scholarly journals Research on the Effects of Drying Temperature on Nitrogen Detection of Different Soil Types by Near Infrared Sensors

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 391 ◽  
Author(s):  
Pengcheng Nie ◽  
Tao Dong ◽  
Yong He ◽  
Shupei Xiao
Keyword(s):  
Near Infrared ◽  
Soil Types ◽  
Infrared Sensors ◽  
Drying Temperature ◽  
Nitrogen Detection

scholarly journals Application of near infrared sensors to preconcentration of hydrothermally-formed copper ore

2016 ◽  
Vol 85 ◽  
pp. 148-167 ◽  
Author(s):  
Shekwonyadu Iyakwari ◽  
Hylke J. Glass ◽  
Gavyn K. Rollinson ◽  
Przemyslaw B. Kowalczuk
Keyword(s):  
Near Infrared ◽  
Infrared Sensors ◽  
Copper Ore

scholarly journals Application of Near-infrared Spectroscopy and Multiple Spectral Algorithms to Explore the Effect of Soil Particle Sizes on Soil Nitrogen Detection

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2486 ◽  
Author(s):  
Shupei Xiao ◽  
Yong He
Keyword(s):  
Plant Growth ◽  
Soil Nitrogen ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Soil Particle ◽  
Detection Accuracy ◽  
Particle Sizes ◽  
Reflectivity Spectra ◽  
Nitrogen Detection

Soil nitrogen is the key parameter supporting plant growth and development; it is also the material basis of plant growth. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near-infrared (NIR) spectroscopy is widely used for rapid detection of soil nutrients. In this study, the variation law of soil NIR reflectivity spectra with soil particle sizes was studied. Moreover, in order to precisely study the effect of particle size on soil nitrogen detection by NIR, four different spectra preprocessing methods and five different chemometric modeling methods were used to analyze the soil NIR spectra. The results showed that the smaller the soil particle sizes, the stronger the soil NIR reflectivity spectra. Besides, when the soil particle sizes ranged 0.18–0.28 mm, the soil nitrogen prediction accuracy was the best based on the partial least squares (PLS) model with the highest Rp2 of 0.983, the residual predictive deviation (RPD) of 6.706. The detection accuracy was not ideal when the soil particle sizes were too big (1–2 mm) or too small (0–0.18 mm). In addition, the relationship between the mixing spectra of six different soil particle sizes and the soil nitrogen detection accuracy was studied. It was indicated that the larger the gap between soil particle sizes, the worse the accuracy of soil nitrogen detection. In conclusion, soil nitrogen detection precision was affected by soil particle sizes to a large extent. It is of great significance to optimize the pre-treatments of soil samples to realize rapid and accurate detection by NIR spectroscopy.

scholarly journals Leaf Chlorophyll Content Estimation of Winter Wheat Based on Visible and Near-Infrared Sensors

Sensors ◽  
2016 ◽  
Vol 16 (4) ◽  
pp. 437 ◽  
Author(s):  
Jianfeng Zhang ◽  
Wenting Han ◽  
Lvwen Huang ◽  
Zhiyong Zhang ◽  
Yimian Ma ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Chlorophyll Content ◽  
Near Infrared ◽  
Infrared Sensors ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

scholarly journals Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms

Sensors ◽  
2017 ◽  
Vol 17 (5) ◽  
pp. 1102 ◽  
Author(s):  
Pengcheng Nie ◽  
Tao Dong ◽  
Yong He ◽  
Fangfang Qu
Keyword(s):  
Soil Nitrogen ◽  
Near Infrared ◽  
Infrared Sensors

UAV Remote Sensing Assessment of Crop Growth

2021 ◽  
Vol 87 (12) ◽  
pp. 891-899
Author(s):  
Freda Elikem Dorbu ◽  
Leila Hashemi-Beni ◽  
Ali Karimoddini ◽  
Abolghasem Shahbazi
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Crop Growth ◽  
Infrared Sensors ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Growth Indicators

The introduction of unmanned-aerial-vehicle remote sensing for collecting high-spatial- and temporal-resolution imagery to derive crop-growth indicators and analyze and present timely results could potentially improve the management of agricultural businesses and enable farmers to apply appropriate solution, leading to a better food-security framework. This study aimed to analyze crop-growth indicators such as the normalized difference vegetation index (NDVI), crop height, and vegetated surface roughness to determine the growth of corn crops from planting to harvest. Digital elevation models and orthophotos generated from the data captured using multispectral, red/green/blue, and near-infrared sensors mounted on an unmanned aerial vehicle were processed and analyzed to calculate the various crop-growth indicators. The results suggest that remote sensing-based growth indicators can effectively determine crop growth over time, and that there are similarities and correlations between the indicators.

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