甘蔗叶片叶绿素含量的光谱测定及其干旱胁迫检测

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Jingyao Gai, Jingyong Wang, Sasa Xie, Lirong Xiang, Ziting Wang
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引用次数: 0

摘要

干旱是影响甘蔗产量的主要非生物胁迫。甘蔗生长过程中缺水会导致叶片色素含量减少,如叶绿素,称为褪绿。虽然光谱反射特征的变化被认为是干旱胁迫引起叶绿素含量变化的显著标志,但叶片叶绿素含量与光谱反射特征之间的定量关系仍未得到充分探讨。在这项研究中,我们系统地建立了一个利用VIS/NIR反射光谱和特征波段提取技术估算干旱甘蔗叶片叶绿素含量的模型。利用不同控制灌溉条件下甘蔗伸长前期叶片的光谱数据采集,并采用标准分析方法采集叶绿素含量。对不同特征波段提取技术和回归模型进行了比较和讨论,以获得性能最好的叶绿素含量估算模型。定量结果表明,将连续投影算法(SPA)提取的特征波段与叠加回归模型相结合,只需要4.3%的原始光谱变量作为输入,就能获得较高的叶绿素含量估计性能(R2 = 0.9834, RMSE = 0.0544 mg/cm2)。该研究为大规模栽培中准确、无创地估算干旱胁迫水平提供了理论依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Spectroscopic determination of chlorophyll content in sugarcane leaves for drought stress detection

Spectroscopic determination of chlorophyll content in sugarcane leaves for drought stress detection

Drought is a major abiotic stress that affects the productivity of sugarcane worldwide. Water deficiency during sugarcane growth will lead to a reduction in leaf pigment content, such as chlorophyll, known as chlorosis. Although changes in spectral reflectance signature were identified a conspicuous sign of chlorophyll content changes caused by drought stress, the quantitative relationships between leaf chlorophyll content and spectral reflection signatures are still poorly explored. In this study, we present our contribution in systematically establishing a model for estimating leaf chlorophyll content in drought-affected sugarcane using VIS/NIR reflectance spectroscopy and characteristic band extraction techniques. Leaves of sugarcane plants at early elongation stage under different controlled irrigation conditions were used for spectra data collection, and the chlorophyll contents were collected with standard analytical methods. Different characteristic band extraction techniques and regression models were compared and discussed to obtain a chlorophyll content estimation model with the best performance. As the quantitative results, the combination of characteristic bands extracted by the successive projection algorithm (SPA) with a Stacking regression model achieved a high chlorophyll content estimation performance (R2 = 0.9834, RMSE  = 0.0544 mg/cm2) with only 4.3% of original spectral variables as inputs. This study provides a theoretical basis for accurate and non-invasive drought stress level estimation in large-scale cultivation.

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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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