scholarly journals Estimation of leaf nitrogen content from nondestructive methods in Eucalyptus tereticornis and Eucalyptus saligna plantations

2021 ◽  
Vol 74 (3) ◽  
pp. 9655-9666
Author(s):  
Juan Carlos Valverde
Keyword(s):  
Nitrogen Content ◽  
Low Cost ◽  
Leaf Nitrogen ◽  
Control Treatment ◽  
Leaf Nitrogen Content ◽  
Eucalyptus Tereticornis ◽  
Eucalyptus Saligna ◽  
Nondestructive Methods ◽  
Negative Relationships

The determination of leaf nitrogen content (LNC) by indirect methods is essential for silvicultural management of forest crops. The application of photography or rapid measurement equipment, such as chlorophyll index (soil-plant analysis development-SPAD), is increasingly used due to its low-cost, ease of estimation and accuracy. Therefore, the aim of this study was to estimate foliar nitrogen content from nondestructive methods in plantations of Eucalyptus tereticornis and Eucalyptus saligna using three urea treatments (120 kg N ha-1, 240 kg N ha-1 and a control treatment without urea). For each treatment, 10 trees were selected, including four for the validation of the equations. The LNC was directly evaluated for color with the CIEL*a*b* model, photographic measurement with the RGB model, SPAD measurement and destructive estimation of nitrogen in leaves. The results showed negative relationships with the L* (luminosity) and b* (trend from yellow to green) indices, while the a* (red to green trend) index was discarded, with SPAD positive relationships were found with LNC and RGB space. In the R and B indices, the greatest negative relationships were found. It was determined that the multivariate equation Y=a+b1x1+b2x2+…+bnxn can be used for this type of study. It was also determined that the LNC=0.389+0.026SPAD model was the optimum for E. tereticornis and the LNC=3.826-0.001R-0.10B equation was the optimum for E. saligna.

Determination of Pear Leaf Nitrogen Content Based on Multi-Spectral Imaging Technology and Multivariate Calibration

2011 ◽  
Vol 467-469 ◽  
pp. 718-724 ◽  
Author(s):  
Hai Qing Yang ◽  
Gang Lv
Keyword(s):  
Nitrogen Content ◽  
Input Data ◽  
Imaging System ◽  
Spectral Imaging ◽  
Fruit Trees ◽  
Leaf Nitrogen ◽  
Front Face ◽  
Leaf Nitrogen Content ◽  
Calibration Models

Fast determination of mineral nutrition contents of fruit trees is essential for orchard precision fertilizing management. A multi-spectral imaging system was developed and tested for the measurement of leaf nitrogen content of fruit trees in the study. Images taken using this system included visible images(R-G-B) and near-infrared image(NIR). These images were further processed into several indices such as RVI, NDVI, GNDVI, -log(R) and –log(G). Total 185 leaf samples were picked from Huang-hua pear trees which were planted in three orchards with different nitrogen fertilizing levels. Among them, 135 samples were randomly sorted out as calibration set with the remaining 50 as prediction set. A SPAD-502 chlorophyll meter was used for nitrogen reference measurement. In calibration modeling, leaf front and back faces were photographed respectively. Calibration models were developed based on single variant as well as multiple variants. The result shows that calibration models based on leaf front face are better than those based on leaf back face. Among others, R and G are the most important factors for nitrogen determination with less contribution of B and NIR. Based on the images of leaf front face, R, G, RVI, NDVI, GNDVI, -log(R) and –log(G) were found significantly correlated with nitrogen content with correlation coefficients of prediction (r_pre^2) of 0.7516, 0.7396, 0.7332, 0.7220, 0.7588, 0.7598 and 0.7379 respectively. The linear combinations of R-G-B-NIR, RVI-NDVI-GNDVI and NDVI-GNDVI achieved better prediction accuracy with r_pre^2 of 0.8157, 0.7775 and 0.7661 respectively. To further improve the prediction accuracy, a three-layer BP-ANN was developed with the three combinations as its input data. The result shows that BP-ANN has an excellent performance to predict nitrogen contents. BP-ANN with the input of R-G-B-NIR performs best with r_pre^2 of 0.9386 and maximum error of 3.52(SPAD). The study suggests that multi-spectral imaging system integrated with prediction model of BP-ANN with original reflectance intensity of R-G-B-NIR channels as its input data is promising for in situ measurement of nitrogen content of fruit tree.

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

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.

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

2010 ◽  
Vol 67 (6) ◽  
pp. 624-632 ◽  
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).

Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images

2019 ◽  
Vol 83 ◽  
pp. 101907 ◽  
Author(s):  
Lucas Prado Osco ◽  
Ana Paula Marques Ramos ◽  
Érika Akemi Saito Moriya ◽  
Maurício de Souza ◽  
José Marcato Junior ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Nitrogen Content ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content ◽  
Valencia Orange ◽  
Orange Trees

scholarly journals Estimation of Leaf Nitrogen Content from Spectral Characteristics of Rice Canopy

2001 ◽  
Vol 1 ◽  
pp. 81-89 ◽  
Author(s):  
Chwen-Ming Yang
Keyword(s):  
Nitrogen Content ◽  
Linear Relationship ◽  
Spectral Reflectance ◽  
Spectral Characteristics ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Content ◽  
Nitrogen Application ◽  
Spectral Signatures ◽  
Growing Period ◽  
Application Rates

Ground-based remotely sensed reflectance spectra of hyperspectral resolution were monitored during the growing period of rice under various nitrogen application rates. It was found that reflectance spectrum of rice canopy changed in both wavelength and reflectance as the plants developed. Fifteen characteristic wavebands were identified from the apparent peaks and valleys of spectral reflectance curves, in accordance with the results of the first-order differentiation, measured over the growing season of rice. The bandwidths and center wavelengths of these characteristic wavebands were different among nitrogen treatments. The simplified features by connecting these 15 characteristic wavelengths may be considered as spectral signatures of rice canopy, but spectral signatures varied with developmental age and nitrogen application rates. Among these characteristic wavebands, the changes of the wavelength in band 11 showed a positive linear relationship with application rates of nitrogen fertilizer, while it was a negative linear relationship in band 5. Mean reflectance of wavelengths in bands 1, 2, 3, 5, 11, and 15 was significantly correlated with application rates. Reflectance of these six wavelengths changed nonlinearly after transplanting and could be used in combination to distinguish rice plants subjected to different nitrogen application rates. From the correlation analyses, there are a variety of correlation coefficients for spectral reflectance to leaf nitrogen content in the range of 350-2400 nm. Reflectance of most wavelengths exhibited an inverse correlation with leaf nitrogen content, with the largest negative value (r = �0.581) located at about 1376 nm. Changes in reflectance at 1376 nm to leaf nitrogen content during the growing period were closely related and were best fitted to a nonlinear function. This relationship may be used to estimate and to monitor nitrogen content of rice leaves during rice growth. Reflectance of red light minimum and near-infrared peak and leaf nitrogen content were correlated nonlinearly.

scholarly journals Wheat Leaf Rust Uredospore Production on Adult Plants: Influence of Leaf Nitrogen Content and Septoria tritici Blotch

2004 ◽  
Vol 94 (7) ◽  
pp. 712-721 ◽  
Author(s):  
Corinne Robert ◽  
Marie-Odile Bancal ◽  
Christian Lannou
Keyword(s):  
Nitrogen Content ◽  
Leaf Rust ◽  
Lesion Size ◽  
Leaf Nitrogen ◽  
Spore Production ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Infected Leaf ◽  
Leaf Nitrogen Content ◽  
Wheat Seedlings

Leaf rust uredospore production and lesion size were measured on flag leaves of adult wheat plants in a glasshouse for different lesion densities. We estimated the spore weight produced per square centimeter of infected leaf, per lesion, and per unit of sporulating area. Three levels of fertilization were applied to the plants to obtain different nitrogen content for the inoculated leaves. In a fourth treatment, we evaluated the effect of Septoria tritici blotch on leaf rust uredospore production. The nitrogen and carbon content of the spores was unaffected or marginally affected by lesion density, host leaf nitrogen content, or the presence of Mycosphaerella graminicola on the same leaf. In leaves with a low-nitrogen content, spore production per lesion was reduced, but lesion size was unaffected. A threshold effect of leaf nitrogen content in spore production was however, evident, since production was similar in the medium- and high-fertilizer treatments. In leaves inoculated with M. graminicola and Puccinia triticina, the rust lesions were smaller and produced fewer spores. The relationships among rust lesion density, lesion size, and uredospore production were fitted to a model. We determined that the density effect on spore production resulted mainly from a reduction in lesion size, the spore production per unit of sporulating surface being largely independent of lesion density. These results are consistent with those obtained previously on wheat seedlings. The main difference was that the sporulation period lasted longer in adult leaves.

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