Tritrophic Effects of Leaf Nitrogen onLiriomyza trifolii(Burgess) and an Associated ParasitoidChrysocharis oscinidis(Ashmead) on Bean

1996 ◽  
Vol 6 (2) ◽  
pp. 186-192 ◽  
Author(s):  
Lynne N. Kaneshiro ◽  
Marshall W. Johnson
Keyword(s):  
Leaf Nitrogen ◽  
Tritrophic Effects

Estimation of Canopy Leaf Nitrogen Status Using Imaging Spectrometer and Digital Camera in Cotton

2011 ◽  
Vol 37 (6) ◽  
pp. 1039-1048 ◽  
Author(s):  
Fang-Yong WANG ◽  
Ke-Ru WANG ◽  
Shao-Kun LI ◽  
Shi-Ju GAO ◽  
Chun-Hua XIAO ◽  
...  
Keyword(s):  
Digital Camera ◽  
Leaf Nitrogen ◽  
Nitrogen Status ◽  
Imaging Spectrometer

Variations in the relationship between maximum leaf carboxylation rate and leaf nitrogen concentration

2014 ◽  
Vol 38 (6) ◽  
pp. 640-652 ◽  
Author(s):  
YAN Shuang ◽  
◽  
ZHANG Li ◽  
JING Yuan-Shu ◽  
HE Hong-Lin ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Concentration ◽  
The Relationship

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 Analysis of Common Canopy Reflectance Spectra for Indicating Leaf Nitrogen Concentrations in Wheat and Rice

2007 ◽  
Vol 10 (4) ◽  
pp. 400-411 ◽  
Author(s):  
Yan Zhu ◽  
Yongchao Tian ◽  
Xia Yao ◽  
Xiaojun Liu ◽  
Weixing Cao
Keyword(s):  
Leaf Nitrogen ◽  
Reflectance Spectra ◽  
Canopy Reflectance ◽  
Nitrogen Concentrations
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