scholarly journals Canopy Nitrogen Concentration Monitoring Techniques of Summer Corn Based on Canopy Spectral Information

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4123
Author(s):  
Lu Liu ◽  
Zhigong Peng ◽  
Baozhong Zhang ◽  
Zheng Wei ◽  
Nana Han ◽  
...  
Keyword(s):  
Spectral Index ◽  
Spectral Reflectance ◽  
Nitrogen Concentration ◽  
Growth Period ◽  
Monitoring Techniques ◽  
Index Model ◽  
First Derivative ◽  
Band Combination ◽  
Canopy Nitrogen ◽  
Canopy Spectral Reflectance

Crop nitrogen monitoring techniques, particularly choosing sensitive monitoring bands and suitable monitoring models, have great significance both in theory and in practice for achieving non-destructive monitoring of nitrogen concentration and accurate management of water and fertilizer in large-scale areas. In this study, a lysimeter experiment was carried out to examine the characteristics of canopy spectral reflectance variation of summer corn under different fertilization levels. The relationship between canopy spectral reflectance and nitrogen concentration was investigated, based on which sensitive bands for the corn canopy nitrogen monitoring were selected and a suitable spectral index model was determined. The results suggest that under different fertilization levels, the canopy spectral reflectance of summer corn decreases with the increase of the canopy nitrogen concentration in the visible light band, but varies in the opposite direction in the near-infrared band, with a premium put on a higher correlation between the spectral reflectance of the characteristic bands and their first derivatives and the canopy nitrogen concentration. The most sensitive bands for monitoring the canopy nitrogen concentration using spectral reflectance and its first derivative are found to be 762 nm and 726 nm and the correlation coefficients are 0.550 and 0.795, respectively. The optimal band combination, generated by multivariate stepwise regression analysis, is composed of 762 nm, 944 nm and 957 nm bands. From the 55 reported spectral index models of crop nitrogen concentration monitoring, the most suitable index model, NDRE, is chosen such that this index model has the highest correlation with the canopy nitrogen concentration in summer corn. This model has a significant positive correlation with the canopy nitrogen concentration at each growth period, and the correlation coefficient is up to 0.738 during the whole growth period. Spectral monitoring models of canopy nitrogen concentration are constructed using sensitive bands, and a combination of bands and the spectral index, suggesting that these models perform well in monitoring. The models arranged in descending order of simulation accuracy are as follows: the suitable spectral index model, the optimal band combination model, the sensitive band reflectance first derivative model, the sensitive band reflectance model. The determination coefficients are 0.754, 0.711, 0.639 and 0.306, respectively.

scholarly journals Comparison of the Prediction of Winter Wheat Leaf Water Content by Using New Hyperspectral Index and Machine Learning Models

2020 ◽  
Author(s):  
Juanjuan Zhang ◽  
Wen Zhang ◽  
Shuping Xiong ◽  
Zhaoxiang Song ◽  
Wenzhong Tian ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Water Content ◽  
Spectral Index ◽  
Spectral Reflectance ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Full Band ◽  
Wheat Leaf ◽  
Canopy Spectral Reflectance ◽  
Better Than

Abstract In this study, hyperspectral technology was used to establish the winter wheat leaf water content inversion model to provide technical reference for winter wheat precision irrigation. In a field experiment, seven different wheat varieties for different irrigation times were treated during two consecutive years. The data onto canopy spectral reflectance and leaf water content (LWC) of winter wheat were collected. Five different modeling methods, Spectral index, partial least squares (PLSR), random forest (RF), extreme random tree (ERT) and k-nearest neighbor (KNN) were used to construct LWC estimation models. The results showed that the canopy spectral reflectance was directly proportional to the irrigation times, especially in the near infrared band. As for LWC, the prediction effect of the newly differential spectral index DVI (R1185, R1308) is better than the existing spectral index, and R2 are 0.78. Because of the large amount of hyperspectral data. The correlation coefficient method (CA) and loading weight (x-Lw) are used to select the water characteristic bands from the full band. The results show that the accuracy of the model based on the characteristic band is not significantly lower than that of the full band. Among these models, the ERT- x-Lw model performs best (R2 and RMSE of 0.88 and 1.81; 0.84 and 1.62 for calibration and validation, respectively). In addition, the accuracy of LWC estimation model constructed by ERT-x-Lw was better than that of DVI (R1185, R1307). The results provide technical reference and basis for crop water monitoring and diagnosis under similar production conditions.

Nitrogen, Phosphorus and Water Contents During Grain Development and Maturation in Wheat

1977 ◽  
Vol 4 (5) ◽  
pp. 799 ◽  
Author(s):  
I Sofield ◽  
IF Wardlaw ◽  
LT Evans ◽  
SY Zee
Keyword(s):  
Grain Growth ◽  
Dry Matter ◽  
Nitrogen Concentration ◽  
Growth Period ◽  
Calcium Content ◽  
Dry Weight ◽  
Water Entry ◽  
Grain Development ◽  
Nitrogen And Phosphorus ◽  
Steady Rate

Plants of five cultivars of wheat were grown under controlled-environmental conditions in order to analyse the effect of cultivar and of temperature and illuminance after anthesis on the accumulation of nitrogen and phosphorus by grains in relation to dry matter. The water relations of the grain during maturation were also examined, using calcium content as an index of water entry. The nitrogen and phosphorus contents of grains increased linearly throughout the grain growth period. The percentage of nitrogen and phosphorus in grains fell sharply during the first few days after anthesis but rose progressively thereafter. The higher the temperature, and the lower the illuminance, the higher was the percentage of nitrogen in the grain of all cultivars. Such conditions also reduce final grain size, but their effects on nitrogen concentration in the grain were apparent early in grain development. No evidence was found of a flush of nitrogen or phosphorus into the grain late in its development. Water entry into the grain continued at a steady rate until maximum grain dry weight was reached, then ceased suddenly. No evidence was found of an increased rate of water loss by the grain at that stage, and the rapid fall in water content at the cessation of grain growth may have been due to blockage of the chalazal zone of entry into the grain by the deposition of lipids. Accumulation of dry matter, nitrogen and phosphorus and entry of water into the grain all ceased at the time of lipid deposition in the chalazal zone.

scholarly journals Nitrogen for growth of stock plants and production of strawberry runner tips

Bragantia ◽  
2012 ◽  
Vol 71 (3) ◽  
pp. 394-399 ◽  
Author(s):  
Djeimi Isabel Janisch ◽  
Jerônimo Luiz Andriolo ◽  
Vinícius Toso ◽  
Kamila Gabriele Ferreira dos Santos ◽  
Jéssica Maronez de Souza
Keyword(s):  
Leaf Area ◽  
Nutrient Solution ◽  
Dry Matter ◽  
Nitrogen Concentration ◽  
Growth Period ◽  
Dry Matter Partitioning ◽  
Area Index ◽  
Camino Real ◽  
Nitrogen Concentrations ◽  
Crown Roots

The objective of this research was to determine growth and dry matter partitioning among organs of strawberry stock plants under five Nitrogen concentrations in the nutrient solution and its effects on emission and growth of runner tips. The experiment was carried out under greenhouse conditions, from September 2010 to March 2011, in a soilless system with Oso Grande and Camino Real cultivars. Nitrogen concentrations of 5.12, 7.6, 10.12 (control), 12.62 and 15.12 mmol L-1 in the nutrient solution were studied in a 5x2 factorial randomised experimental design. All runner tips bearing at least one expanded leaf (patent requested) were collected weekly and counted during the growth period. The number of leaves, dry matter (DM) of leaves, crown and root, specific leaf area and leaf area index (LAI) was determined at the final harvest. Increasing N concentration in the nutrient solution from 5.12 to 15.12 mmol L-1 reduces growth of crown, roots and LAI of strawberry stock plants but did not affect emission and growth of runner tips. It was concluded that for the commercial production of plug plants the optimal nitrogen concentration in the nutrient solution should be 5.12 mmol L-1.

scholarly journals Correlation Analysis Procedures for Canopy Spectral Reflectance Data of Seashore Paspalum under Traffic Stress

2003 ◽  
Vol 128 (3) ◽  
pp. 343-348 ◽  
Author(s):  
Yiwei Jiang ◽  
Robert N. Carrow ◽  
Ronny R. Duncan
Keyword(s):  
Spectral Reflectance ◽  
Soil Compaction ◽  
Canopy Temperature ◽  
Quality Analysis ◽  
Stress Index ◽  
Canopy Reflectance ◽  
Seashore Paspalum ◽  
First Derivative ◽  
Turf Quality ◽  
Optimum Position

Traffic stresses often cause a decline in turfgrass quality. Analysis of spectral reflectance is valuable for assessing turfgrass canopy status. The objectives of this study were to determine correlations of narrow band canopy reflectance and selected reflectance indices with canopy temperature and turf quality for seashore paspalum exposed to wear and wear plus soil compaction traffic stresses, and to evaluate the effects of the first derivative of reflectance and degree of data smoothing (spectral manipulations) on such correlations. `Sea Isle 1' seashore paspalum (Paspalum vaginatum Swartz) was established on a simulated sports field during 1999 and used for this study. Compared to original reflectance, the first derivative of reflectance increased the correlation coefficient (r) of certain wavelengths with canopy temperature and turf quality under both traffic stresses. Among 217 wavelengths tested between 400 and 1100 nm, the peak correlations of the first derivative of reflectance occurred at 661 nm and 664 nm for both canopy temperature and turf quality under wear stress, respectively, while the highest correlations were found at 667 nm and 820 to 869 nm for both variables under wear plus soil compaction. Collectively, the first derivative of reflectance at 667 nm was the optimum position to determine correlation with canopy temperature (r > 0.62) and turf quality (r < -0.72) under both traffic stresses. All correlations were not sensitive to degrees of smoothing of reflectance from 400 to 1100 nm. A ratio of R936/R661 (IR/R, Infrared/red) and R693/759 (stress index) had the strongest correlations with canopy temperature for wear (r = -0.63) and wear plus soil compaction (r = 0.66), respectively; and a ratio of R693/R759 had the strongest correlation with turf quality for both wear (r = -0.89) and wear plus soil compaction (r = -0.82). The results suggested that the first derivative of reflectance could be used to estimate any single wavelength simultaneously correlated with multiple turf canopy variables such as turf quality and canopy temperature, and that the stress index (R693/R759) was also a good indicator of canopy stress status.

scholarly journals Canopy Spectral Reflectance Feature and Leaf Water Potential of Sugarcane Inversion

2012 ◽  
Vol 25 ◽  
pp. 595-600 ◽  
Author(s):  
Haibo Chen ◽  
Pei Wang ◽  
Jiuhao Li ◽  
Jingdong Zhang ◽  
Luxiang Zhong
Keyword(s):  
Water Potential ◽  
Leaf Water Potential ◽  
Spectral Reflectance ◽  
Leaf Water ◽  
Canopy Spectral Reflectance

scholarly journals Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves

2020 ◽  
Vol 10 (7) ◽  
pp. 2259 ◽  
Author(s):  
Haixia Qi ◽  
Bingyu Zhu ◽  
Lingxi Kong ◽  
Weiguang Yang ◽  
Jun Zou ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Spectral Index ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Plant Density ◽  
Cell Structure ◽  
Spectral Bands ◽  
Study Results ◽  
Leaf Spectral Reflectance ◽  
Estimation Models

The purpose of this study is to determine a method for quickly and accurately estimating the chlorophyll content of peanut plants at different plant densities. This was explored using leaf spectral reflectance to monitor peanut chlorophyll content to detect sensitive spectral bands and the optimum spectral indicators to establish a quantitative model. Peanut plants under different plant density conditions were monitored during three consecutive growth periods; single-photon avalanche diode (SPAD) and hyperspectral data derived from the leaves under the different plant density conditions were recorded. By combining arbitrary bands, indices were constructed across the full spectral range (350–2500 nm) based on blade spectra: the normalized difference spectral index (NDSI), ratio spectral index (RSI), difference spectral index (DSI) and soil-adjusted spectral index (SASI). This enabled the best vegetation index reflecting peanut-leaf SPAD values to be screened out by quantifying correlations with chlorophyll content, and the peanut leaf SPAD estimation models established by regression analysis to be compared and analyzed. The results showed that the chlorophyll content of peanut leaves decreased when plant density was either too high or too low, and that it reached its maximum at the appropriate plant density. In addition, differences in the spectral reflectance of peanut leaves under different chlorophyll content levels were highly obvious. Without considering the influence of cell structure as chlorophyll content increased, leaf spectral reflectance in the visible (350–700 nm): near-infrared (700–1300 nm) ranges also increased. The spectral bands sensitive to chlorophyll content were mainly observed in the visible and near-infrared ranges. The study results showed that the best spectral indicators for determining peanut chlorophyll content were NDSI (R520, R528), RSI (R748, R561), DSI (R758, R602) and SASI (R753, R624). Testing of these regression models showed that coefficient of determination values based on the NDSI, RSI, DSI and SASI estimation models were all greater than 0.65, while root mean square error values were all lower than 2.04. Therefore, the regression model established according to the above spectral indicators was a valid predictor of the chlorophyll content of peanut leaves.

scholarly journals Genome-wide association mapping of soybean chlorophyll traits based on canopy spectral reflectance and leaf extracts

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Arun Prabhu Dhanapal ◽  
Jeffery D. Ray ◽  
Shardendu K. Singh ◽  
Valerio Hoyos-Villegas ◽  
James R. Smith ◽  
...  
Keyword(s):  
Association Mapping ◽  
Spectral Reflectance ◽  
Genome Wide Association ◽  
Leaf Extracts ◽  
Genome Wide ◽  
Canopy Spectral Reflectance
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