Nitrogen Content Estimation of Apple Leaves Using Hyperspectral Analysis

Novel hyperspectral indices, which are the first derivative normalized difference nitrogen index (FD-NDNI) and the first derivative ratio nitrogen vegetation index (FD-SRNI), were developed to estimate the leaf nitrogen content (LNC) of wheat. The field stress experiments were conducted with different nitrogen and water application rates across the growing season of wheat and 190 measurements were collected on canopy spectra and LNC under various treatments. The inversion models were constructed based on the dataset to evaluate the ability of various spectral indices to estimate LNC. A comparative analysis showed that the model accuracies of FD-NDNI and FD-SRNI were higher than those of other commonly used hyperspectral indices including mNDVI705, mSR, and NDVI705, which was indicated by higher R2 and lower root mean square error (RMSE) values. The least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms were then used to optimize the models constructed by FD-NDNI and FD-SRNI. The p-R2 values of the FD-NDNI_RFR and FD-SRNI_RFR models reached 0.874 and 0.872, respectively, which were higher than those of the exponential and SVR model and indicated that the RFR model was accurate. Using the RFR inversion model, remote sensing mapping for the Operative Modular Imaging Spectrometer (OMIS) image was accomplished. The remote sensing mapping of the OMIS image yielded an accuracy of R2 = 0.721 and RMSE = 0.540 for FD-NDNI and R2 = 0.720 and RMSE = 0.495 for FD-SRNI, which indicates that the similarity between the inversion value and the measured value was high. The results show that the new hyperspectral indices, i.e., FD-NDNI and FD-SRNI, are the optimal hyperspectral indices for estimating LNC and that the RFR algorithm is the preferred modeling method.

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WAVELENGTH SELECTION OF HYPERSPECTRAL LIDAR BASED ON FEATURE WEIGHTING FOR ESTIMATION OF LEAF NITROGEN CONTENT IN RICE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b1-9-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 9-13

Author(s):

Lin Du ◽

Shuo Shi ◽

Wei Gong ◽

Jian Yang ◽

Jia Sun ◽

...

Keyword(s):

Nitrogen Content ◽

System Integration ◽

Wide Spectrum ◽

Green Plant ◽

Leaf Nitrogen ◽

Feature Weighting ◽

Support Vector ◽

Leaf Nitrogen Content ◽

Wavelength Selection ◽

Regression Methods

Hyperspectral LiDAR (HSL) is a novel tool in the field of active remote sensing, which has been widely used in many domains because of its advantageous ability of spectrum-gained. Especially in the precise monitoring of nitrogen in green plants, the HSL plays a dispensable role. The exiting HSL system used for nitrogen status monitoring has a multi-channel detector, which can improve the spectral resolution and receiving range, but maybe result in data redundancy, difficulty in system integration and high cost as well. Thus, it is necessary and urgent to pick out the nitrogen-sensitive feature wavelengths among the spectral range. The present study, aiming at solving this problem, assigns a feature weighting to each centre wavelength of HSL system by using matrix coefficient analysis and divergence threshold. The feature weighting is a criterion to amend the centre wavelength of the detector to accommodate different purpose, especially the estimation of leaf nitrogen content (LNC) in rice. By this way, the wavelengths high-correlated to the LNC can be ranked in a descending order, which are used to estimate rice LNC sequentially. In this paper, a HSL system which works based on a wide spectrum emission and a 32-channel detector is conducted to collect the reflectance spectra of rice leaf. These spectra collected by HSL cover a range of 538 nm – 910 nm with a resolution of 12 nm. These 32 wavelengths are strong absorbed by chlorophyll in green plant among this range. The relationship between the rice LNC and reflectance-based spectra is modeled using partial least squares (PLS) and support vector machines (SVMs) based on calibration and validation datasets respectively. The results indicate that I) wavelength selection method of HSL based on feature weighting is effective to choose the nitrogen-sensitive wavelengths, which can also be co-adapted with the hardware of HSL system friendly. II) The chosen wavelength has a high correlation with rice LNC which can be retrieved by using PLS and SVMs regression methods.

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WAVELENGTH SELECTION OF HYPERSPECTRAL LIDAR BASED ON FEATURE WEIGHTING FOR ESTIMATION OF LEAF NITROGEN CONTENT IN RICE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b1-9-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 9-13

Author(s):

Lin Du ◽

Shuo Shi ◽

Wei Gong ◽

Jian Yang ◽

Jia Sun ◽

...

Keyword(s):

Nitrogen Content ◽

System Integration ◽

Wide Spectrum ◽

Green Plant ◽

Leaf Nitrogen ◽

Feature Weighting ◽

Support Vector ◽

Leaf Nitrogen Content ◽

Wavelength Selection ◽

Regression Methods

Hyperspectral LiDAR (HSL) is a novel tool in the field of active remote sensing, which has been widely used in many domains because of its advantageous ability of spectrum-gained. Especially in the precise monitoring of nitrogen in green plants, the HSL plays a dispensable role. The exiting HSL system used for nitrogen status monitoring has a multi-channel detector, which can improve the spectral resolution and receiving range, but maybe result in data redundancy, difficulty in system integration and high cost as well. Thus, it is necessary and urgent to pick out the nitrogen-sensitive feature wavelengths among the spectral range. The present study, aiming at solving this problem, assigns a feature weighting to each centre wavelength of HSL system by using matrix coefficient analysis and divergence threshold. The feature weighting is a criterion to amend the centre wavelength of the detector to accommodate different purpose, especially the estimation of leaf nitrogen content (LNC) in rice. By this way, the wavelengths high-correlated to the LNC can be ranked in a descending order, which are used to estimate rice LNC sequentially. In this paper, a HSL system which works based on a wide spectrum emission and a 32-channel detector is conducted to collect the reflectance spectra of rice leaf. These spectra collected by HSL cover a range of 538 nm – 910 nm with a resolution of 12 nm. These 32 wavelengths are strong absorbed by chlorophyll in green plant among this range. The relationship between the rice LNC and reflectance-based spectra is modeled using partial least squares (PLS) and support vector machines (SVMs) based on calibration and validation datasets respectively. The results indicate that I) wavelength selection method of HSL based on feature weighting is effective to choose the nitrogen-sensitive wavelengths, which can also be co-adapted with the hardware of HSL system friendly. II) The chosen wavelength has a high correlation with rice LNC which can be retrieved by using PLS and SVMs regression methods.

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Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery

Sensors ◽

10.3390/s21020613 ◽

2021 ◽

Vol 21 (2) ◽

pp. 613

Author(s):

Baohua Yang ◽

Jifeng Ma ◽

Xia Yao ◽

Weixing Cao ◽

Yan Zhu

Keyword(s):

Nitrogen Content ◽

Hyperspectral Imagery ◽

Leaf Nitrogen ◽

Support Vector ◽

Growth Monitoring ◽

Leaf Nitrogen Content ◽

Spectral Features ◽

Wheat Growth ◽

Important Indicator ◽

Model Based

Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R2 = 0.975 for calibration set, R2 = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring.

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Estimating the Leaf Nitrogen Content with a New Feature Extracted from the Ultra-High Spectral and Spatial Resolution Images in Wheat

Remote Sensing ◽

10.3390/rs13040739 ◽

2021 ◽

Vol 13 (4) ◽

pp. 739

Author(s):

Jiale Jiang ◽

Jie Zhu ◽

Xue Wang ◽

Tao Cheng ◽

Yongchao Tian ◽

...

Keyword(s):

Nitrogen Content ◽

Precision Agriculture ◽

Field Experiments ◽

Mean Squared Error ◽

Growing Season ◽

Leaf Nitrogen ◽

Textural Analysis ◽

Hyperspectral Images ◽

Leaf Nitrogen Content ◽

New Feature

Real-time and accurate monitoring of nitrogen content in crops is crucial for precision agriculture. Proximal sensing is the most common technique for monitoring crop traits, but it is often influenced by soil background and shadow effects. However, few studies have investigated the classification of different components of crop canopy, and the performance of spectral and textural indices from different components on estimating leaf nitrogen content (LNC) of wheat remains unexplored. This study aims to investigate a new feature extracted from near-ground hyperspectral imaging data to estimate precisely the LNC of wheat. In field experiments conducted over two years, we collected hyperspectral images at different rates of nitrogen and planting densities for several varieties of wheat throughout the growing season. We used traditional methods of classification (one unsupervised and one supervised method), spectral analysis (SA), textural analysis (TA), and integrated spectral and textural analysis (S-TA) to classify the images obtained as those of soil, panicles, sunlit leaves (SL), and shadowed leaves (SHL). The results show that the S-TA can provide a reasonable compromise between accuracy and efficiency (overall accuracy = 97.8%, Kappa coefficient = 0.971, and run time = 14 min), so the comparative results from S-TA were used to generate four target objects: the whole image (WI), all leaves (AL), SL, and SHL. Then, those objects were used to determine the relationships between the LNC and three types of indices: spectral indices (SIs), textural indices (TIs), and spectral and textural indices (STIs). All AL-derived indices achieved more stable relationships with the LNC than the WI-, SL-, and SHL-derived indices, and the AL-derived STI was the best index for estimating the LNC in terms of both calibration (Rc2 = 0.78, relative root mean-squared error (RRMSEc) = 13.5%) and validation (Rv2 = 0.83, RRMSEv = 10.9%). It suggests that extracting the spectral and textural features of all leaves from near-ground hyperspectral images can precisely estimate the LNC of wheat throughout the growing season. The workflow is promising for the LNC estimation of other crops and could be helpful for precision agriculture.

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Estimation of tomato leaf nitrogen content using continuum-removal spectroscopy analysis technique

10.1117/12.977406 ◽

2012 ◽

Author(s):

Yongjun Ding ◽

Minzan Li ◽

Lihua Zheng ◽

Hong Sun

Keyword(s):

Nitrogen Content ◽

Leaf Nitrogen ◽

Tomato Leaf ◽

Leaf Nitrogen Content ◽

Spectroscopy Analysis ◽

Analysis Technique ◽

Continuum Removal

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Effect of fluorescence characteristics and different algorithms on the estimation of leaf nitrogen content based on laser-induced fluorescence lidar in paddy rice

Optics Express ◽

10.1364/oe.25.003743 ◽

2017 ◽

Vol 25 (4) ◽

pp. 3743 ◽

Cited By ~ 19

Author(s):

Jian Yang ◽

Jia Sun ◽

Lin Du ◽

Biwu Chen ◽

Zhenbing Zhang ◽

...

Keyword(s):

Nitrogen Content ◽

Laser Induced Fluorescence ◽

Leaf Nitrogen ◽

Paddy Rice ◽

Leaf Nitrogen Content ◽

Fluorescence Characteristics

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Leaf traits and gas exchange in saplings of native tree species in the Central Amazon

Scientia Agricola ◽

10.1590/s0103-90162010000600002 ◽

2010 ◽

Vol 67 (6) ◽

pp. 624-632 ◽

Cited By ~ 14

Author(s):

Keila Rego Mendes ◽

Ricardo Antonio Marenco

Keyword(s):

Gas Exchange ◽

Nitrogen Content ◽

Leaf Area ◽

Tree Species ◽

Leaf Traits ◽

Leaf Nitrogen ◽

Dry Season ◽

Leaf Thickness ◽

Rainfall Regime ◽

Leaf Nitrogen Content

Global climate models predict changes on the length of the dry season in the Amazon which may affect tree physiology. The aims of this work were to determine the effect of the rainfall regime and fraction of sky visible (FSV) at the forest understory on leaf traits and gas exchange of ten rainforest tree species in the Central Amazon, Brazil. We also examined the relationship between specific leaf area (SLA), leaf thickness (LT), and leaf nitrogen content on photosynthetic parameters. Data were collected in January (rainy season) and August (dry season) of 2008. A diurnal pattern was observed for light saturated photosynthesis (Amax) and stomatal conductance (g s), and irrespective of species, Amax was lower in the dry season. However, no effect of the rainfall regime was observed on g s nor on the photosynthetic capacity (Apot, measured at saturating [CO2]). Apot and leaf thickness increased with FSV, the converse was true for the FSV-SLA relationship. Also, a positive relationship was observed between Apot per unit leaf area and leaf nitrogen content, and between Apot per unit mass and SLA. Although the rainfall regime only slightly affects soil moisture, photosynthetic traits seem to be responsive to rainfall-related environmental factors, which eventually lead to an effect on Amax. Finally, we report that little variation in FSV seems to affect leaf physiology (Apot) and leaf anatomy (leaf thickness).

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