessment of water quality and prediction of algal blooms, for estimation of primary productivity of biocenoses, stress tolerance of agricultural plants, etc. is discussed.

Advances in field-based high-throughput photosynthetic phenotyping

Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.

Using hyperspectral leaf reflectance to estimate photosynthetic capacity and nitrogen content across eastern cottonwood and hybrid poplar taxa

Eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performing Populus genotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (Vcmax), electron transport-limited carboxylation rate (Jmax), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (Narea), and leaf reflectance of seven taxa and 62 genotypes of Populus from two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except for Narea in which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimating Vcmax (R2 = 0.51 and RMSPE = 31%) and Jmax (R2 = 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R2 = 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimating Narea (R2 = 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimating Vcmax and Jmax. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes because of its capacity to estimate these physicochemical parameters.

Using UAV image data to monitor the effects of different nitrogen application rates on tea quality

BACKGROUND

Accurate and efficient evaluation of the effect of nitrogen application rate on tea quality is of great significance for nitrogen management in a tea garden. However, previous methods were all through soil or leaf sampling, using biochemical methods for laboratory testing. These methods are not only less one-time detection samples, but also time-consuming, laborious and inefficient. Therefore, the development of fast, efficient and non-destructive diagnostic methods is an important goal in this field.

RESULTS

We obtained spectral information on the tea canopy using a multispectral camera carried by an unmanned aerial vehicle (UAV), and extracted the average DN value of the experimental plot by environmental visual imagery (ENVI); we finally obtained 28 spectral parameters. By analyzing the correlation between spectral parameters and ground parameters measured synchronously, five spectral parameters with high correlation were selected. Finally, the prediction models of tea nitrogen, polyphenol and amino acid content were established by using support vector machine (SVM), partial least squares and backpropagation neural network. Through modeling comparison and coefficient verification, the results show that the ground parameters measured in the laboratory were in good agreement with the results estimated by the model. The SVM model had the best performance in predicting nitrogen and tea polyphenol content, with R²  = 0.7583 and 0.7533, root mean square error of prediction (RMSEP) = 0.4086 and 0.3392, and normalized RMSEP (NRMSEP) = 1.23 and 1.28, respectively. The partial least squares regression model had the best performance in predicting amino acid content, with R²  = 0.7597, RMSEP = 0.1176 and NRMSEP = 4.10.

CONCLUSION

The results show that the model based on UAV image data and machine learning algorithm can effectively detect the main biochemical components of the tea plant, which provides an important basis for tea garden management. © 2021 Society of Chemical Industry.

Effects of Pymetrozine and Tebuconazole with Foliar Fertilizer Through Mixed Application on Plant Growth and Pesticide Residues in Cucumber

The mixed application of pesticides and foliar fertilizer has been widely used in the production of cucumber, however, their effects on plant growth and pesticide dissipation are still unclear. In this study, the effects of mixed application of pymetrozine, tebuconazole and foliar fertilizer on the cucumber plant growth and pesticide dissipation were investigated simultaneously. The results show that the mixed use of pymetrozine, tebuconazole, especially adding foliar fertilizer, improved the physiological indexes (i.e., area, nitrogen content and chlorophyll content of the leaves, and root growth) of cucumber plants compared to those with the application of single pesticide. Meanwhile, it can significantly affect the dissipation of pymetrozine even in the slower growth matrices (lower leaves, stems, and plants). The residue of tebuconazole in cucumber plants was affected by the combination of formulation type and foliar fertilizer. This study can provide data for scientifically guiding the mixed application of pesticide and fertilizer.

Selecting informative bands for partial least squares regressions improves their goodness-of-fits to estimate leaf photosynthetic parameters from hyperspectral data

The plant photosynthetic capacity determines the photosynthetic rates of the terrestrial biosphere. Timely approaches to obtain the spatiotemporal variations of the photosynthetic parameters are urgently needed to grasp the gas exchange rhythms of the terrestrial biosphere. While partial least squares regression (PLSR) is a promising way to predict the photosynthetic parameters maximum carboxylation rate (V_cmax) and maximum electron transport rate (J_max) rapidly and non-destructively from hyperspectral data, the approach, however, faces a high risk of overfitting and remains a high hurdle for applications. In this study, we propose to incorporate proper band selection techniques for PLSR analysis to refine the goodness-of-fit (GoF) in estimating V_cmax and J_max. Different band selection procedures coupled with different hyperspectral forms (reflectance, apparent absorption, as well as derivatives) were examined. Our results demonstrate that the GoFs of PLSR models could be greatly improved by combining proper band selection methods (especially the iterative stepwise elimination approach) rather than using full bands as commonly done with PLSR. The results also show that the 1st order derivative spectra had a balance between accuracy (R² = 0.80 for V_cmax, and 0.94 for J_max) and denoising (when a Gaussian noise was added to each leaf reflectance spectrum at each wavelength with a standard deviation of 1%) on retrieving photosynthetic parameters from hyperspectral data. Our results clearly illustrate the advantage of using the band selection approach for PLSR dimensionality reduction and model optimization, highlighting the superiority of using derivative spectra for V_cmax and J_max estimations, which should provide valuable insights for retrieving photosynthetic parameters from hyperspectral remotely sensed data.

Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy

Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO₂ absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO₂ absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO₂ exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO₂ fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration (R_dark25), the maximum rate of carboxylation by the enzyme Rubisco (V_cmax25), the maximum rate of electron transport (J_max25), and the triose phosphate utilization rate (T_p25), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for V_cmax25, J_max25, T_p25 and R_dark25 (RMSE of 13, 20, 1.5 and 0.3 μmol m^-2 s^-1, and R² of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V_c,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V_c,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V_c,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V_c,max and the four morphological and biochemical traits (R²  ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V_c,max relationships across three forest biomes into a single robust model for V_c,max (R²  = 0.77), and also accurately estimated the four traits (R²  = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V_c,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V_c,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.