Estimation Of Nitrogen Content Research Articles

Spatial-spectral feature mining in hyperspectral corn leaf venation structure and its application in nitrogen content estimation

The growth of corn plants requires a significant amount of nitrogen nutrient supply to ensure the yield. Meanwhile, overfertilization could result in adverse environmental impacts, making it crucial to monitor the nitrogen supply condition in corn plants precisely. Hyperspectral imaging (HSI) could be the solution to this challenge, as HSI plant phenotyping technology has been widely utilized to nondestructively identify plant traits, such as mineral nutrient deficiencies in corn plants. However, limitations persist where the conventional HSI nitrogen prediction models mainly relied on the overall colors of the plant tissue extracted from the spectral domain but rarely included the spatially distributed information on the leaf level. This study aimed to examine if including new information from the spatial domain could potentially improve the performance of HSI models. Because the venation structure plays a crucial role in the distribution of various nutrients across the leaf, a series of physiological responses caused by nitrogen deficiency could result in non-uniform color patterns related to the veins. The recent advancement of HSI techniques such as LeafSpec, whose imaging quality and spatial-spectral resolution have been significantly improved, made it possible to extract the venation structure together with high-resolution spatial-spectral features. In this study, the hypothesis was that utilizing high-resolution spatial-spectral features extracted based on the venation structure could yield a better-performed nitrogen content prediction model compared to the averaged spectrum of the corn leaf. First, a venation structure segmentation algorithm was developed using the Local Binary Patterns (LBP) method for hyperspectral corn leaf images scanned with LeafSpec. Second, for every hyperspectral leaf image, 131 spectral index heatmaps were generated, and 30 leaf regions were picked based on the venation structure, resulting in 3930 spatial-spectral features. Third, the features were picked using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. As the results tested with a field assay involving two different corn genotypes and two nitrogen treatments, the Partial Least Square Regression (PLS-R) model built with selected spatial-spectral features outperformed the model built with the averaged leaf spectra in terms of the Root Mean Squared Error (RMSE) of predictions. The results provided encouraging proof and guidance for the potential benefit of using high-resolution spatial-spectral features derived from hyperspectral leaf images to improve the accuracy and robustness of the nitrogen content prediction models for corn plants.

Remote estimation of leaf nitrogen content, leaf area, and berry yield in wild blueberries

Nitrogen (N) fertilization is a major management requirement for wild blueberry fields. Its presence and estimation can be difficult given the perennial and heterogeneous nature of the plant, low N requirement, and residual N effects, resulting in the frequent over-application of N, excessive canopy growth, and resulting reduction in berry yields. Therefore, this study aimed to estimate nitrogen content and growth parameters using remote sensing approaches. Three trials were established in three commercial fields in Nova Scotia, Canada. An RCBD with 5 replicates and a plot size of 6 × 8 m with a 2 m buffer was used. Treatments consisted of 0, 20, 40, 60, and 100 kg N ha-1 of fertilizer. Using a DJI Matrice 300 UAV mounted with an RGB and a multispectral camera, aerial measurements were collected at 30 m altitude. Several field measurements including leaf nitrogen content (LNC), leaf area, floral bud numbers, stem height, and yield were conducted. Several vegetation indices (VIs) were computed for each plot, and correlation and regression analyses were conducted. Results indicated that treatments with high nitrogen rates had correspondingly high LAI measurements with the 60 kg ha-1 rate achieving the best growth parameters compared to the other treatments. LNC, LAI, and berry yield estimations using VIs [green leaf index (GLI), green red vegetation index (GRVI), and visible atmospheric red index (VARI)] produced significantly positive R2 values of 0.43, 0.48, and 0.30 respectively. Results from this study illustrated the potential of using VIs to estimate LNC, LAI, and berry yield parameters. It was established that the near-infrared VIs are the most effective in estimating differences in nitrogen rates, making them suitable for use in prescription maps for N fertilization applications.

Effects of Variety and Growth Stage on UAV Multispectral Estimation of Plant Nitrogen Content of Winter Wheat

The accurate estimation of nitrogen content in crop plants is the basis of precise nitrogen fertilizer management. Unmanned aerial vehicle (UAV) imaging technology has been widely used to rapidly estimate the nitrogen in crop plants, but the accuracy will still be affected by the variety, the growth stage, and other factors. We aimed to (1) analyze the correlation between the plant nitrogen content of winter wheat and spectral, texture, and structural information; (2) compare the accuracy of nitrogen estimation at single versus multiple growth stages; (3) assess the consistency of UAV multispectral images in estimating nitrogen content across different wheat varieties; (4) identify the best model for estimating plant nitrogen content (PNC) by comparing five machine learning algorithms. The results indicated that for the estimation of PNC across all varieties and growth stages, the random forest regression (RFR) model performed best among the five models, obtaining R2, RMSE, MAE, and MAPE values of 0.90, 0.10%, 0.08, and 0.06%, respectively. Additionally, the RFR estimation model achieved commendable accuracy in estimating PNC in three different varieties, with R2 values of 0.91, 0.93, and 0.72. For the dataset of the single growth stage, Gaussian process regression (GPR) performed best among the five regression models, with R2 values ranging from 0.66 to 0.81. Due to the varying nitrogen sensitivities, the accuracy of UAV multispectral nitrogen estimation was also different among the three varieties. Among the three varieties, the estimation accuracy of SL02-1 PNC was the worst. This study is helpful for the rapid diagnosis of crop nitrogen nutrition through UAV multispectral imaging technology.

Self-correcting deep learning for estimating rice leaf nitrogen concentration with mobile phone images

As a pivotal role in crop development, the application of nitrogen fertilizers enhances yields, while excessive use poses environmental risks. Precision nitrogen management requires rapid crop nitrogen assessment in field conditions and mobile phone images can be a promising approach. However, few studies have investigated how arbitrary white balance and exposure value settings affect crop nitrogen diagnosis using phone photos. Therefore, a new deep learning framework: self-correcting leaf nitrogen concentration estimation network (SCLNC-Net), was proposed to explore the potential of mobile phone images for non-destructive nitrogen status monitoring. Imperfect camera metering and incorrect user operations were addressed through attention U-Net for automatic white balance and exposure correction. Then a lightweight deep learning algorithm MobileNetV3 was utilized to estimate rice leaf nitrogen concentration based on the self-corrected images. Compared with most deep learning models, SCLNC-Net has fewer parameters and higher computational efficiency, which is better suited for deployment on mobile platforms. The results revealed that SCLNC-Net enhanced image quality and acquired high accuracy in the estimation of rice leaf nitrogen concentration (R2 = 0.84, RMSE = 0.39 %, RRMSE = 13 %). Moreover, the robustness and effectiveness of the proposed SCLNC-Net remained excellent across various shooting angles, mobile phone types, and rice varieties. This study provides insights into rapid and cost-efficient nutrient assessment and precision nitrogen management.

Convolutional Neural Network-Based Estimation of Nitrogen Content in Regenerating Rice Leaves

Regenerated rice, characterized by single planting and double harvesting, saves labor and costs, significantly contributing to global food security. Hyperspectral imaging technology, which integrates image and spectral data, provides comprehensive, non-destructive, and pollution-free vegetation canopy analysis, making it highly effective for crop nutrient diagnosis. In this study, we selected two varieties of regenerated rice for field trials. Hyperspectral images were captured during key growth stages (flush, grouting, and ripening) of both the first and regenerated seasons. Utilizing a two-dimensional convolutional neural network (2D-CNN) as a deep feature extractor and a fully connected layer for nitrogen content prediction, we developed a robust model suitable for estimating nitrogen content in regenerated rice. The experimental results demonstrate that our method achieves a mean squared error (MSE) of 0.0008, significantly outperforming the back-propagation (BP) network and multiple linear regression by reducing the MSE by 0.0151 and 0.0247, respectively. It also surpasses the one-dimensional convolutional neural network (1D-CNN) by 0.003. This approach ensures accurate nitrogen content prediction throughout the growth cycle of regenerated rice, aiding in yield and economic benefit enhancement.

Impact of Raoiella indica Hirst (Acari: Tenuipalpidae) Infestation on Nitrogen Concentration of Areca catechu L.

Different tenuipalpid phytophagous mites might infest the foliage and nuts of the areca palm and cause significant damage. Raoiella indica (Hirst), has been known as a serious pest of areca palm. In the present study, concentration has been fixed on exploring the feeding impact of R. indica through the quantitative estimation of nitrogen concentration of mite-infested and uninfested leaves by ‘Kjeldahl method (1883)’. Results of the study have exposed that the feeding activity of the mite on the leaves of A. catechu has induced severe reduction in the levels of ‘total nitrogen’. The percentage loss of ‘total nitrogen’ was estimated to be 37- 62% in RPM-infested Areca leaves. Loss of nitrogen content as evidenced during the present study disclosed the potential of R. indica on A. catechu.

Temporal evolution analysis of spectral characteristics in male ginkgo inflorescences and estimation of nitrogen content

Temporal evolution analysis of spectral characteristics in male ginkgo inflorescences and estimation of nitrogen content

An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods

Accurate nitrogen fertilizer management determines the yield and quality of fruit trees, but there is a lack of multispectral UAV-based nitrogen fertilizer monitoring technology for orchards. Therefore, in this study, a field experiment was conducted by UAV to acquire multispectral images of an apple orchard with dwarf stocks and dense planting in southern Xinjiang and to estimate the nitrogen content of canopy leaves of apple trees by using three machine learning methods. The three inversion methods were partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RFR). The results showed that the RF model could significantly improve the accuracy of estimating the leaf nitrogen content of the apple tree canopy, and the validation set of the four periods of apple trees ranged from 0.670 to 0.797 for R2, 0.838 mg L−1 to 4.403 mg L−1 for RMSE, and 1.74 to 2.222 for RPD, among which the RF model of the pre-fruit expansion stage of the 2023 season had the highest accuracy. This paper shows that the apple tree leaf nitrogen content estimation model based on multispectral UAV images constructed by using the RF machine learning method can timely and accurately diagnose the growth condition of apple trees, provide technical support for precise nitrogen fertilizer management in orchards, and provide a certain scientific basis for tree crop growth.

Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model

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.

Estimation of Apple Leaf Nitrogen Concentration Using Hyperspectral Imaging-Based Wavelength Selection and Machine Learning

In apple cultivation, the total nitrogen content is an important indicator of plant growth, fruit quality, and yield. Timely monitoring of growth becomes imperative, since an imbalance, either in deficiency or excess nitrogen, can result in physiological disorders, adversely impacting both the quantity and quality of fruit. Leaf nitrogen content can be determined using simple chlorophyll meters or destructive testing; however, these methods are time-consuming. However, by employing spectral imaging technology, it is possible to swiftly predict leaf nitrogen content. This study estimated the total nitrogen content in apple trees via hyperspectral imaging and machine learning-based regression analysis (partial least-squares regression (PLSR), support vector regression (SVR), and eXtreme gradient boosting regression (XGBoost). Additionally, to reduce computational costs and improve reproducibility, spectral binning was divided into three stages (4, 8, and 16 bins), and models were compared with a 2-binning estimation model. The analysis focused on green, red, red edge, and near-infrared (NIR) spectra, with 5–10 selected wavelengths, and the SVR-based prediction model showed a similar or greater performance to that of the full spectrum. At 4- and 8-binning, the selected wavelengths were similar to those at 2-binning, maintaining similar prediction model performance. However, at 16 bp, the performance of the prediction model decreased owing to spectral data loss, leading to a significant reduction in wavelengths for nitrogen content estimation. These results can support informed nitrogen fertilization decisions, enabling precise, real-time monitoring of nitrogen content for enhanced plant growth, fruit quality, and yield in apple trees. Additionally, the selected wavelengths can be considered in the development of new types of multispectral sensors.

Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and leaves

Context or problemNitrogen is one of the important elements of crops, which plays a decisive role in crop growth and development and the formation of yields. Monitoring of rice organ-scale nitrogen concentration based on the unmanned aerial vehicle (UAV) images is of great significance for rice field management and yield prediction. Objective or research questionPrevious studies have focused on the use of traditional statistical methods and chlorophyll-related vegetation indices to construct plant nitrogen concentration, with models lacking generalizability. MethodsIn this study, rice field trials of two varieties (NJ9108, YD6) and nitrogen fertilizer treatments (N0-N3: 0, 105, 210 and 315 kg/ha) were conducted for 3 years with manual sampling and UAV digital and hyperspectral images during key fertility periods. Based on the data of the whole growth periods and combined with vegetation indices (VIs), color indices (CIs), hyperspectral parameters (HPs), texture indices (TIs) and machine-learning algorithms, monitoring models of nitrogen concentration at the organ scale of rice were constructed and used to estimate the N content of multiple organs (leaf and stem) of rice at different periods. Field experiments were used to collect the multi-organ nitrogen concentration of rice and the remote sensing (RS) data of UAV during the critical growth period of the two years (2021, 2022), and machine-learning algorithms were used to construct the estimation models. ResultsThe results showed that VIs had good correlations with leaf nitrogen concentration (LNC), stem nitrogen concentration (SNC) and plant nitrogen concentration (PNC), with correlation coefficients (r) of 0.86, 0.74 and 0.81, respectively. Machine learning estimation models combining multiple types of RS indices were more accurate than single parameter models constructed by traditional statistical methods, with the LNC optimal model (R2 = 0.8, RMSE = 3.83 mg/g), the SNC optimal model (R2 = 0.7, RMSE = 2.43 mg/g) and the PNC optimal model (R2 = 0.7, RMSE = 3.19 mg/g). Validated using data from 2020, the machine-learning models were far more accurate than traditional methods. ConclusionsThese results show that the use of multi-source remote sensing data based on machine-learning algorithms can effectively estimate the nitrogen concentration of organs in rice. ImplicationsThis study provides an accurate, stable and universal method for estimating rice nitrogen concentration in rice organs, which can be used as a reference for estimating rice nitrogen concentration in large fields using UAV RS technology.

UAV-borne hyperspectral estimation of nitrogen content in tobacco leaves based on ensemble learning methods

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 (R2), 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 (R2=0.680, RMSE=5.402 mg/g, 19.72%). Specifically, the stacking-based models achieved the highest accuracy as well as relatively high stability (R2=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.

PEMANFAATAN NORMALIZED GREEN RED DIFFERENCE INDEX (NGRDI) UNTUK MENDUGA KADAR N-TOTAL PADA LAHAN PADI DI KABUPATEN PAMEKASAN

Inefficient fertilization is the cause of decreased rice production. This is due to the inappropriate use of fertilizers. The absence of information on soil nitrogen content in their area causes farmers not to apply fertilizer properly. Analysis of soil nitrogen content in Pamekasan Regency requires much time, cost, and effort. Remote sensing methods can be used to obtain information on soil nitrogen levels by reducing the number of samples used. This study aims to estimate nitrogen content in rice fields using the vegetation index in the form of NGRDI (Normalized Green Red Difference Index). This research was conducted by purposive random sampling method based on 40 land map units formed in Pamekasan Regency, laboratory analysis, statistical analysis, and making distribution maps. The results showed that there was a significant relationship between the NGRDI pixel value and the nitrogen content, with a correlation value of -0.42. The regression equation results obtained from the NGRDI value with nitrogen content are y = -0.269x + 0.139. The accuracy test was carried out by the RMSE method, and the result was 0.027. This shows that the NGRDI index can estimate soil nitrogen content in Pamekasan Regency

Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images

Nitrogen is one of the most important nutrients affecting the growth and fruit quality of walnut trees. Rapid and accurate monitoring of nitrogen concentration in the walnut canopy can provide a digital basis for its nutritional diagnosis and precision fertilization. Consequently, the main purpose of this study was to use Unmanned Aerial Vehicle (UAV) remote sensing technology to monitor the nitrogen concentration of walnut canopies. In this study, UAV multispectral images of the canopies of nine walnut orchards with different management levels in Wensu County, South Xinjiang, China, were collected during the fast-growing (20 May), sclerotization (25 June), and near-maturity (27 August) periods of walnut fruit, and canopy nitrogen concentration data for 180 individual plants were collected during the same periods. The validity of the information extracted via the outline canopy and simulated canopy methods was compared. The accuracy of nitrogen concentration inversion for three modeling methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF), was analyzed; the effects of different combinations of variables on model accuracy were compared; and the spatial distribution of the nitrogen concentration in the walnut canopy was numerically mapped using the optimal model. The results showed that the accuracy of the model created using the single plant information extracted from the outlined canopy was better than that of the simulated canopy method, but the simulated canopy method was more efficient in extracting effective information from the single plant canopy than the outlined canopy. The simulated canopy method overcame the difficulty of mismatching the spectral information of individual plants extracted, by outlining the canopy in the original image for nitrogen distribution mapping with the spectral information of image elements in the original resolution image. The prediction accuracy of the RF model was better than that of the SVM and PLSR models; the prediction accuracy of the model using a combination of waveband texture information and vegetation index texture information was better than that of the single-source model. The coefficients of determination (R2) values of the RF prediction model built using the band texture information extracted via the simulated canopy method with the vegetation index texture information were in the range of 0.61–0.84, the root mean square error (RMSE) values were in the range of 0.27–0.43 g kg−1, and the relative analysis error (RPD) values were in the range of 1.58–2.20. This study shows that it is feasible to monitor the nitrogen concentration of walnut tree canopies using UAV multispectral remote sensing. This study provides a theoretical basis and methodological reference for the rapid monitoring of nutrients in fruit trees in southern Xinjiang.

Hyperspectral Estimation of Nitrogen Content in Wheat Based on Fractional Difference and Continuous Wavelet Transform

Nitrogen content is a crucial index for crop growth diagnosis and the exact estimation of nitrogen content is of great significance for grasping crop growth status in real-time. This paper takes winter wheat as the study object and the precision agriculture demonstration area of the Jiaozuo Academy of Agricultural and Forestry Sciences in Henan Province as the research area. The hyperspectral reflectance data of the wheat canopy in different growth periods are obtained with the ASD ground object hyperspectral instrument, and the original canopy spectral data are preprocessed by fractional differential and continuous wavelet transform; then, the vegetation indices are established, correlation analysis with nitrogen content is conducted, and the fractional differential spectra are selected; finally, based on the wavelet energy coefficient and the vegetation indices with strong correlations, the methods of support vector machine (SVM), ridge regression, stepwise regression, Gaussian process regression (GPR), and the BP neural network are used to construct the estimation model for nitrogen content in wheat at different growth stages. By adopting the R2 and root mean square error (RMSE) indices, the best nitrogen content estimation model at every growth stage is selected. The overall analysis of the nitrogen content estimation effect indicated that for the four growth periods, the maximum modeling and validation R2 of the nitrogen content estimation models of the SVM, ridge regression, stepwise regression, GPR, and BP neural network models reached 0.95 and 0.93, the average reached 0.76 and 0.71, and the overall estimation effect was good. The average values of the modeling and validation R2 of the nitrogen content estimation model at the flag picking stage were 0.85 and 0.81, respectively, which were 37.10% and 44.64%, 1.19% and 3.85%, and 14.86% and 17.39% higher than those at the jointing stage, flowering stage, and filling stage, respectively. Therefore, the model of the flag picking stage has higher estimation accuracy and a better estimation effect on the nitrogen content. For the different growth stages, the optimal estimation models of nitrogen content were different. Among them, continuous wavelet transform combined with the BP neural network model can be the most effective method for estimating the N content in wheat at the flagging stage. The paper provides an effective method for estimating the nitrogen content in wheat and a new idea for crop growth monitoring.

Study on the nitrogen content estimation model of cotton leaves based on "image-spectrum-fluorescence" data fusion.

Precise monitoring of cotton leaves' nitrogen content is important for increasing yield and reducing fertilizer application. Spectra and images are used to monitor crop nitrogen information. However, the information expressed using nitrogen monitoring based on a single data source is limited and cannot consider the expression of various phenotypic and physiological parameters simultaneously, which can affect the accuracy of inversion. Introducing a multi-source data-fusion mechanism can improve the accuracy and stability of cotton nitrogen content monitoring from the perspective of information complementarity. Five nitrogen treatments were applied to the test crop, Xinluzao No. 53 cotton, grown indoors. Cotton leaf hyperspectral, chlorophyll fluorescence, and digital image data were collected and screened. A multilevel data-fusion model combining multiple machine learning and stacking integration learning was built from three dimensions: feature-level fusion, decision-level fusion, and hybrid fusion. The determination coefficients (R2) of the feature-level fusion, decision-level fusion, and hybrid-fusion models were 0.752, 0.771, and 0.848, and the root-mean-square errors (RMSE) were 3.806, 3.558, and 2.898, respectively. Compared with the nitrogen estimation models of the three single data sources, R2 increased by 5.0%, 6.8%, and 14.6%, and the RMSE decreased by 3.2%, 9.5%, and 26.3%, respectively. The multilevel fusion model can improve accuracy to varying degrees, and the accuracy and stability were highest with the hybrid-fusion model; these results provide theoretical and technical support for optimizing an accurate method of monitoring cotton leaf nitrogen content.

Soil Total Nitrogen Content Estimation Based on the Differential Evolutionary Algorithm for Secondary Feature Extraction with Zy1-02d Hyperspectral Satellite Imagery

Soil Total Nitrogen Content Estimation Based on the Differential Evolutionary Algorithm for Secondary Feature Extraction with Zy1-02d Hyperspectral Satellite Imagery

Determination of nitrogen content in Ulva fenestrata by color image analysis – a rapid and cost-efficient method to estimate nitrogen content in seaweeds

There is an increasing interest in the cultivation of seaweeds for food and feed, and the seaweed aquaculture industry is rapidly developing. The nutritional status of the seaweeds is important to ensure a good quality crop. Cost-efficient and straightforward methods for farmers to analyze their crop are essential for the successful development of the industry. In this study, we developed non-destructive, labor- and cost-efficient models to estimate the nitrogen content in the crop seaweed Ulva fenestrata by color image analysis. We quantified tissue nitrogen content and thallus color in sea-farmed seaweed every week throughout a whole cultivation season (15 consecutive weeks) and analyzed data with linear regression models. We showed that color image analysis accurately estimated the nitrogen content in the seaweed (R2 = 0.944 and 0.827 for fresh tissue and dried powder, respectively), and through tenfold cross validation we showed that the developed models were robust and precise. Based on these models, we developed a web-based application that automatically analyzes the nitrogen content of U. fenestrata. Furthermore, we produced a color guide that can easily be brought to the farm for onsite crude estimation of the nitrogen content of U. fenestrata. Our results demonstrate that color can be a powerful tool for seaweed farmers (and researchers) to estimate seaweeds’ nutritional status. We anticipate that similar models can be developed for other commercially interesting seaweed species.

Estimation of nitrogen content in wheat using indices derived from RGB and thermal infrared imaging

The important period of wheat grain accumulation is from the flowering stage to the filling stage, and the nitrogen content of wheat in this period is of great significance to the yield accumulation. With the rapid development of sensor technology, different sensors have been increasingly used for crop nitrogen status estimation due to their flexibility. This study aimed to investigate the use of a combination of image information from two proximal sensors (RGB and thermal sensors) to assess the nitrogen status of wheat at the reproductive growth stage. Previous studies have focused on estimating leaf N status at the nutritional growth stage of wheat, and the precision of N estimation is not high at the later stages. Considering that the canopy was composed of leaves and spikes in the reproductive stage, we integrated leaf N content and spike N content as plant N content for assessment. A two-year field trial was conducted, and this study used a Sony camera to acquire RGB images from flowering to maturity and obtained thermal images using the handle thermal infrared camera during the same period. Then, these images were further processed to extract the color features (17), the texture features (5) and temperature values (2). Based on these 24 indices, this study used three machine learning algorithms (i.e., Back-Propagation neural network (BP), Random Forest (RF) and Support Vector Regression (SVR)), resulted in nine estimation models based on a single dataset (i.e., c-based BP, te-based BP, t-based BP, c-based RF, te-based RF, t-based RF, c-based SVR, te-based SVR, t-based SVR) and 12 models based on data fusions (i.e., c+te-based BP, c+t-based BP, te+t-based BP, c+te+t-based BP, c+te-based RF, c+t-based RF, te+t-based RF, c+te+t-based RF, c+te-based SVR, c+t-based SVR, te+t-based SVR, c+te+t-based SVR). The performance of the 21 models was evaluated and compared with each other according to the coefficient of determination (R 2 ), root mean square error (RMSE) and residual prediction deviation (RPD) in nitrogen content estimation. The results show that the best model was the c+te+t-based RF, which was a model based on the combination of color features, texture features and temperature values. It achieved high accuracy in estimating plant N content (R 2 = 0.89, RMSE = 3.23 mg g −1 , RPD = 1.90). In conclusion, the combination of information from RGB and thermal images has good potential for application in monitoring crop N content at late reproductive stages, and plant temperature values can be used as effective indicators for assessing crop growth and nitrogen nutrient status. • Wheat temperature and temperature difference have the potential to monitor wheat growth. • Multi-source data (color, texture, and temperature information) based on the source-sink level have a good potential for monitoring nitrogen nutrient status in the later stages of wheat. • The fusion of information from RGB and thermal images has promising applications for monitoring the nitrogen nutrient status of crops. • The machine-learning models incorporating multiple features significantly improved the accuracy of the models for estimating nitrogen content in wheat plants.

A hybrid model to predict nitrogen concentration in heterogeneous grassland using field spectroscopy

Field spectroscopy is a rapid and non-destructive tool used for the estimation of nitrogen concentration (N%) of vegetation. Empirical and physically-based models are widely used for retrieving N%. However, model transferability to different times and locations, and feature redundancy remain the two key challenges of field spectroscopy analysis. Here we addressed these problems by developing a hybrid method (i.e., a combination of physically-based (PROSAIL) and empirical models) to retrieve N% in grasslands. We used a large spectral dataset with >6000 samples collected over 8 years (2009–2016) for grassland farms across New Zealand. The hybrid model combines the features derived from PROSAIL inversion and an empirical model and develops a predictive model using a Gaussian Process Regression (GPR) algorithm. The model performance is tested on spatially and temporally independent data and compared with PROSAIL and empirical models. The hybrid model achieves a higher performance with an RMSE (%N), R2 and Mean Prediction Interval Width (MPIW) of 0.27, 0.78 and 0.26 as compared to empirical (RMSE = 0.28, R2 = 0.77 and MPIW = 0.32) and physically-based models (RMSE = 0.33, R2 = 0.65 and MPIW = 0.56). In addition, the hybrid model significantly outperforms the physically-based and empirical models during autumn (RMSE = 0.32, R2 = 0.78 and MPIW = 0.11) and summer (RMSE = 0.27, R2 = 0.80 and MPIW = 0.16) seasons.