In vitro plant spectral response reveals dust stress

IF 5.6 1区 农林科学 Q1 AGRONOMY
Ali Darvishi Boloorani , Saham Mirzaei , Hossein Ali Bahrami , Masoud Soleimani , Najmeh Neysani Samany , Ramin Papi , Maryam Mahmoudi , Mohsen Bakhtiari , Alfredo Huete
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引用次数: 0

Abstract

Early-stage plant stress detection is a key measure for sustainable agriculture management. Mineral dust as an abiotic stressor affects the physical, chemical, and physiological characteristics of plants, which are linked to the plant's visible and near-infrared (VNIR) reflectance. However, considering the intensity of plant exposure to dust and associated spectral feedback remain unclear. This study investigates the effects of dust particles on the spectral properties of 11 plant species over the growing season by conducting an in-vitro experiment based on VNIR spectroscopy. The capabilities of machine learning algorithms based on VNIR data, including partial least-squares regression (PLSR) and support vector machine (SVM), were also evaluated for dust stress detection. Analyses show that increases in dust concentration lead to (i) reduction of leaf chlorophyll and water contents; (ii) increase of spectral reflectance at 450–490, 640–660, 1370–1450, and 1820–1940 nm; (iii) decrease of spectral reflectance at 530–590, 740–1200 nm; (iv) decrease the slope and height of the red edge; (v) red absorption feature (AF) became smaller and shifted towards shorter wavelength; (vi) reduction of area, width, and depth of AFs at 400–740, 1350–1450, and 1800–1900 nm; and (vii) shift of AF position at 400–740 nm towards shorter wavelength. The results show that, PLSR estimates dust concentration with an R² ranging from 0.83 to 0.95. Additionally, the SVM successfully distinguishes between dust-exposed and non-dust-exposed samples, achieving an overall accuracy of 80–96 %. The research reveals how mineral dust affects the spectral behavior of plants, providing a basis for early-stage dust stress detection through the combination of VNIR spectroscopy and machine learning. Leveraging the research findings, transition from laboratory spectroscopy to hyperspectral remote sensing imagery enables cost-effective and extensive spatiotemporal monitoring, facilitating timely protective measures to mitigate dust-induced damage to plants.
离体植物光谱响应揭示粉尘胁迫
植物早期胁迫检测是农业可持续经营的关键措施。矿物粉尘作为一种非生物胁迫源,影响植物的物理、化学和生理特性,这些特性与植物的可见光和近红外(VNIR)反射率有关。然而,考虑到植物暴露于尘埃的强度和相关的光谱反馈仍然不清楚。本研究通过近红外光谱的体外实验,研究了尘埃颗粒对11种植物生长季节光谱特性的影响。基于VNIR数据的机器学习算法(包括偏最小二乘回归(PLSR)和支持向量机(SVM))的能力也被评估用于粉尘应力检测。分析表明,粉尘浓度的增加导致:(1)叶片叶绿素和水分含量的降低;(ii) 450 ~ 490、640 ~ 660、1370 ~ 1450和1820 ~ 1940 nm的光谱反射率增加;(iii) 530 ~ 590,740 ~ 1200nm光谱反射率降低;(iv)减小红边的坡度和高度;(v)红光吸收特征(AF)变小并向短波长偏移;(vi)减少400-740 nm、1350-1450 nm和1800-1900 nm光圈的面积、宽度和深度;(vii) 400-740 nm处自动对焦位置向短波长偏移。结果表明,PLSR估计粉尘浓度的R²范围为0.83 ~ 0.95。此外,支持向量机成功地区分了粉尘暴露和非粉尘暴露的样本,实现了80 - 96%的总体精度。该研究揭示了矿物粉尘如何影响植物的光谱行为,为通过VNIR光谱和机器学习相结合的早期粉尘胁迫检测提供了基础。利用研究成果,从实验室光谱到高光谱遥感图像的转换可以实现成本效益和广泛的时空监测,促进及时的保护措施,以减轻粉尘对植物的损害。
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来源期刊
CiteScore
10.30
自引率
9.70%
发文量
415
审稿时长
69 days
期刊介绍: Agricultural and Forest Meteorology is an international journal for the publication of original articles and reviews on the inter-relationship between meteorology, agriculture, forestry, and natural ecosystems. Emphasis is on basic and applied scientific research relevant to practical problems in the field of plant and soil sciences, ecology and biogeochemistry as affected by weather as well as climate variability and change. Theoretical models should be tested against experimental data. Articles must appeal to an international audience. Special issues devoted to single topics are also published. Typical topics include canopy micrometeorology (e.g. canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, fluxes of trace gases), micrometeorological instrumentation (e.g., sensors for trace gases, flux measurement instruments, radiation measurement techniques), aerobiology (e.g. the dispersion of pollen, spores, insects and pesticides), biometeorology (e.g. the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology), forest-fire/weather interactions, and feedbacks from vegetation to weather and the climate system.
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