Search Results (26)

Leaf nitrogen content (LNC) is an important physiological indicator for evaluating citrus nutritional status, photosynthetic capacity, and fertilization demand. However, conventional LNC determination mainly relies on field sampling and laboratory chemical analysis, which are destructive, time-consuming, labor-intensive, and limited in spatial continuity, making them unsuitable for large-scale real-time nitrogen monitoring in complex orchard environments. To achieve rapid and non-destructive estimation of citrus LNC, this study developed a UAV multispectral inversion framework integrating object-based canopy extraction and machine learning models. Field experiments were conducted in a citrus orchard in western Hubei Province, China. Multi-temporal UAV multispectral images were collected from April to October 2025, and ground measurements of citrus LNC were collected simultaneously. First, minimum distance classification (MDC), maximum likelihood classification (MLC), and object-based image analysis (OBIA) were used for land-cover classification of citrus orchard images, and their canopy extraction performance under complex orchard backgrounds was compared. Subsequently, multiple vegetation indices were calculated from the extracted citrus canopy spectra, and sensitive spectral features were selected through correlation analysis. Finally, seven models, including simple linear regression, quadratic regression, partial least squares regression (PLS), back propagation neural network (BP), extreme learning machine (ELM), particle swarm optimization-extreme learning machine (PSO-ELM), and particle swarm optimization-back propagation neural network (PSO-BP), were constructed to systematically evaluate the inversion performance of citrus LNC across the entire growth period. The results showed that: (1) OBIA achieved higher classification accuracy and temporal stability in citrus orchard land-cover classification, with overall accuracy ranging from 68.86% to 85.65% and Kappa coefficients ranging from 0.56 to 0.72, outperforming MDC and MLC. This indicates that OBIA can effectively reduce the interference of bare soil, grass, shadows, and other non-target objects on canopy spectral extraction. (2) The correlations between vegetation indices and LNC varied markedly among different growth stages, suggesting that the spectral response of citrus LNC has strong phenological dependence and that a single vegetation index is insufficient to stably characterize LNC variation across the whole growth period. (3) At the whole-growth-period scale, multi-index fusion models outperformed single-index models, among which EVI, TVI, and MTVI showed relatively strong cross-stage sensitivity. (4) Optimized machine learning models generally outperformed traditional regression models and unoptimized machine learning models. Among them, PSO-BP achieved the best performance, with a validation R² of 0.68 and an RMSE of 1.54 g kg⁻¹, representing an increase in R² of 23.64% compared with the PLS model and 25.93% over the baseline BP model in terms of R². Overall, this study demonstrates that OBIA-based canopy spectral quality improvement combined with PSO-optimized machine learning can effectively improve the stability and reliability of UAV multispectral estimation of citrus LNC under complex orchard backgrounds. The proposed framework provides technical support for citrus nitrogen diagnosis, precision fertilization, and intelligent orchard management. Full article

Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized ‘Yotsuboshi’ strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model’s classifications of ‘NormalN’ and ‘LowN’ statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions. Full article

Nitrogen (N) and phosphorus (P) are essential nutrients that play critical roles in plant physiological processes and the accumulation of N and P in broccoli head was significantly correlated with yield. Therefore, there is a need for a rapid, non-destructive diagnosis of crop status by detecting deficiencies in essential nutrients. This study evaluated the effects of N and P deficiency on field grown broccoli (Brassica oleracea L. var. italica Plenk) using a plant-induced electrical signal (PIES) sensor, in which needle electrodes are inserted into the stem to measure electrical conductivity reflecting plant water and ion status. Four treatments were established, including the control (N100P100) with sufficient N and P supply, N deficiency (N0P100), P deficiency (N100P0), and combined N–P deficiency (N0P0). For sufficient supply, urea and fused phosphate (FP) were applied at rates of 122 kg N ha⁻¹ and 71 kg P ha⁻¹, respectively. Soil, stem, and leaf nutrient contents, growth parameters, and stress related indicators were analyzed and their relationship with PIES values were evaluated. PIES was highest in control (N100P100) and lowest under N–P deficiency (N0P0). Higher PIES values were observed during the vegetative stage, whereas values declined during the reproductive stage, reflecting changes in physiological activity. Growth parameters such as shoot and root weight and stem diameter were generally superior in the control (N100P100) treatment, while leaf calcium (Ca), magnesium (Mg), and potassium (K) concentrations showed no significant differences among treatments. Total N content in leaves was higher in N fertilized treatments (control and P deficiency). Photosynthesis-related parameters, including soil plant analysis development (SPAD), Fv/Fm, and chlorophyll content, were lowest under N–P deficiency, which was reflected in the PIES. Principal component analysis (PCA) showed that the PIES was closely associated with growth and photosynthetic parameters and clearly distinguished N sufficient treatments (control and P deficiency) from N deficient treatments (N0P100, N0P0). Overall, these findings suggest that PIES monitoring can serve as a sensitive physiological indicator of nutrient stress and may be applied as an early diagnostic tool before visible growth inhibition occurs in broccoli cultivation. Full article

Rapid and accurate diagnosis of nitrogen (N) and phosphorus (P) is crucial for Hydrangea macrophylla nursery management. Traditional methods are time-consuming, and existing non-destructive studies rarely target ornamental plants or support joint N-P diagnosis at the early growth stage. A total of 339 RGB images were captured from potted hydrangeas grown under varying N and P levels at the seedling stage, with 65 phenotypic traits (color, texture, and morphology) extracted. Nutritional status (deficient, optimal, and surplus) was categorized with reference to plant nutrition indices. Discriminant models were then developed using four machine learning algorithms: convolutional neural network (CNN), support vector machine (SVM), random forest (RF), and probabilistic neural network (PNN). The model performances were evaluated using overall accuracy, precision, recall, F1-score, and Cohen’s Kappa coefficient (κ). As a result, CNN achieved 82.65% accuracy (κ = 0.7392) for N classification, and SVM reached 83.65% accuracy (κ = 0.7357) for P classification. Color-related traits dominated the top five contributing features, indicating a stronger correlation with N and P status. This work offers a practical solution for real-time, low-cost, and non-destructive nutrient diagnosis, supporting precision fertilization and enhancing environmental sustainability in nursery production. Full article

Optimising nitrogen (N) management is crucial for improving garlic (Allium sativum L.) productivity and nitrogen use efficiency (NUE). This study evaluated the effects of N application rate on bulb yield, NUE, and the links between N status, Normalised Difference Eed Edge (NDRE), and yield. A field experiment was conducted using nine N rates (0–360 kg N ha⁻¹) across three garlic varieties (Glenlarge, Southern Glen and AV08). Foliage and bulb N concentrations were measured at key growth stages, and yield components were determined at 185 days after planting (DAP). NDRE values were collected at 147 DAP to evaluate their potential as non-destructive indicators of crop N status and yield. Nitrogen rate significantly affected bulb yield, with maximum yield achieved at 160–240 kg N ha⁻¹. Increasing the N application rate reduced dry matter content and NUE across three garlic varieties. NDRE showed strong positive correlations with N rate (r² = 0.96), leaf N concentration (r² ≥ 0.82), and bulb yield (r² = 0.85), demonstrating its sensitivity to in-season N variability and its potential for yield prediction. This study provides a systematic assessment of NDRE for N status diagnosis in garlic and presents initial evidence of varietal differences in N response, contributing to improving the understanding of management in this crop. Full article

Considering the growing demand for modern facility agriculture, it is essential to develop non-destructive technologies for assessing lettuce nutritional status. To overcome the limitations of traditional methods, which are destructive and time-consuming, this study proposes a multimodal non-destructive nitrogen detection method for lettuce based on multi-source imaging. The approach integrates terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI) to achieve rapid and non-invasive nitrogen detection. Spectral imaging data of lettuce samples under different nitrogen gradients (20–150%) were simultaneously acquired using a THz-TDS system (0.2–1.2 THz) and a NIR-HSI system (1000–1600 nm), with image segmentation applied to remove background interference. During data processing, Savitzky–Golay smoothing, MSC (for THz data), and SNV (for NIR data) were employed for combined preprocessing, and sample partitioning was performed using the SPXY algorithm. Subsequently, SCARS/iPLS/IRIV algorithms were applied for THz feature selection, while RF/SPA/ICO methods were used for NIR feature screening, followed by nitrogen content prediction modeling with LS-SVM and KELM. Furthermore, small-sample learning was utilized to fuse crop feature information from the two modalities, providing a more comprehensive and effective detection strategy. The results demonstrated that the THz-based model with SCARS-selected power spectrum features and an RBF-kernel LS-SVM achieved the best predictive performance (R² = 0.96, RMSE = 0.20), while the NIR-based model with ICO features and an RBF-kernel LS-SVM achieved the highest accuracy (R² = 0.967, RMSE = 0.193). The fusion model, combining SCARS and ICO features, exhibited the best overall performance, with training accuracy of 96.25% and prediction accuracy of 95.94%. This dual-spectral technique leverages the complementary responses of nitrogen in molecular vibrations (THz) and organic chemical bonds (NIR), significantly enhancing model performance. To the best of our knowledge, this is the first study to realize the synergistic application of THz and NIR spectroscopy in nitrogen detection of facility-grown lettuce, providing a high-precision, non-destructive solution for rapid crop nutrition diagnosis. Full article

Nitrogen fertilization is decisive for maize productivity, fertilizer use efficiency, and sustainability, which calls for fast and nondestructive nutritional diagnosis. This study evaluated the classification of maize plant nutritional status from red, green, and blue (RGB) leaf images using texture attributes. A greenhouse experiment was conducted under a completely randomized factorial design with four nitrogen doses, one maize hybrid Pioneer 30F35, and four replicates, at two sampling times corresponding to distinct phenological stages, totaling thirty-two experimental units. Images were processed with the gray-level cooccurrence matrix computed at three distances 1, 3, and 5 pixels and four orientations 0°, 45°, 90°, and 135°, yielding eight texture descriptors that served as inputs to five supervised classifiers: an artificial neural network, a support vector machine, k nearest neighbors, a decision tree, and Naive Bayes. The results indicated that texture descriptors discriminated nitrogen doses with good performance and moderate computational cost, and that homogeneity, dissimilarity, and contrast were the most informative attributes. The artificial neural network showed the most stable performance at both stages, followed by the support vector machine and k nearest neighbors, whereas the decision tree and Naive Bayes were less suitable. Confusion matrices and receiver operating characteristic curves indicated greater separability for omission and excess classes, with D1 standing out, and the patterns were consistent with the chemical analysis. Future work should include field validation, multiple seasons and genotypes, integration with spectral indices and multisensor data, application of model explainability techniques, and assessment of latency and scalability in operational scenarios. Full article

Stable crop yields require an appropriate supply of essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) based on the accurate diagnosis of soil nutrient status. Traditional laboratory analysis of soil nutrients is often complicated and time-consuming and does not provide real-time nutrient status. Visible–near-infrared (Vis-NIR) spectroscopy has emerged as a non-destructive and rapid method for estimating soil nutrient levels. Vis-NIR spectra reflect sample characteristics as the peak intensities; however, they are often affected by various artifacts and complex variables. Since Vis-NIR spectroscopy does not directly measure nutrient levels in soil, improving estimation accuracy is essential. For spectral preprocessing, the most important aspect is to develop an appropriate preprocessing strategy based on the characteristics of the data and identify artifacts such as noise, baseline drift, and scatter in the spectral data. Machine learning-based modeling techniques such as partial least-squares regression (PLSR) and support vector machine regression (SVMR) enhance estimation accuracy by capturing complex patterns of spectral data. Therefore, this review focuses on the use of Vis-NIR spectroscopy for evaluating soil properties including soil water content, organic carbon (C), and nutrients and explores its potential for real-time field application through spectral preprocessing and machine learning algorithms. Vis-NIR spectroscopy combined with machine learning is expected to enable more efficient and site-specific nutrient management, thereby contributing to sustainable agricultural practices. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

Efficient nitrogen management is key to maximizing production and minimizing the environmental impact of horticultural crops. This study analyses the effect of different doses of nitrogen on the development of broccoli (Brassica oleracea var. italica) (cultivar Parthenon), processing tomato (Solanum lycopersicum) (cultivar H1015), and processing pepper (Capsicum annuum) (cultivar Ramonete Lamuyo) in open fields in Extremadura and evaluates rapid and efficient methods for diagnosing their nutritional status. Trials were carried out at the La Orden Experimental Farm (CICYTEX) with different nitrogen fertilization rates. The N doses were 0–60–120–180 kg N/ha for peppers in 2020 and 2021 and 0–200–300 kg N/ha for 2022. For broccoli, the N doses were 0–100–200–300 kg N/ha in 2020 and 0–200–300 kg N/ha for 2022. For tomatoes, the N doses were 0–100–200–300 kg N/ha in 2021 and 0–200–350 kg N/ha for 2022. The following three indicators were compared: chlorophyll content measured with optical sensors, petiole sap nitrate concentration, and the nitrogen nutrition index (NNI). The results indicate that chlorophyll measurement is not suitable for broccoli due to the characteristics of its leaves, but is useful for tomatoes and peppers, providing a quick and non-destructive diagnosis. Nitrate concentration in sap, although more laborious and destructive, was found to be effective in discriminating nutritional status in the three species. However, the NNI did not prove to be a good reference method in open field conditions. These results highlight the importance of adapting nutrient monitoring strategies to the crop and management conditions, contributing to a more efficient use of nitrogen and a reduction in the environmental impact of nitrate leaching. Full article

Accurate diagnosis of crop nutritional status is critical for optimizing yield and quality in modern agriculture. This study enhances the accuracy of Raman spectroscopy-based nutrient diagnosis, improving its application in precision agriculture. We propose a method to identify optimal diagnostic positions on cucumber leaves for early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, thereby providing a robust scientific basis for high-throughput phenotyping using Raman spectroscopy (RS). Using a dot-matrix approach, we collected RS data across different leaf positions and explored the selection of diagnostic positions through spectral cosine similarity analysis. These results provide critical insights for developing rapid, non-destructive methods for nutrient stress monitoring in crops. Results show that spectral similarity across positions exhibits higher instability during the early developmental stages of leaves or under short-term (24 h) nutrient stress, with significant differences in the stability of spectral data among treatment groups. However, visual analysis of the spatial distribution of positions with lower similarity values reveals consistent spectral similarity distribution patterns across different treatment groups, with the lower similarity values predominantly observed at the leaf margins, near the main veins, and at the leaf base. Excluding low-similarity data significantly improved model performance for early (24 h) nutrient deficiency diagnosis, resulting in higher precision, recall, and F1 scores. Based on these results, the efficacy of the proposed method for selecting diagnostic positions has been validated. It is recommended to avoid collecting RS data from areas near the leaf margins, main veins, and the leaf base when diagnosing early nutrient deficiencies in plants to enhance diagnostic accuracy. Full article

The accurate assessment of nitrogen (N) status is important for N management and yield improvement. The N status in plants is affected by plant densities and N application rates, while the methods for assessing the N status in drip-irrigated cotton under reduced nitrogen treatment and different plant densities are lacking. Therefore, this study was conducted with four different N treatments (195.5, 299, 402.5, and 506 kg N ha⁻¹) and three sowing densities (6.9 × 10⁴, 13.8 × 10⁴, and 24 × 10⁴ plants ha⁻¹) by using a low-cost Unmanned Aerial Vehicle (UAV) system to acquire RGB imagery at a 10 m flight altitude at cotton main growth stages. We evaluated the performance of different ground resolutions (1.3, 2.6, 5.2, 10.4, 20.8, 41.6, 83.2, and 166.4 cm) for image textures, vegetation indices (VIs), and their combination for leaf N concentration (LNC) estimation using four regression methods (stepwise multiple linear regression, SMLR; support vector regression, SVR; extreme learning machine, ELM; random forest, RF). The results showed that combining VIs (ExGR, GRVI, GBRI, GRRI, MGRVI, RGBVI) and textures (VAR, HOM, CON, DIS) yielded higher estimation accuracy than using either alone. Specifically, the RF regression models had a higher accuracy and stability than SMLR and the other two machine learning algorithms. The best accuracy (R² = 0.87, RMSE = 3.14 g kg⁻¹, rRMSE = 7.00%) was obtained when RF was applied in combination with VIs and texture. Thus, the combination of VIs and textures from UAV images using RF could improve the estimation accuracy of drip-irrigated cotton LNC and may have a potential contribution in the rapid and non-destructive nutrition monitoring and diagnosis of other crops or other growth parameters. Full article

The nutritional diagnosis of crops is carried out through costly elemental analyses of different plant organs, particularly leaves, in the laboratory. However, visible and near-infrared (Vis-NIR) spectroscopy of unprocessed plant samples has a high potential as a faster, non-destructive, environmental-friendly alternative to elemental analyses. In this work, the potential of this technique to estimate the concentrations of macronutrients such as nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg), and micronutrients such as iron (Fe), manganese (Mn) and boron (B), in persimmon (Diospyros kaki L.) ‘Rojo Brillante’ leaves, has been investigated. Throughout the crop cycle variable rates of N and K were applied to obtain six nutritional status levels in persimmon trees in an experimental orchard. Then, leaves were systematically sampled throughout the cropping season from the different nutritional levels and spectral reflectance measurements were acquired in the 430–1040 nm wavelength range. The concentrations of nutrients were determined by inductively coupled plasma optical emission spectrometry (ICP-OES) for P, K, Ca, Mg, Fe, Mn and B after microwave digestion, while the Kjeldahl method was used for N. Then, partial least squares regression (PLS-R) was used to model the concentrations of these nutrients from the reflectance measurements of the leaves. The model was calibrated using 75% of the samples while the remaining 25% were left as the independent test set for external validation. The results of the test set indicated an acceptable validation for most of the nutrients, with determination coefficients (R²) of 0.74 for N and P, 0.54 for K, 0.77 for Ca, 0.60 for Mg, 0.39 for Fe, 0.69 for Mn and 0.83 for B. These findings support the potential use of Vis-NIR spectrometric techniques as an alternative to conventional laboratory methods for the persimmon nutritional status diagnosis although more research is needed to know how the models developed one year perform in ensuing years. Full article

Plant nitrogen concentration (PNC) is a traditional standard index to measure the nitrogen nutritional status of winter wheat. Rapid and accurate diagnosis of PNC performs an important role in mastering the growth status of winter wheat and guiding field precision fertilization. In this study, the in situ hyperspectral reflectance data were measured by handheld SVC HR−1024I (SVC) passive field spectroradiometer and PNC were determined by the modified Kjeldahl digestion method. Continuous wavelet transform (CWT), successive projection algorithm (SPA) and partial least square (PLS) regression were combined to construct an efficient method for estimating winter wheat PNC. The main objectives of this study were to (1) use CWT to extract various wavelet coefficients under different decomposition scales, (2) use SPA to screen sensitive wavelet coefficients as independent variables and combine with PLS regression to establish winter wheat PNC estimation models, respectively, and (3) compare the precision of PLS regression models to find a reliable model for estimating winter wheat PNC during the growing season. The results of this paper showed that properly increasing the decomposition scale of CWT could weaken the impact of high-frequency noise on the prediction model. The number of wavelet coefficients has been significantly reduced after screened by SPA. The PNC estimation model (CWT–Scale6–SPA–PLS) based on the wavelet coefficients of the sixth decomposition scale most accurately predicted the PNC (the determination coefficient of the calibration set (R_c²) was 0.85. Root mean square error of the calibration set (RMSE_c) was 0.27. The determination coefficient of the validation set (R_v²) was 0.84. Root mean square error of the validation set (RMSE_v) was 0.28 and relative prediction deviation (RPD) was 2.47). CWT-Scale6-SPA-PLS can be used to predict PNC. The optimal winter wheat PNC prediction model based on CWT proposed in this study is a reliable method for rapid and nondestructive monitoring of PNC and provides a new technical method for precision nitrogen management. Full article

Global climate change and the spread of COVID-19 have caused widespread concerns about food security. The development of smart agriculture could contribute to food security; moreover, the targeted and accurate management of crop nitrogen is a topic of concern in the field of smart agriculture. Unmanned aerial vehicle (UAV) spectroscopy has demonstrated versatility in the rapid and non-destructive estimation of nitrogen in summer maize. Previous studies focused on the entire growth season or early stages of summer maize; however, systematic studies on the diagnosis of nitrogen that consider the entire life cycle are few. This study aimed to: (1) construct a practical diagnostic model of the nitrogen life cycle of summer maize based on ground hyperspectral data and UAV multispectral sensor data and (2) evaluate this model and express a change in the trend of nitrogen nutrient status at a spatiotemporal scale. Here, a comprehensive data set consisting of a time series of crop biomass, nitrogen concentration, hyperspectral reflectance, and UAV multispectral reflectance from field experiments conducted during the growing seasons of 2017–2019 with summer maize cultivars grown under five different nitrogen fertilization levels in Beijing, China, were considered. The results demonstrated that the entire life cycle of summer maize was divided into four stages, viz., V6 (mean leaf area index (LAI) = 0.67), V10 (mean LAI = 1.94), V12 (mean LAI = 3.61), and VT-R6 (mean LAI = 3.94), respectively; moreover, the multi-index synergy model demonstrated high accuracy and good stability. The best spectral indexes of these four stages were GBNDVI, TCARI, NRI, and MSAVI2, respectively. The thresholds of the spectral index of nitrogen sufficiency in the V6, V10, V12, VT, R1, R2, and R3–R6 stages were 0.83–0.44, −0.22 to −5.23, 0.42–0.35, 0.69–0.87, 0.60–0.75, 0.49–0.61, and 0.42–0.53, respectively. The simulated nitrogen concentration at the various growth stages of summer maize was consistent with the actual spatial distribution. Full article