Hyperspectral Narrowbands and Their Indices on Assessing Nitrogen Contents of Cotton Crop Applications

2011 ◽  
pp. 579-590 ◽  
Author(s):  
Jianlong Li ◽  
Cherry Li ◽  
Dehua Zhao ◽  
Chengcheng Gang
Keyword(s):  
Cotton Crop ◽  
Nitrogen Contents

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

Effects of nitrogen and hydrogen in argon shielding gas on bead profile, delta-ferrite and nitrogen contents of the pulsed GTAW welds of AISI 316L stainless steel

2016 ◽  
Vol 58 (6) ◽  
pp. 489-494 ◽  
Author(s):  
Panyasak Phakpeetinan ◽  
Amnuysak Chianpairot ◽  
Ekkarut Viyanit ◽  
Fritz Hartung ◽  
Gobboon Lothongkum
Keyword(s):  
Stainless Steel ◽  
316L Stainless Steel ◽  
Aisi 316L ◽  
Shielding Gas ◽  
Delta Ferrite ◽  
Aisi 316L Stainless Steel ◽  
Pulsed Gtaw ◽  
Nitrogen Contents ◽  
Bead Profile

URANUS 76N

Alloy Digest ◽  
2009 ◽  
Vol 58 (1) ◽  
Keyword(s):  
Fracture Toughness ◽  
Corrosion Resistance ◽  
Shear Strength ◽  
Physical Properties ◽  
Tensile Properties ◽  
Heat Treating ◽  
Localized Corrosion ◽  
High Chromium ◽  
Equivalent Number ◽  
Nitrogen Contents

Abstract URANUS 76N contains high chromium, molybdenum, and nitrogen contents and has a pitting resistance equivalent number (PREN) ≥ 40. These factors explain why the duplex austenitic/ferritic stainless grade is very resistant to localized corrosion. The alloy has been used for offshore applications. This datasheet provides information on composition, physical properties, tensile properties, and shear strength as well as fracture toughness. It also includes information on corrosion resistance as well as forming, heat treating, and joining. Filing Code: SS-1028. Producer or source: Industeel USA, LLC.

scholarly journals Effect of leaf temperature on the estimation of photosynthetic and other traits of wheat leaves from hyperspectral reflectance

2020 ◽  
Author(s):  
Hammad A Khan ◽  
Yukiko Nakamura ◽  
Robert T Furbank ◽  
John R Evans
Keyword(s):  
Leaf Traits ◽  
Leaf Temperature ◽  
Physiological Traits ◽  
Leaf Mass Per Area ◽  
Hyperspectral Reflectance ◽  
Wheat Varieties ◽  
Unit Leaf ◽  
Temperature Signal ◽  
Wheat Leaves ◽  
Nitrogen Contents

Abstract A growing number of leaf traits can be estimated from hyperspectral reflectance data. These include structural and compositional traits, such as leaf mass per area (LMA) and nitrogen and chlorophyll content, but also physiological traits such a Rubisco carboxylation activity, electron transport rate, and respiration rate. Since physiological traits vary with leaf temperature, how does this impact on predictions made from reflectance measurements? We investigated this with two wheat varieties, by repeatedly measuring each leaf through a sequence of temperatures imposed by varying the air temperature in a growth room. Leaf temperatures ranging from 20 °C to 35 °C did not alter the estimated Rubisco capacity normalized to 25 °C (Vcmax25), or chlorophyll or nitrogen contents per unit leaf area. Models estimating LMA and Vcmax25/N were both slightly influenced by leaf temperature: estimated LMA increased by 0.27% °C–1 and Vcmax25/N increased by 0.46% °C–1. A model estimating Rubisco activity closely followed variation associated with leaf temperature. Reflectance spectra change with leaf temperature and therefore contain a temperature signal.

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