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Introduction: Individual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliablemonitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed. Methods: In this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R²), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model. Results: (i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients ( ) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R², RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC. [ABSTRACT FROM AUTHOR]

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha−1) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R2 = 0.75, RMSE = 51.93 kg ha−1, MAE = 29.43 kg ha−1, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year. [ABSTRACT FROM AUTHOR]

Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content. [ABSTRACT FROM AUTHOR]

Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts. Efficient and non-destructive estimation of crop LNC is of paramount importance for on-field crop management. Spectral technology, with its advantages of repeatability and high-throughput observations, provides a feasible method for obtaining LNC data. This study explores the responsiveness of spectral parameters to soybean LNC at different vertical scales, aiming to refine nitrogen management in soybeans. This research collected hyperspectral reflectance data and LNC data from different leaf layers of soybeans. Three types of spectral parameters, nitrogen-sensitive empirical spectral indices, randomly combined dual-band spectral indices, and "three-edge" parameters, were calculated. Four optimal spectral index selection strategies were constructed based on the correlation coefficients between the spectral parameters and LNC for each leaf layer. These strategies included empirical spectral index combinations (Combination 1), randomly combined dual-band spectral index combinations (Combination 2), "three-edge" parameter combinations (Combination 3), and a mixed combination (Combination 4). Subsequently, these four combinations were used as input variables to build LNC estimation models for soybeans at different vertical scales using partial least squares regression (PLSR), random forest (RF), and a backpropagation neural network (BPNN). The results demonstrated that the correlation coefficients between the LNC and spectral parameters reached the highest values in the upper soybean leaves, with most parameters showing significant correlations with the LNC (p < 0.05). Notably, the reciprocal difference index (VI6) exhibited the highest correlation with the upper-layer LNC at 0.732, with a wavelength combination of 841 nm and 842 nm. In constructing the LNC estimation models for soybeans at different leaf layers, the accuracy of the models gradually improved with the increasing height of the soybean plants. The upper layer exhibited the best estimation performance, with a validation set coefficient of determination (R2) that was higher by 9.9% to 16.0% compared to other layers. RF demonstrated the highest accuracy in estimating the upper-layer LNC, with a validation set R2 higher by 6.2% to 8.8% compared to other models. The RMSE was lower by 2.1% to 7.0%, and the MRE was lower by 4.7% to 5.6% compared to other models. Among different input combinations, Combination 4 achieved the highest accuracy, with a validation set R2 higher by 2.3% to 13.7%. In conclusion, by employing Combination 4 as the input, the RF model achieved the optimal estimation results for the upper-layer LNC, with a validation set R2 of 0.856, RMSE of 0.551, and MRE of 10.405%. The findings of this study provide technical support for remote sensing monitoring of soybean LNCs at different spatial scales. [ABSTRACT FROM AUTHOR]

Nitrogen is an essential nutrient element for the growth of citrus tree. The accurate estimation of leaf nitrogen content (LNC) is important to guarantee fruit quality and yield. The unmanned aerial vehicle (UAV) remote sensing has shown great potential for monitoring the LNC of crops. However, the number of training samples is always limited due to the high cost and time consumption of acquiring LNC samples. Obtaining satisfactory results is difficult for most machine-learning models with insufficient samples. Thus, a semi-supervised regression model is designed in this paper to improve the performance of estimating citrus LNC from UAV images under the condition of small samples. First, the local binary pattern (LBP) operator is employed to fully extract textural features in UAV images. Second, semi-supervised cooperative regression models based on ridge regression (Ridge), support vector regression (SVR), and random forest (RF) are constructed by combining spectral and textural features. Finally, the best learning model is used to realize the accurate limited sample LNC estimation from the available training samples. The experiments confirm that the semi-supervised cooperative regression model has better and promising results than other machine-learning methods under the condition of small samples. The RF-based cooperative regression model (CoRF) performs best among other models, with a determination coefficient (R2) of 0.716 and a root mean square error (RMSE) of 0.620 g·kg−1. The CoRF model also achieves superior performance when combining LBP textural features. The results estimated by the semi-supervised cooperative regression model is capable of providing instructions for the production and management of citrus orchard. [ABSTRACT FROM AUTHOR]

Timely and accurately monitoring leaf nitrogen content (LNC) is essential for evaluating crop nutrition status. Currently, Unmanned Aerial Vehicles (UAV) imagery is becoming a potentially powerful tool of assessing crop nitrogen status in fields, but most of crop nitrogen estimates based on UAV remote sensing usually use single type imagery, the fusion information from different types of imagery has rarely been considered. In this study, the fusion images were firstly made from the simultaneously acquired digital RGB and multi-spectral images from UAV at three growth stages of rice, and then couple the selecting methods of optimal features with machine learning algorithms for the fusion images to estimate LNC in rice. Results showed that the combination with different types of features could improve the models' accuracy effectively, the combined inputs with bands, vegetation indices (VIs) and Grey Level Co-occurrence Matrices (GLCMs) have the better performance. The LNC estimation of using fusion images was improved more obviously than multispectral those, and there was the best estimation at jointing stage based on Lasso Regression (LR), with R2 of 0.66 and RMSE of 11.96%. Gaussian Process Regression (GPR) algorithm used in combination with one feature-screening method of Minimum Redundancy Maximum Correlation (mRMR) for the fusion images, showed the better improvement to LNC estimation, with R2 of 0.68 and RMSE of 11.45%. It indicates that the information fusion from UAV multi-sensor imagery can significantly improve crop LNC estimates and the combination with multiple types of features also has a great potential for evaluating LNC in crops. [ABSTRACT FROM AUTHOR]

Accurate and rapid estimation of leaf nitrogen content (LNC) in winter wheat using hyperspectral techniques is important for growth monitoring and accurate fertilization. Based on field experiments conducted over four years at three ecological sites with four different nitrogen (N) application treatments and four different N-efficient wheat varieties in Henan Province, the canopy spectra and LNC of winter wheat were obtained simultaneously in the main growth stages. The original spectrum was subjected to continuum removal (CR), the correlation between LNC and various vegetation indices was systematically analysed, and the optimal vegetation index estimation model for LNC was constructed. Simultaneously, discrete wavelet transform (DWT) was used to further compress and extract the CR spectrum. The model was combined with partial least squares regression (PLSR) and K-nearest neighbour (KNN) algorithms to estimate LNC in winter wheat. The results showed that the spectrum treated with CR was significantly improved in its correlation with LNC and that the model established based on normalized vegetation index NDVI (CR728, CR977) combined with the CR spectrum was better compared to existing vegetation indices. The calibration and validation coefficients of determination (R2) and root mean square error (RMSE) were 0.816 and 0.799, and 0.352% and 0.342%, respectively. DWT was used to transform the CR spectrum, and the results showed that a combination of the CR spectrum and a sym8 wavelet function with PLSR based on the approximation coefficients at decomposition level 2 predicted LNC most accurately. The coefficient of determination RC2, root mean square error RMSEC, and relative percent deviation (RPDC) of the calibration set were 0.884, 0.279%, and 2.932, respectively, and RV2, RMSEV, and RPDV of the validation set were 0.855, 0.291%, and 2.619, respectively, indicating that the model had good stability and predictive ability. Combination of CR and DWT improved the modelling accuracy of wheat LNC and showed better prediction results for multi-year and multi-point samples. The results of the study can provide a basis and reference for rapid monitoring of LNC in winter wheat. [ABSTRACT FROM AUTHOR]

Accurate estimation of wheat leaf nitrogen concentration (LNC) is critical for characterizing ecosystem and plant physiological processes; it can further guide fertilization and other field management operations, and promote the sustainable development of agriculture. In this study, a wheat LNC test method based on multi-source spectral data and a convolutional neural network is proposed. First, interpolation reconstruction was performed on the wheat spectra data collected by different spectral instruments to ensure that the number of spectral channels and spectral range were consistent, and multi-source spectral data were constructed using interpolated, reconstructed imaging spectral data and non-imaging spectral data. Afterwards, the convolutional neural network DshNet and machine learning methods (PLSR, SVR, and RFR) were compared under various scenarios (non-imaging spectral data, imaging spectral data, and multi-source spectral data). Finally, the competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the LNC detection model. The results show that the model based on DshNet has the highest test accuracy. The CARS method is more suitable for DshNet model optimization than SPA. In the modeling scenario with non-imaging spectral, imaging spectral, and multi-source spectral, the optimized R2 is 0.86, 0.82, and 0.82, and the RMSE is 0.29, 0.31, and 0.31, respectively. The LNC visualization results show that DshNet modeling using multi-source spectral data is conducive to the visualization expansion of non-imaging spectral data. Therefore, the method presented in this paper provides new considerations for spectral data from different sources and is helpful for related research on the chemometric task of multi-source spectral data. [ABSTRACT FROM AUTHOR]

Photosynthetic responses across complex elevational gradients provides insight into fundamental processes driving responses of plant growth and net primary production to environmental change. Gas exchange of needles and twig water potential were measured in two widespread coniferous tree species, Pinus contorta and Picea engelmannii, over an 800-m elevation gradient in southeastern Wyoming, USA. We hypothesized that limitations to photosynthesis imposed by mesophyll conductance (gm) would be greatest at the highest elevation sites due to higher leaf mass per area (LMA) and that estimations of maximum rate of carboxylation (Vcmax) without including gm would obscure elevational patterns of photosynthetic capacity. We found that gm decreased with elevation for P. contorta and remained constant for P. engelmannii, but in general, limitation to photosynthesis by gm was small. Indeed, estimations of Vcmax when including gm were equivalent to those estimated without including gm and no correlation was found between gm and LMA nor between gm and leaf N. Stomatal conductance (gs) and biochemical demand for CO2 were by far the most limiting processes to photosynthesis at all sites along the elevation gradient. Photosynthetic capacity (A) and gs were influenced strongly by differences in soil water availability across the elevation transect, while gm was less responsive to water availability. Based on our analysis, variation in gm plays only a minor role in driving patterns of photosynthesis in P. contorta and P. engelmannii across complex elevational gradients in dry, continental environments of the Rocky Mountains and accurate modeling of photosynthesis, growth and net primary production in these forests may not require detailed estimation of this trait value. [ABSTRACT FROM AUTHOR]

The leaf maximum rate of carboxylation (Vcmax) is a key parameter of plant photosynthetic capacity. The accurate estimation of Vcmax is crucial for correctly predicting the carbon flux in the terrestrial carbon cycle. Vcmax is correlated with plant traits including leaf nitrogen (Narea) and leaf photosynthetic pigments. Proxies for leaf chlorophyll (Chlarea) and carotenoid contents (Cararea) need to be explored in different ecosystems. In this study, we evaluated the relationship between leaf maximum rate of carboxylation (scaled to 25°C; Vcmax25) and both leaf Narea and photosynthetic pigments (Chlarea and Cararea) in winter wheat in a farmland ecosystem. Our results showed that Vcmax25 followed the same trends as leaf Chlarea. However, leaf Narea showed smaller dynamic changes before the flowering stage, and there were smaller seasonal variations in leaf Cararea. The correlation between leaf Vcmax25 and leaf Chlarea was the strongest, followed by leaf Cararea and leaf Narea (R² = 0.69, R² = 0.47 and R² = 0.36, respectively). The random forest regression analysis also showed that leaf Chlarea and leaf Cararea were more important than leaf Narea for Vcmax25. The correlation between leaf Vcmax25 and Narea can be weaker since nitrogen allocation is dynamic. The estimation accuracy of the Vcmax25 model based on Narea, Chlarea, and Cararea (R² = 0.75) was only 0.05 higher than that of the Vcmax25 model based on Chlarea and Cararea (R² = 0.70). However, the estimation accuracy of the Vcmax25 model based on Chlarea and Cararea (R² = 0.70) was 0.34 higher than that of the Vcmax25 model based on Narea (R² = 0.36). These results highlight that leaf photosynthetic pigments can be a predictor for estimating Vcmax25, expanding a new way to estimate spatially continuous Vcmax25 on a regional scale, and to improve model simulation accuracy. [ABSTRACT FROM AUTHOR]

Studying the canopy spectral reflection characteristics of different N-efficient maize varieties and analyzing the relationship between their growth indicators and spectral vegetation indices can help the breeding and application of N-efficient maize varieties. To achieve the optimal management of N fertilizer resources, developing N-efficient maize varieties is necessary. In this research, maize varieties, i.e., the low-N-efficient (Zhengdan 958, ZD958), the high-N efficient (Xianyu 335, XY335), the double-high varieties (Qiule 368, QL368), and the double inefficient-type varieties (Yudan 606 YD606), were used as materials. Results indicate that nitrogen fertilization significantly increased the vegetation indices NDVI, GNDVI, GOSAVI, and RVI of maize varieties with different nitrogen efficiencies. These findings were consistent with the performance of yield, dry matter mass, and leaf nitrogen content and were also found highest under both medium and high nitrogen conditions in the double-high variety QL368. The correlations of dry matter quality, leaf nitrogen content, yield, and vegetation indices (NDVI, GNDVI, RVI, and GOSAVI) at the filling stage of different N-efficient maize varieties were all highly significant and positive. In this relationship, the best effect was found at the filling stages, with correlation coefficients reaching 0.772–0.942, 0.774–0.970, 0754–0.960, and 0.800–0.960. The results showed that the yield, dry matter weight, and leaf nitrogen content of maize varieties with different nitrogen efficiencies increased first and then stabilized with the increase in the nitrogen application level in different periods, and the highest nitrogen application level of maize yield should be between 270 and 360 kg/hm2. At the filling stage, canopy vegetation index of maize varieties with different nitrogen efficiencies was positively correlated with yield, dry matter weight, and leaf nitrogen content, especially GNDVI and GOSAVI on the leaf nitrogen content. It can be used as a means to predict its growth index.

In order to achieve non-destructive monitoring of nitrogen nutrition in winter wheat from multiple angles based on image processing technology, in this paper, winter wheat with different nitrogen operation tests was taken as the research object, the winter wheat canopy was shot by camera from multiple angles, the image of winter wheat canopy was processed based on openCV image processing technology and the color characteristic parameters were extracted, combined with the nitrogen content index of leaves, using multiple linear regression, stepwise multiple linear regression, and BP-neural network, a nitrogen monitoring model of color characteristic parameters under multi-angle conditions was established. The results showed that there was a certain correlation between the canopy image of winter wheat and the nitrogen content of leaves, and the correlation between the five color parameters of R, G, B, reG, and lnG in the RGB color space obtained from the canopy image and the leaf nitrogen content of winter wheat reached an extremely significant level. Different shooting angles caused influence on the accuracy of spectral monitoring of nitrogen content in winter wheat leaves based on image parameters, among which the modeling effect of the canopy image taken with the angle of 60 °(backlight, 30 ° to the direction of the zenith) to canopy horizontal plane was the most accurate and the effect was best(R² =0.896，RMSE=0.572). The angle of 30 °(backlight, 60 ° to the direction of the zenith) to canopy horizontal plane took second place. The angle of 90°(backlight, vertical down, 0 ° to the direction of the zenith) to canopy horizontal plane was the worst. The multiple linear regression model of 60° canopy image established by using the five color characteristic parameters of R, G, B, LnG, and reG performed best. [ABSTRACT FROM AUTHOR]

ABSTRACT Hyperspectral sensors and regression analysis have been used to analyze the most important spectral ranges for biophysical parameters of target crops, aiding in management decision-making. This study aimed to analyze the spectral response of Brachiaria brizantha cv. Marandú leaves to increasing rates of urea fertilization and predict leaf nitrogen content (LNC). Four rates of urea fertilization (0, 25, 50, and 75 kg of N ha-1) were applied. Eight leaves were collected per plot seven times at monthly intervals and subjected to hyperspectral analysis. Leaf spectral responses differed statistically within the visible region, particularly at 550 nm (green). The regression models achieved moderate to good R2 values (0.53 to 0.83) for predicting LNC and identified important wavelengths in the red edge region (715 to 720 nm). These findings demonstrate the potential of spectral analysis to detect changes and forecast leaf nitrogen content in B. brizantha cv. Marandú crops at different fertilization levels.

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha−1) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R2 = 0.75, RMSE = 51.93 kg ha−1, MAE = 29.43 kg ha−1, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year.

Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts. Efficient and non-destructive estimation of crop LNC is of paramount importance for on-field crop management. Spectral technology, with its advantages of repeatability and high-throughput observations, provides a feasible method for obtaining LNC data. This study explores the responsiveness of spectral parameters to soybean LNC at different vertical scales, aiming to refine nitrogen management in soybeans. This research collected hyperspectral reflectance data and LNC data from different leaf layers of soybeans. Three types of spectral parameters, nitrogen-sensitive empirical spectral indices, randomly combined dual-band spectral indices, and “three-edge” parameters, were calculated. Four optimal spectral index selection strategies were constructed based on the correlation coefficients between the spectral parameters and LNC for each leaf layer. These strategies included empirical spectral index combinations (Combination 1), randomly combined dual-band spectral index combinations (Combination 2), “three-edge” parameter combinations (Combination 3), and a mixed combination (Combination 4). Subsequently, these four combinations were used as input variables to build LNC estimation models for soybeans at different vertical scales using partial least squares regression (PLSR), random forest (RF), and a backpropagation neural network (BPNN). The results demonstrated that the correlation coefficients between the LNC and spectral parameters reached the highest values in the upper soybean leaves, with most parameters showing significant correlations with the LNC (p < 0.05). Notably, the reciprocal difference index (VI6) exhibited the highest correlation with the upper-layer LNC at 0.732, with a wavelength combination of 841 nm and 842 nm. In constructing the LNC estimation models for soybeans at different leaf layers, the accuracy of the models gradually improved with the increasing height of the soybean plants. The upper layer exhibited the best estimation performance, with a validation set coefficient of determination (R2) that was higher by 9.9% to 16.0% compared to other layers. RF demonstrated the highest accuracy in estimating the upper-layer LNC, with a validation set R2 higher by 6.2% to 8.8% compared to other models. The RMSE was lower by 2.1% to 7.0%, and the MRE was lower by 4.7% to 5.6% compared to other models. Among different input combinations, Combination 4 achieved the highest accuracy, with a validation set R2 higher by 2.3% to 13.7%. In conclusion, by employing Combination 4 as the input, the RF model achieved the optimal estimation results for the upper-layer LNC, with a validation set R2 of 0.856, RMSE of 0.551, and MRE of 10.405%. The findings of this study provide technical support for remote sensing monitoring of soybean LNCs at different spatial scales.

Abstract Background Atmospheric CO2 may double by the year 2100, thereby altering plant growth, photosynthesis, leaf nutrient contents and water relations. Specifically, atmospheric CO2 is currently 50% higher than pre-industrial levels and is projected to rise as high as 936 μmol mol−1 under worst-case scenario in 2100. The objective of the study was to investigate the effects of elevated CO2 on woody plant growth, production, photosynthetic characteristics, leaf N and water relations. Methods A meta-analysis of 611 observations from 100 peer-reviewed articles published from 1985 to 2021 was conducted. We selected articles in which elevated CO2 and ambient CO2 range from 600–1000 and 300–400 μmol mol−1, respectively. Elevated CO2 was categorized into 700 μmol mol−1 concentrations. Results Total biomass increased similarly across the three elevated CO2 concentrations, with leguminous trees (LTs) investing more biomass to shoot, whereas non-leguminous trees (NLTs) invested to root production. Leaf area index, shoot height, and light-saturated photosynthesis (A max) were unresponsive at 700 μmol mol−1. However, shoot biomass and A max acclimatized as the duration of woody plants exposure to elevated CO2 increased. Maximum rate of photosynthetic Rubisco carboxylation (V cmax) and apparent maximum rate of photosynthetic electron transport (J max) were downregulated. Elevated CO2 reduced stomatal conductance (g s) by 32% on average and increased water use efficiency by 34, 43 and 63% for 700 μmol mol−1, respectively. Leaf N content decreased two times more in NLTs than LTs growing at elevated CO2 than ambient CO2. Conclusions Our results suggest that woody plants will benefit from elevated CO2 through increased photosynthetic rate, productivity and improved water status, but the responses will vary by woody plant traits and length of exposure to elevated CO2.

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LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram–Schmidt Pan Sharpening) was utilized to fuse images from two sensors of digital RGB and multispectral cameras mounted on UAV, and the specific bands of the multispectral cameras are blue, green, red, rededge and NIR. The color space transformation method, HSV (Hue-Saturation-Value), was used to separate soil background noise from crops due to the high spatial resolution of UAV images. Two methods of optimizing feature variables, the Successive Projection Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling method (CARS), combined with two regularization regression algorithms, LASSO and RIDGE, were adopted to estimate the LNC, compared to the commonly used Random Forest algorithm. The results showed that: (1) the accuracy of LNC estimation using the fusion image is improved distinctly by a comparison to the original multispectral image; (2) the denoised images performed better than the original multispectral images in evaluating LNC in rice; (3) the RIDGE-SPA combined method, using SPA to select the MCARI, SAVI and OSAVI, had the best performance for LNC in rice, with an R2 of 0.76 and an RMSE of 10.33%. It can be demonstrated that the information fusion of multiple-sensor imagery from UAV coupling with the methods of optimizing feature variables can estimate the rice LNC more effectively, which can also provide a reference for guiding the decision making of fertilization in rice fields. [ABSTRACT FROM AUTHOR]

Background: Nitrogen (N) is an essential macronutrient for plant growth and development as it is an essential constituent of biomolecules. Its availability directly impacts crop yield. Increased N application in crop fields has caused environmental and health problems, and decreasing nitrogen inputs are in demand to maintain crop production sustainability. Understanding the molecular mechanism of N utilization could play a crucial role in improving the nitrogen use efficiency (NUE) of crop plants. Methods and results: In the present study, the effect of low N supply on plant growth, physio-biochemical, chlorophyll fluorescence attributes, yield components, and gene expression analysis were measured at six developmental stages in rice cultivars. Two rice cultivars were grown with a supply of optimium (120 kg ha−1) and low N (60 kg ha−1). Cultivar Vikramarya excelled Aditya at low N supply, and exhibits enhanced plant growth, physiological efficiency, agronomic efficiency, and improved NUE due to higher N uptake and utilization at low N treatment. Moreover, plant biomass, leaf area, and photosynthetic rate were significantly higher in cv. Vikramarya than cv. Aditya at different growth stages, under low N treatment. In addition, enzymatic activities in cultivar Vikramarya were higher than cultivar Aditya under low nitrogen, indicating its greater potential for N metabolism. Gene expression analysis was carried out for the most important nitrogen assimilatory enzymes, such as nitrate reductase (NR), nitrite reductase (NiR), glutamine synthetase (GS), and glutamate synthase (GOGAT). Expression levels of these genes at different growth stages were significantly higher in cv. Vikramarya compared to cv. Aditya at low N supply. Our findings suggest that improving NUE needs specific revision in N metabolism and physiological assimilation. Conclusion: Overall differences in plant growth, physiological efficiency, biochemical activities, and expression levels of N metabolism genes in N-efficient and N-inefficient rice cultivars need a specific adaptation to N metabolism. Regulatory genes may separately or in conjunction, enhance the NUE. These results provide a platform for selecting crop cultivars for nitrogen utilization efficiency at low N treatment. [ABSTRACT FROM AUTHOR]

Studying the canopy spectral reflection characteristics of different N-efficient maize varieties and analyzing the relationship between their growth indicators and spectral vegetation indices can help the breeding and application of N-efficient maize varieties. To achieve the optimal management of N fertilizer resources, developing N-efficient maize varieties is necessary. In this research, maize varieties, i.e., the low-N-efficient (Zhengdan 958, ZD958), the high-N efficient (Xianyu 335, XY335), the double-high varieties (Qiule 368, QL368), and the double inefficient-type varieties (Yudan 606 YD606), were used as materials. Results indicate that nitrogen fertilization significantly increased the vegetation indices NDVI, GNDVI, GOSAVI, and RVI of maize varieties with different nitrogen efficiencies. These findings were consistent with the performance of yield, dry matter mass, and leaf nitrogen content and were also found highest under both medium and high nitrogen conditions in the double-high variety QL368. The correlations of dry matter quality, leaf nitrogen content, yield, and vegetation indices (NDVI, GNDVI, RVI, and GOSAVI) at the filling stage of different N-efficient maize varieties were all highly significant and positive. In this relationship, the best effect was found at the filling stages, with correlation coefficients reaching 0.772–0.942, 0.774–0.970, 0754–0.960, and 0.800–0.960. The results showed that the yield, dry matter weight, and leaf nitrogen content of maize varieties with different nitrogen efficiencies increased first and then stabilized with the increase in the nitrogen application level in different periods, and the highest nitrogen application level of maize yield should be between 270 and 360 kg/hm2. At the filling stage, canopy vegetation index of maize varieties with different nitrogen efficiencies was positively correlated with yield, dry matter weight, and leaf nitrogen content, especially GNDVI and GOSAVI on the leaf nitrogen content. It can be used as a means to predict its growth index. [ABSTRACT FROM AUTHOR]

Crop models can support the design of smart crop management practices. The Farquhar-von Caemmerer-Berry (FvCB) model is increasingly being used in these models for quantifying leaf photosynthesis. Nitrogen (N) is required for many functional machineries of photosynthesis, thus relationships between FvCB-model parameters and leaf N content (LNC) should be established. We conducted combined gas exchange and chlorophyll fluorescence measurements on fully expanded leaves of two C3 crops, rice (Oryza sativa) and wheat (Triticum aestivum), and two C4 crops, maize (Zea mays) and sorghum (Sorghum bicolor), grown under three N levels. Photosynthetic parameters were estimated and linear relationships between these parameters and LNC were quantified in both C3 and C4 crop types. The efficiency of converting incident light into linear electron transport for C3 crops or into ATP production for C4 crops showed a weak increase with LNC. The maximum electron transport rate (Jmax) for C3 crops or the maximum ATP production rate (Jmax,atp) for C4 crops significantly increased with LNC. The increase in Rubisco carboxylation capacity (Vcmax) with LNC was significantly higher in C3 than in C4 crops. Triose phosphate utilization for C3 crops and PEP carboxylation capacity (Vpmax) for C4 crops increased significantly with LNC as well. Except for Jmax at 21% O2 and Vcmax of C3 crops, there was no significant difference among crops in the relationship between estimated photosynthetic parameters and LNC. The tight associations of photosynthesis parameters with LNC were discussed in view of decision making on N management in the context of smart farming.

Leaf nitrogen concentration (LNC) is a critical indicator of crop nutrient status. In this study, the feasibility of using visible and near-infrared spectroscopy combined with deep learning to estimate LNC in cotton leaves was explored. The samples were collected from cotton’s whole growth cycle, and the spectra were from different measurement environments. The random frog (RF), weighted partial least squares regression (WPLS), and saliency map were used for characteristic wavelength selection. Qualitative models (partial least squares discriminant analysis (PLS-DA), support vector machine for classification (SVC), convolutional neural network classification (CNNC) and quantitative models (partial least squares regression (PLSR), support vector machine for regression (SVR), convolutional neural network regression (CNNR)) were established based on the full spectra and characteristic wavelengths. Satisfactory results were obtained by models based on CNN. The classification accuracy of leaves in three different LNC ranges was up to 83.34%, and the root mean square error of prediction (RMSEP) of quantitative prediction models of cotton leaves was as low as 3.36. In addition, the identification of cotton leaves based on the predicted LNC also achieved good results. These results indicated that the nitrogen content of cotton leaves could be effectively detected by deep learning and visible and near-infrared spectroscopy, which has great potential for real-world application. [ABSTRACT FROM AUTHOR]

Background. The nitrogen (N) and protein concentrations in plant tissues exposed to elevated CO2 (eCO2) generally decline, such declines in forage grass composition are expected to have negative implications for the nutritional and economic value of grass. Plants require N for the production of a photosynthetically active canopy and storage proteins in the tissues, whose functionality will strongly influence productivity and quality. The objective of this study was to investigate whether eCO2 plus N-fertilization increases growth and N nutrition of Agropyron mongolicum, and the dependence of this improvement on the coordination between root and leaf development. Methods. We analyzed A. mongolicum from field-grown within the open-top chambers (OTCs) facility under two atmospheric CO2 (ambient, 400 ± 20 mmol mol-1, aCO2, and elevated, 800±20 μmol mol-1, eCO2) and three N-fertigation treatments (control, low N-fertigation, and high N-fertigation) for two months. Results. Elevated CO2 plus N-fertigation strongly increased shoot and root biomass, and the nitrogen and protein concentrations of A. mongolicum compared to those plants at aCO2 levels. Increased N content in leaves and reduced specific leaf area (SLA) at a highNsupply could alleviate photosynthetic acclimation to eCO2 and drive the production of greater shoot biomass with the potential for higher photosynthesis, productivity, and nutritional quality. The increased root length (RL), the ratio of total aboveground N taken up per RL (TN/RL), stomatal conductance (Gs), and transpiration rate (Tr) contribute to the transpiration-driven mass flow of N, consequently increasingNuptakeby roots. In addition, a smaller percentage of N remained as unassimilated nitrate (NO3-) under eCO2, indicating that assimilation ofNO3-into proteins was not inhibited by eCO2. These findings imply that grass productivity and quality will enhance under anticipated elevated CO2 concentration when effective management measures of N-fertilization are employed. [ABSTRACT FROM AUTHOR]

Background The nitrogen (N) and protein concentrations in plant tissues exposed to elevated CO2 (eCO2) generally decline , such declines in forage grass composition are expected to have negative implications for the nutritional and economic value of grass. Plants require N for the production of a photosynthetically active canopy and storage proteins in the tissues, whose functionality will strongly influence productivity and quality. The objective of this study was to investigate whether eCO2 plus N-fertilization increases growth and N nutrition of Agropyron mongolicum, and the dependence of this improvement on the coordination between root and leaf development. Methods We analyzed A. mongolicum from field-grown within the open-top chambers (OTCs) facility under two atmospheric CO2 (ambient, 400 ± 20 µmol mol−1, aCO2, and elevated, 800 ± 20 µmol mol−1, eCO2) and three N-fertigation treatments (control, low N-fertigation , and high N-fertigation) for two months. Results Elevated CO2 plus N-fertigation strongly increased shoot and root biomass, and the nitrogen and protein concentrations of A. mongolicum compared to those plants at aCO2 levels. Increased N content in leaves and reduced specific leaf area (SLA) at a high N supply could alleviate photosynthetic acclimation to eCO2 and drive the production of greater shoot biomass with the potential for higher photosynthesis, productivity, and nutritional quality. The increased root length (RL), the ratio of total aboveground N taken up per RL (TN/RL), stomatal conductance (Gs), and transpiration rate (Tr) contribute to the transpiration-driven mass flow of N, consequently increasing N uptake by roots. In addition, a smaller percentage of N remained as unassimilated nitrate ( ${\mathrm{NO}}_{3}^{-}$ NO 3 − ) under eCO2, indicating that assimilation of ${\mathrm{NO}}_{3}^{-}$ NO 3 − into proteins was not inhibited by eCO2. These findings imply that grass productivity and quality will enhance under anticipated elevated CO2 concentration when effective management measures of N-fertilization are employed.

Accurate estimation of wheat leaf nitrogen concentration (LNC) is critical for characterizing ecosystem and plant physiological processes; it can further guide fertilization and other field management operations, and promote the sustainable development of agriculture. In this study, a wheat LNC test method based on multi-source spectral data and a convolutional neural network is proposed. First, interpolation reconstruction was performed on the wheat spectra data collected by different spectral instruments to ensure that the number of spectral channels and spectral range were consistent, and multi-source spectral data were constructed using interpolated, reconstructed imaging spectral data and non-imaging spectral data. Afterwards, the convolutional neural network DshNet and machine learning methods (PLSR, SVR, and RFR) were compared under various scenarios (non-imaging spectral data, imaging spectral data, and multi-source spectral data). Finally, the competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the LNC detection model. The results show that the model based on DshNet has the highest test accuracy. The CARS method is more suitable for DshNet model optimization than SPA. In the modeling scenario with non-imaging spectral, imaging spectral, and multi-source spectral, the optimized R2 is 0.86, 0.82, and 0.82, and the RMSE is 0.29, 0.31, and 0.31, respectively. The LNC visualization results show that DshNet modeling using multi-source spectral data is conducive to the visualization expansion of non-imaging spectral data. Therefore, the method presented in this paper provides new considerations for spectral data from different sources and is helpful for related research on the chemometric task of multi-source spectral data.

Background: Atmospheric CO2 may double by the year 2100, thereby altering plant growth, photosynthesis, leaf nutrient contents and water relations. Specifically, atmospheric CO2 is currently 50% higher than pre-industrial levels and is projected to rise as high as 936 μmol mol−1 under worst-case scenario in 2100. The objective of the study was to investigate the effects of elevated CO2 on woody plant growth, production, photosynthetic characteristics, leaf N and water relations. Methods: A meta-analysis of 611 observations from 100 peer-reviewed articles published from 1985 to 2021 was conducted. We selected articles in which elevated CO2 and ambient CO2 range from 600–1000 and 300–400 μmol mol−1, respectively. Elevated CO2 was categorized into < 700, 700 and > 700 μmol mol−1 concentrations. Results: Total biomass increased similarly across the three elevated CO2 concentrations, with leguminous trees (LTs) investing more biomass to shoot, whereas non-leguminous trees (NLTs) invested to root production. Leaf area index, shoot height, and light-saturated photosynthesis (Amax) were unresponsive at < 700 μmol mol−1, but increased significantly at 700 and > 700 μmol mol−1. However, shoot biomass and Amax acclimatized as the duration of woody plants exposure to elevated CO2 increased. Maximum rate of photosynthetic Rubisco carboxylation (Vcmax) and apparent maximum rate of photosynthetic electron transport (Jmax) were downregulated. Elevated CO2 reduced stomatal conductance (gs) by 32% on average and increased water use efficiency by 34, 43 and 63% for < 700, 700 and > 700 μmol mol−1, respectively. Leaf N content decreased two times more in NLTs than LTs growing at elevated CO2 than ambient CO2. Conclusions: Our results suggest that woody plants will benefit from elevated CO2 through increased photosynthetic rate, productivity and improved water status, but the responses will vary by woody plant traits and length of exposure to elevated CO2. [ABSTRACT FROM AUTHOR]

Summary: Plant plastic responses are critical to the adaptation and survival of species under climate change, but whether they are constrained by evolutionary history (phylogeny) is largely unclear. Plant leaf traits are key in determining plants' performance in different environments, and if these traits and their variation are phylogenetically dependent, predictions could be made to identify species vulnerable to climate change.We compiled data on three leaf traits (photosynthetic rate, specific leaf area, and leaf nitrogen content) and their variation under four environmental change scenarios (warming, drought, elevated CO2, or nitrogen addition) for 434 species, from 210 manipulation experiments.We found phylogenetic signal in the three traits but not in their variation under the four scenarios. This indicates that closely related species show similar traits but that their plastic responses could not be predicted from species relatedness under environmental change. Meanwhile, phylogeny weakened the slopes but did not change the directions of conventional pairwise trait relationships, suggesting that co‐evolved leaf trait pairs have consistent responses under contrasting environmental conditions.Phylogeny can identify lineages rich in species showing similar traits and predict their relationships under climate change, but the degree of plant phenotypic variation does not vary consistently across evolutionary clades. [ABSTRACT FROM AUTHOR]

The leaf nitrogen content (LNC) of wheat is one of key bases for wheat nitrogen fertilizer management and nutritional diagnosis, which is of great significance to the sustainable development of precision agriculture. The canopy spectrum provides an effective way to monitor the nitrogen content of wheat. Previous studies have shown that features extracted from the canopy spectrum, such as vegetation indices (VIs) and band positions (BPs), have successfully achieved the monitoring of crop nitrogen nutrition. However, the features mentioned above are spectral features extracted on the basis of linear or nonlinear combination models with a simple structure, which limits the general applicability of the model. In addition, models based on spectral features are prone to overfitting, which also reduces the accuracy of the model. Therefore, we propose an estimation model based on multimodal features (convolutional features and VIs, BPs) of the canopy spectrum, which aim to improve accuracy in estimating wheat LNC. Among these, the convolutional features (CFs) extracted by the designed convolutional neural network represent the deep semantic information of the canopy reflection spectrum, which can make up for the lack of robustness of the spectral features. The results showed that the accuracy of the model based on the fusion features (VIs + BPs + CFs) was higher than that of the feature of single modality. Moreover, the particle swarm optimization–support vector regression (PSO-SVR) model based on multimodal features had the best prediction effect (R2 = 0.896, RMSE = 0.188 for calibration, R2 = 0.793, RMSE = 0.408 for validation). Therefore, the method proposed in this study could improve performance in the estimation of wheat LNC, which provides technical support for wheat nitrogen nutrition monitoring. [ABSTRACT FROM AUTHOR]

Improving leaf photosynthetic capacity is one of the most promising approaches to further boost crop yield. Clarifying factors limiting leaf photosynthetic capacity, especially in C3 crops, is meaningful for designing strategies to improve it. Leaf net photosynthetic rate (A) is one of the parameters describing leaf photosynthetic capacity. In the present study, physiological factors limiting A in C3 crops such as rice were discussed and different approaches for A improvement were summarized to provide theoretical guidance for increasing leaf photosynthetic capacity. A will be limited by both CO2 availability and light intensity over periods from a few hours to several days, and by one of them over shorter intervals. Under current ambient atmospheric conditions, A of C3 crops is mainly limited by Rubisco activity and the CO2 concentration in chloroplasts. Leaf nitrogen content affects A by regulating Rubisco content and leaf anatomy; leaf morphological and anatomical traits limit A by impacting stomatal and mesophyll CO2 diffusion. Further improvements of A in C3 crops can be achieved by designing or introducing high-activity Rubisco; adjusting leaf nitrogen allocation to optimize leaf anatomy and leaf chemical composition; modifying leaf morphology and anatomy for greater CO2 diffusion; improving the activity of proteins and enzymes associated with sugar transportation and utilization; introducing C4 photosynthetic mechanisms and combining high photosynthetic traits by conventional breeding. [ABSTRACT FROM AUTHOR]

Rapid and accurate prediction of crop nitrogen content is of great significance for guiding precise fertilization. In this study, an unmanned aerial vehicle (UAV) digital camera was used to collect cotton canopy RGB images at 20 m height, and two cotton varieties and six nitrogen gradients were used to predict nitrogen content in the cotton canopy. After image-preprocessing, 46 hand features were extracted, and deep features were extracted by convolutional neural network (CNN). Partial least squares and Pearson were used for feature dimensionality reduction, respectively. Linear regression, support vector machine, and one-dimensional CNN regression models were constructed with manual features as input, and the deep features were used as inputs to construct a two-dimensional CNN regression model to achieve accurate prediction of cotton canopy nitrogen. It was verified that the manual feature and deep feature models constructed from UAV RGB images had good prediction effects. R2 = 0.80 and RMSE = 1.67 g kg−1 of the Xinluzao 45 optimal model, and R2 = 0.42 and RMSE = 3.13 g kg−1 of the Xinluzao 53 optimal model. The results show that the UAV RGB image and machine learning technology can be used to predict the nitrogen content of large-scale cotton, but due to insufficient data samples, the accuracy and stability of the prediction model still need to be improved. [ABSTRACT FROM AUTHOR]

Estimation of nitrogen content in crop leaves using hyperspectral techniques encounters the challenge of limited model stability. In this study, a method for the construction of nitrogen estimation model of oilseed rape (Brassica napus L.) was proposed, which combines the spectral features of nitrogen and nitrogen-containing physiological components. Based on the pot experiment, a total of 320 sets of canopy spectra were collected in the five growth stages using hyperspectral technology, and the concentration of leaf nitrogen (LNC), malondialdehyde (MDA), proline, soluble sugar (SS), and water were determined in indoor chemical experiments. Original spectra was preprocessed by five kinds of spectral conversions, and the random frog (RF) method was used to screen the spectral features of nitrogen and nitrogen-containing physiological components that were used to construct the partial least square regression (PLSR) and support vector machine (SVM) models. The results showed that the selection of spectral features of LNC and nitrogen-containing physiological components based on RF greatly reduced the collinearity and redundant information among bands. The accuracy of the models based on the spectral features of LNC and nitrogen-containing physiological components were higher than that of the model based on the spectral features of single LNC. Further interannual test showed that the prediction accuracy of the N + MDA + SS model was higher than that of other models, especially at the rapid growth stage, with R2 of 0.871. This study provides a new idea for improving the accuracy and stability of crop nitrogen estimation model. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) image spectral information and texture feature (TF) information were fused to develop an improved model for winter wheat leaf nitrogen content (LNC) monitoring model to provide a reference for wheat nitrogen status monitoring and accurate management. The data of wheat LNC and UAV-hyperspectral imaging were simultaneously obtained at the main growth stages (jointing, booting, and filling stages) of various winter wheat varieties under various nitrogen fertilizer treatments. The correlation between the vegetation indexes (VIs) in combination of any two bands, the TFs, and LNC were systematically analyzed. Then, the optimal VIs and TFs without multicollinearity problems were screened using a variance inflation factor (VIF) to form a 'graph–spectrum' fusion index. Four machine learning methods, namely ridge regression (RR), partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), were used to construct respective quantitative winter wheat LNC estimation models. The results revealed that the model for estimating LNC constructed using the 'graph–spectrum' information formed by eight parameters, including the normalized vegetation index NDVI (R578, R490), the difference vegetation index DVI (R830, R778), MEA490, MEA778, VAR490, VAR578, VAR778, and HOM578 as input and the RR algorithm, performed the best. It outperformed the models developed by the implementation of VIs and TFs as input. The coefficient of determination (R2), root mean square error (RMSE), and relative percent deviation (RPD) of the calibration set were 0.84, 0.25, and 2.50, correspondingly, and those of the validation set were 0.87, 0.27, and 2.33, respectively. The model of winter wheat LNC constructed by fusing spectral and TF information considerably improved the prediction accuracy. The present research results provide a basis and reference for the application of UAV hyperspectral technology in wheat nitrogen nutrient monitoring. [ABSTRACT FROM AUTHOR]

Canopy spectral reflectance can indicate both crop nutrient and canopy structural information. Differences in canopy structure can affect spectral reflectance. However, a non-imaging spectrometer cannot distinguish such differences while monitoring crop nutrients, because the results are likely to be influenced by the canopy structure. In addition, nitrogen application rate is one of the main factors influencing the canopy structure of crops. Strong correlations exist between indices of canopy structure and leaf nitrogen, and thus, these can be used to compensate for the spectral monitoring of nitrogen content in wheat leaves. In this study, canopy structural indices (CSI) such as wheat coverage, height, and textural features were obtained based on the RGB and height images obtained by the RGB-D camera. Moreover, canopy spectral reflectance was obtained by an ASD hyperspectral spectrometer, based on which two vegetation indices—ratio vegetation index (RVI) and angular insensitivity vegetation index (AIVI)—were constructed. With the vegetation indices and CSIs as input parameters, a model was established to predict the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) of wheat based on partial least squares (PLS) and random forest (RF) regression algorithms. The results showed that the RF model with RVI and CSI as inputs had the highest prediction accuracy for LNA, the coefficient of determination (R2) reached 0.79, and the root mean square error (RMSE) was 1.54 g/m2. The vegetation indices and coverage were relatively important features in the model. In addition, the PLS model with AIVI and CSI as input parameters had the highest prediction accuracy for LNC, with an R2 of 0.78 and an RMSE of 0.35%, among the vegetation indices. In addition, parts of both the textural and height features were important. The results suggested that PLS and RF regression algorithms can effectively integrate spectral and canopy structural information, and canopy structural information effectively supplement spectral information by improving the prediction accuracy of vegetation indices for LNA and LNC. [ABSTRACT FROM AUTHOR]

The experiment on nitrogen fertilizer gradient of maize under drip irrigation in Ningxia Pingjibao farm was conducted from 2018 and 2019. The variety of Tianci 19 was used as an experimental material, six nitrogen level treatments were set, which was 0(N0), 90(N1), 180(N2), 270(N3), 360(N4) and 450(N5)kg(N)·ha-1, respectively. P and K fertilizers was conventional fertilization treatment, which was 138kg·ha-1 and 120kg·ha-1. Using drip irrigation water and fertilizer integration technology, fertilizer was applied along with water. The proportion of fertilizer application in each growth stage was 10% in seedling stage, 45% in six leaves stage, 20% in silking stage and 25% in filling stage. Maize canopy images were obtained by UAV equipped with digital camera in the maize six leaves stage(V6), ten leaves stage(V10), twelve leaves stage(V12), silking stage(R1) and milk stage(R3), and used Matlab code developed by digital image recognition system to extract the corn canopy image the red light value R, green light value G and blue light value B. The correlation between 10 canopy image parameters and nitrogen nutrition indexes based on this calculation was studied, and the image color parameters with good stability and high sensitivity were screened out. The relationship model between diagnostic indices of maize nitrogen nutrition and image parameters was constructed and verified. In order to explore the feasibility of using UAV image to estimate the dynamic nitrogen nutrition of maize under drip irrigation in Ningxia Yellow River Irrigation area during the period of joining and milk ripening. The results showed that the ratio of green light to red light (G/R), standardized value of green light (NGI), and red-green-blue vegetation index (RGBVI) were highly correlated with plant nitrogen content and leaf nitrogen content and had small coefficient of variation, which could be used as the potential optimal color parameters for nitrogen nutrition diagnosis. The regression model of optimal color parameters with nitrogen content in plant and leaf was constructed, and the power function model could better predict nitrogen status of maize. The same nitrogen experiment in 2019 was used to verify the model. It was found that the R² of measured and estimated value of NGI and plant nitrogen concentration and leaf nitrogen concentration were 0.738 and 0.689, respectively. The test indexes RMSE were 2.594 and 3.014, and nRMSE were 13.125% and 13.347%. The prediction accuracy and accuracy are higher than G/R and RGBVI. Therefore, NGI can be selected as the optimal parameter for dynamic diagnosis of nitrogen nutrition in drip irrigation maize at the stage of V6-R3 stage, and the correlation model between parameter NGI and plant nitrogen concentration (NP=4.967×106NGI14.26) R² was 0.707. The R² of the model (NL=1.707×106NGI12.88) was 0.654. The results showed that the application of UAV image technology could dynamically estimate the nitrogen nutrition status of maize during the period of V6-R3 stage in Ningxia Yellow River Irrigation area, and the nitrogen nutrition diagnostic model constructed could provide a theoretical basis for the precise allocation of nitrogen fertilizer for drip irrigation maize in Ningxia Yellow River irrigation area. [ABSTRACT FROM AUTHOR]

Traditional gas exchange measurements are cumbersome, which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (Vcmax25) and the maximum electron transport at a reference temperature (Jmax25), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of Vcmax25 and Jmax25; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, Vcmax25 and Jmax25 were measured over multiple growth stages during two growing seasons for field‐grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). Spectral vegetation indices and machine learning methods were used to build predictive models for Vcmax25 and Jmax25, based on the leaf reflectance. Results showed that these models yielded an R2 of 0.57–0.65 and 0.48–0.58 for Vcmax25 and Jmax25, respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity. Seasonally dependent acclimation of leaf photosynthetic biochemistry was found in field‐grown soybean under full season warming. Hyperspectral reflectance measurements coupled with machine learning regressions can be used to predict photosynthetic biochemical acclimation to high temperature. [ABSTRACT FROM AUTHOR]

LNC (leaf nitrogen content) in crops is significant for diagnosing the crop growth status and guiding fertilization decisions. Currently, UAV (unmanned aerial vehicles) remote sensing has played an important role in estimating the nitrogen nutrition of crops at the field scale. However, many existing methods of evaluating crop nitrogen based on UAV imaging techniques usually have used a single type of imagery such as RGB or multispectral images, seldom considering the usage of information fusion from different types of UAV imagery for assessing the crop nitrogen status. In this study, GS (Gram–Schmidt Pan Sharpening) was utilized to fuse images from two sensors of digital RGB and multispectral cameras mounted on UAV, and the specific bands of the multispectral cameras are blue, green, red, rededge and NIR. The color space transformation method, HSV (Hue-Saturation-Value), was used to separate soil background noise from crops due to the high spatial resolution of UAV images. Two methods of optimizing feature variables, the Successive Projection Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling method (CARS), combined with two regularization regression algorithms, LASSO and RIDGE, were adopted to estimate the LNC, compared to the commonly used Random Forest algorithm. The results showed that: (1) the accuracy of LNC estimation using the fusion image is improved distinctly by a comparison to the original multispectral image; (2) the denoised images performed better than the original multispectral images in evaluating LNC in rice; (3) the RIDGE-SPA combined method, using SPA to select the MCARI, SAVI and OSAVI, had the best performance for LNC in rice, with an R2 of 0.76 and an RMSE of 10.33%. It can be demonstrated that the information fusion of multiple-sensor imagery from UAV coupling with the methods of optimizing feature variables can estimate the rice LNC more effectively, which can also provide a reference for guiding the decision making of fertilization in rice fields.

Introduction: Individual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed., Methods: In this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square ( R 2 ), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model., Results: (i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients ( r ) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R 2 , RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yin, Wang, Lv, Qin, Ma, Zhang and Tang.)

Investigating the effects of ontogenetic stage and leaf age on leaf traits is important for understanding the utilization and distribution of resources in the process of plant growth. However, few studies have been conducted to show how traits and trait-trait relationships change across a range of ontogenetic stage and leaf age for evergreen coniferous species. We divided 67 Pinus koraiensis Sieb. et Zucc. of various sizes (0.3–100 cm diameter at breast height, DBH) into four ontogenetic stages, i.e., young trees, middle-aged trees, mature trees and over-mature trees, and measured the leaf mass per area (LMA), leaf dry matter content (LDMC), and mass-based leaf nitrogen content (N) and phosphorus content (P) of each leaf age group for each sampled tree. One-way analysis of variance (ANOVA) was used to describe the variation in leaf traits by ontogenetic stage and leaf age. The standardized major axis method was used to explore the effects of ontogenetic stage and leaf age on trait-trait relationships. We found that LMA and LDMC increased significantly and N and P decreased significantly with increases in the ontogenetic stage and leaf age. Most trait-trait relationships were consistent with the leaf economic spectrum (LES) at a global scale. Among them, leaf N content and LDMC showed a significant negative correlation, leaf N and P contents showed a significant positive correlation, and the absolute value of the slopes of the trait-trait relationships showed a gradually increasing trend with an increasing ontogenetic stage. LMA and LDMC showed a significant positive correlation, and the slopes of the trait-trait relationships showed a gradually decreasing trend with leaf age. Additionally, there were no significant relationships between leaf N content and LMA in most groups, which is contrary to the expectation of the LES. Overall, in the early ontogenetic stages and leaf ages, the leaf traits tend to be related to a "low investment-quick returns" resource strategy. In contrast, in the late ontogenetic stages and leaf ages, they tend to be related to a "high investment-slow returns" resource strategy. Our results reflect the optimal allocation of resources in Pinus koraiensis according to its functional needs during tree and leaf ontogeny. [ABSTRACT FROM AUTHOR]

Remote estimation of leaf nitrogen content is a critical requirement for precision farm management. Precise knowledge of nitrogen distribution in the crop enables farmers to decide the fertilisation amount required at specific locations on the farm. Importantly, nitrogen related molecules in plants are transported using water molecules, and water molecules surround the amide bonds (a plant protein created from nitrogen). Consequently, the nitrogen in various crop parts loses its activity in the absence of sufficient water molecules. The association of water molecules around plant proteins makes the optical remote estimation of plant nitrogen challenging as nitrogen and water molecules simultaneously affect the reflectance data. Moreover, the coarse spatial resolution of satellite data and sparse canopy coverage at early growth stages of the crop make it challenging to estimate leaf-level nitrogen contents. Accordingly, this research developed a leaf nitrogen content estimation model using drone-based top-of-canopy 400–1000 nm pure pixel hyperspectral images collected from a maize research farm treated with different water and nitrogen levels. Leaf level spectral signatures were also collected using a field spectroradiometer and used to identify indices more sensitive to nitrogen than water. The leaves were also destructively sampled for obtaining ground truth leaf water and nitrogen content. Red-edge region bands of electromagnetic spectra were identified to be sensitive to leaf nitrogen content. A synthetic data was created using maximum and minimum values of these indices and crop growth stage information, which was further used for training a gradient-boosting machine model to estimate leaf nitrogen content from drone-based hyperspectral images. The estimated leaf nitrogen content values from drone observations were critically analysed with respect to leaf water content values. For water-stressed areas, the model gave an R2 and RMSE of 0.63 and 2.74 mg/g, respectively. However, the model did not perform adequately for well irrigated areas, having an R2 and RMSE of 0.26 and 4.54 mg/g, respectively.

Predicting leaf nitrogen content (LNC) using unmanned aerial vehicle (UAV) images is of great significance. Traditional LNC prediction methods based on empirical and mechanistic models have limitations. This study aimed to propose a new LNC prediction method based on combining deep learning methods and mechanistic models. Wheat field experiments were conducted to make plants with different LNC values. The LNC and UAV hyperspectral images were collected during the critical growth stages of wheat. Based on these data, a method combining the deep multitask learning method and the N-based PROSAIL model was proposed and compared with traditional LNC prediction methods, including spectral index (SI), partial least squares regression (PLSR) and artificial neural network (ANN) methods. The results show that the new proposed method obtained the best LNC prediction results, with R2, RMSE and RMSE% values of 0.79, 20.86 μg/cm2 and 18.63%, respectively, during calibration and 0.82, 18.40 μg/cm2 and 16.92%, respectively, during validation. The other methods obtained R2, RMSE and RMSE% values between 0.29 and 0.68, 25.71 and 38.52 μg/cm2 and 22.95 and 34.39%, respectively, during calibration and between 0.43 and 0.74, 22.79 and 33.55 μg/cm2 and 20.96 and 30.86%, respectively, during validation. Thus, this study provides an accurate LNC prediction tool for precise nitrogen (N) management in the field.

Improving leaf photosynthetic capacity is one of the most promising approaches to further boost crop yield. Clarifying factors limiting leaf photosynthetic capacity, especially in C3 crops, is meaningful for designing strategies to improve it. Leaf net photosynthetic rate (A) is one of the parameters describing leaf photosynthetic capacity. In the present study, physiological factors limiting A in C3 crops such as rice were discussed and different approaches for A improvement were summarized to provide theoretical guidance for increasing leaf photosynthetic capacity. A will be limited by both CO2 availability and light intensity over periods from a few hours to several days, and by one of them over shorter intervals. Under current ambient atmospheric conditions, A of C3 crops is mainly limited by Rubisco activity and the CO2 concentration in chloroplasts. Leaf nitrogen content affects A by regulating Rubisco content and leaf anatomy; leaf morphological and anatomical traits limit A by impacting stomatal and mesophyll CO2 diffusion. Further improvements of A in C3 crops can be achieved by designing or introducing high-activity Rubisco; adjusting leaf nitrogen allocation to optimize leaf anatomy and leaf chemical composition; modifying leaf morphology and anatomy for greater CO2 diffusion; improving the activity of proteins and enzymes associated with sugar transportation and utilization; introducing C4 photosynthetic mechanisms and combining high photosynthetic traits by conventional breeding.

The leaf nitrogen content (LNC) of wheat is one of key bases for wheat nitrogen fertilizer management and nutritional diagnosis, which is of great significance to the sustainable development of precision agriculture. The canopy spectrum provides an effective way to monitor the nitrogen content of wheat. Previous studies have shown that features extracted from the canopy spectrum, such as vegetation indices (VIs) and band positions (BPs), have successfully achieved the monitoring of crop nitrogen nutrition. However, the features mentioned above are spectral features extracted on the basis of linear or nonlinear combination models with a simple structure, which limits the general applicability of the model. In addition, models based on spectral features are prone to overfitting, which also reduces the accuracy of the model. Therefore, we propose an estimation model based on multimodal features (convolutional features and VIs, BPs) of the canopy spectrum, which aim to improve accuracy in estimating wheat LNC. Among these, the convolutional features (CFs) extracted by the designed convolutional neural network represent the deep semantic information of the canopy reflection spectrum, which can make up for the lack of robustness of the spectral features. The results showed that the accuracy of the model based on the fusion features (VIs + BPs + CFs) was higher than that of the feature of single modality. Moreover, the particle swarm optimization–support vector regression (PSO-SVR) model based on multimodal features had the best prediction effect (R2 = 0.896, RMSE = 0.188 for calibration, R2 = 0.793, RMSE = 0.408 for validation). Therefore, the method proposed in this study could improve performance in the estimation of wheat LNC, which provides technical support for wheat nitrogen nutrition monitoring.

Canopy spectral reflectance can indicate both crop nutrient and canopy structural information. Differences in canopy structure can affect spectral reflectance. However, a non-imaging spectrometer cannot distinguish such differences while monitoring crop nutrients, because the results are likely to be influenced by the canopy structure. In addition, nitrogen application rate is one of the main factors influencing the canopy structure of crops. Strong correlations exist between indices of canopy structure and leaf nitrogen, and thus, these can be used to compensate for the spectral monitoring of nitrogen content in wheat leaves. In this study, canopy structural indices (CSI) such as wheat coverage, height, and textural features were obtained based on the RGB and height images obtained by the RGB-D camera. Moreover, canopy spectral reflectance was obtained by an ASD hyperspectral spectrometer, based on which two vegetation indices—ratio vegetation index (RVI) and angular insensitivity vegetation index (AIVI)—were constructed. With the vegetation indices and CSIs as input parameters, a model was established to predict the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) of wheat based on partial least squares (PLS) and random forest (RF) regression algorithms. The results showed that the RF model with RVI and CSI as inputs had the highest prediction accuracy for LNA, the coefficient of determination (R2) reached 0.79, and the root mean square error (RMSE) was 1.54 g/m2. The vegetation indices and coverage were relatively important features in the model. In addition, the PLS model with AIVI and CSI as input parameters had the highest prediction accuracy for LNC, with an R2 of 0.78 and an RMSE of 0.35%, among the vegetation indices. In addition, parts of both the textural and height features were important. The results suggested that PLS and RF regression algorithms can effectively integrate spectral and canopy structural information, and canopy structural information effectively supplement spectral information by improving the prediction accuracy of vegetation indices for LNA and LNC.

• In the Pinus yunnanensis forest, there existed synergistic pairs of plant functional traits, such as those between LNC and LPC, LNC and SLA, LPC and SLA, as well as N:P and LDMC. • Trade-offs were identified between LNC and LDMC, between LPC and N:P, and between LPC and LDMC. • The intensity of soil factors affected by selective logging was stronger than that of plant functional traits. • The impact of selective logging on plant functional traits was primarily observed through direct effects on soil enzyme activities and nutrient cycles, although the mechanisms of influence varied among different functional traits. • To gain a better understanding of the relationship between soil factors and plant functional traits, future research should adopt a network approach. Selective logging has a wide impact on the remaining trees and soils which in turn influences total ecosystem function. Lots of studies focus on effects of selective logging on changing of plant functional traits, but the mechanism underlying these effects is still unclear. Thus, we studied 6 plant functional traits including leaf N concentration (LNC), leaf P concentration (LPC), leaf N:P ratio (N:P), wood density (WD), specific leaf area (SLA), and leaf dry mass content (LDMC) typically associated with resource acquisition and allocation. We measured these traits across 13 tree species in 52 plots under 5 selective logging intensities in a Pinus yunnanensis forest and aimed to test how selective logging and soil factors affect community weighted mean (CWM) of plant functional traits. The results revealed significant positive correlations between LNC and LPC, LNC and SLA, LPC and SLA, N:P and LDMC. Significant negative correlations were found between LNC and LDMC, LPC and N:P, LPC and LDMC. Only soil β-glucosidase activity (SGA), soil phosphatase activity (SPA), soil total nitrogen (STN), soil total phosphorus (STP) and soil hydrolysable nitrogen (SHN) had a significant relationship with one or several of the 6 plant functional traits. Compared with the control group, there was little significant difference in all plant functional traits across the different selective logging intensities. However, for plant functional traits, selective logging significantly increased the LNC, LPC and SLA. For soil factors, selective logging significantly increased the SGA, soil urease activity (SUA), SPA, soil moisture (SM), STN, STP and SHN. Based on structural equation models, we found that although selective logging has no direct effect on plant functional traits, it can indirectly affect plant functional traits through soil factors, mainly soil enzyme activity and nutrient cycling. Meanwhile, the mechanism of selective logging affecting different functional traits may be varied. Our findings provide a theoretical basis for the scientific management of P. yunnanensis forest. [ABSTRACT FROM AUTHOR]

Nitrogen nutrition is strongly associated with crop growth and development. Nitrogen application level affects leaf size as well as nitrogen content and distribution, and thus affects photosynthetic nitrogen-use efficiency (PNUE) and yield. In this study, soybean varieties "Jinyuan 55" and "Keshan 1" were treated with nitrogen as urea at: N0, 0 kg hm−2; N0.5, 60 kg hm−2; N1, 120 kg hm−2; and N1.5, 180 kg hm−2. We compared the effect of nitrogen level on plant morphology, biomass, photosynthetic physiology, nitrogen distribution, PNUE, and other soybean seedling leaf characteristics. Maximum carboxylation and electron transfer, net photosynthetic rates, and PNUE of both soybean varieties showed initial significant increases with increasing nitrogen application rate and subsequent stabilization. PNUE, carboxylation system components, electron transport components, and non-photosynthetic system distribution ratios in the photosynthetic system increased and subsequently decreased with increased nitrogen application rate. The nitrogen ratio between carboxylation and electron transport systems was positively correlated with PNUE in both soybean varieties. The nitrogen ratio in light-harvesting and non-photosynthetic systems showed a linear negative correlation with PNUE. Overall, an appropriate nitrogen level maintained a high photosynthetic nitrogen ratio, whereas low- or high-nitrogen conditions increased or decreased the nitrogen ratio in non-photosynthetic and photosynthetic systems, respectively, thus decreasing the PNUE and photosynthetic capacity. Moreover, increased nitrogen application rate led to a decreased nitrogen ratio in the light-harvesting system and an increased nitrogen ratio of electron transport and carboxylation systems. Our results provide a theoretical basis for optimizing leaf nitrogen distribution, determining optimum nitrogen levels, and promoting soybean seedling growth. [Display omitted] [ABSTRACT FROM AUTHOR]

Fast, accurate, and real-time detection of nitrogen content in tobacco leaves is of great significance for monitoring the quality of tobacco leaves. Hyperspectral remote sensing (HRS) coupled with unmanned aerial vehicle (UAV) platform can provide unprecedented spectral information of field plants on a large scale. And with the support of various machine learning algorithms, a series of efficient models for leaf nitrogen content (LNC) assessment can be developed. This study aimed to develop a high-performance model to estimate the LNC of tobacco using UAV-borne HRS image data. Meanwhile, to cope with the heterogeneous performance problem of the individual model, ensemble learning strategies were applied to assemble multiple estimators, including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), adaptive boosting (Adaboost), and stacking to mine more valid data features. To accurately assess the performance of the established models, the coefficient of determination (R 2), root mean square error (RMSE), and mean absolute percentage error (MAPE) were introduced as the evaluation indicators, and partial least squares regression (PLSR) was selected as the baseline model. Results on the test set showed that all ensemble learning methods outperformed PLSR (R 2 =0.680, RMSE=5.402 mg/g, 19.72%). Specifically, the stacking-based models achieved the highest accuracy as well as relatively high stability (R 2 =0.745, RMSE=4.825 mg/g, 17.98%). This study provides a reference for efficient and non-destructive detection of LNC or other vegetation phenotypic traits using UAV-borne HRS technology. • A new attempt using drones-based hyperspectral for tobacco nitrogen estimation. • Three ensemble strategies performed well in predicting nitrogen content of tobacco leaves. • Exploring methods to improve the prediction accuracy and stability of the stacking strategy. [ABSTRACT FROM AUTHOR]