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Hyperspectral Characteristics of an Individual Leaf of Wheat Grown under Nitrogen Gradient.

作者信息

Jung Jae Gyeong, Song Ki Eun, Hong Sun Hee, Shim Sang In

机构信息

Department of Agronomy, Gyeongsang National University, Jinju 52828, Korea.

Department of Plant Life Science, Hankyong National University, Anseong 17579, Korea.

出版信息

Plants (Basel). 2021 Oct 25;10(11):2291. doi: 10.3390/plants10112291.

DOI:10.3390/plants10112291
PMID:34834653
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8626060/
Abstract

Since the application of hyperspectral technology to agriculture, many scientists have been conducting studies to apply the technology in crop diagnosis. However, due to the properties of optical devices, the reflectances obtained according to the image acquisition conditions are different. Nevertheless, there is no optimized method for minimizing such technical errors in applying hyperspectral imaging. Therefore, this study was conducted to find the appropriate image acquisition conditions that reflect the growth status of wheat grown under different nitrogen fertilization regimes. The experiment plots were comprised of six plots with various N application levels of 145.6 kg N ha (N1), 109.2 kg N ha (N2), 91.0 kg N ha (N3), 72.8 kg N ha (N4), 54.6 kg N ha (N5), and 36.4 kg N ha (N6). Hyperspectral image acquisitions were performed at different shooting angles of 105° and 125° from the surface, and spike, flag leaf, and the second uppermost leaf were divided into five parts from apex to base when analyzing the images. The growth analysis conducted at heading showed that the N6 was 85.6% in the plant height, 44.1% in LAI, and 64.9% in SPAD as compared to N1. The nitrogen content in the leaf decreased by 55.2% compared to N1 and the quantity was 44.9% in N6 compared to N1. Based on the vegetation indices obtained from hyperspectral reflectances at the heading stage, the spike was not suitable for analysis. In the case of the flag leaf and the 2nd uppermost leaf, the vegetation indices from spectral data taken at 105 degrees were more appropriate for acquiring imaging data by clearly dividing the effects of fertilization level. The results of the regional variation in a leaf showed that the region of interest (ROI), which is close to the apex of the flag leaf and the base of the second uppermost leaf, has a high coefficient of determination between the fertilization levels and the vegetation indices, which effectively reflected the status of wheat.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/d064857ceb60/plants-10-02291-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/ca580c09e8fb/plants-10-02291-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/04d5bf7ad8eb/plants-10-02291-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/2847c1000235/plants-10-02291-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/5c639bd59a57/plants-10-02291-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/dfa1c643c81f/plants-10-02291-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/2deb4eda1fe5/plants-10-02291-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/e25e0fe65221/plants-10-02291-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/dc6b58fbdbe9/plants-10-02291-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/d064857ceb60/plants-10-02291-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/ca580c09e8fb/plants-10-02291-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/04d5bf7ad8eb/plants-10-02291-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/2847c1000235/plants-10-02291-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/5c639bd59a57/plants-10-02291-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/dfa1c643c81f/plants-10-02291-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/2deb4eda1fe5/plants-10-02291-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/e25e0fe65221/plants-10-02291-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/dc6b58fbdbe9/plants-10-02291-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/209e/8626060/d064857ceb60/plants-10-02291-g009.jpg

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本文引用的文献

1
Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content.用于预测冬小麦叶片含水量的新型高光谱指数与机器学习模型的比较
Plant Methods. 2021 Mar 31;17(1):34. doi: 10.1186/s13007-021-00737-2.
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Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery.基于近红外高光谱图像光谱特征和深度特征融合的小麦叶片氮含量估算。
Sensors (Basel). 2021 Jan 17;21(2):613. doi: 10.3390/s21020613.
3
Nitrogen partitioning and remobilization in relation to leaf senescence, grain yield and protein concentration in Indian wheat cultivars.
印度小麦品种中与叶片衰老、籽粒产量和蛋白质浓度相关的氮素分配与再转运
Field Crops Res. 2020 Jun 15;251:107778. doi: 10.1016/j.fcr.2020.107778.
4
Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms.利用高光谱反射率和机器学习算法对茶叶叶绿素含量进行无损检测
Plants (Basel). 2020 Mar 17;9(3):368. doi: 10.3390/plants9030368.
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Effect of nitrogen levels and nitrogen ratios on lodging resistance and yield potential of winter wheat (Triticum aestivum L.).氮素水平和氮素比例对冬小麦(普通小麦)抗倒伏性及产量潜力的影响
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