评估植物色素沉着的影响:综合无人机和多光谱数据分析土壤污染对莠去津代谢物影响的新方法

IF 6.3 Q1 AGRICULTURAL ENGINEERING Smart agricultural technology Pub Date : 2024-09-08 DOI:10.1016/j.atech.2024.100570
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引用次数: 0

摘要

本研究的目的是评估阿特拉津的亲水代谢物脱乙基阿特拉津(DEA)的含量及其对植物健康的影响。具体方法是利用无人飞行器 (UAV) 拍摄的多光谱图像,结合地面测量数据,评估农业土壤中施用阿特拉津后 Green Cos 莴苣的光合色素水平。发现莴苣中的 DEA 水平与叶绿素 a、叶绿素 b 和花青素水平之间存在很强的相关性(R² >0.70),而与类胡萝卜素水平的相关性较弱(R² = 0.55)。对色素的干扰会影响光合作用,可能会阻碍植物的生长和发育,最终导致减产。花青素反射指数(ARI)与 DEA 呈稳健的正相关,而归一化差异红边(NDRE)、叶绿素指数(LCI)和归一化差异植被指数(NDVI)则呈明显的负相关。不管有没有归一化植被指数,将 ARI、LCI 和 NDRE 结合在一起,都能最准确地预测 DEA 水平,R² 超过 0.96。NDRE 是预测叶绿素 a 和叶绿素 b 水平最有效的指数。修正的叶绿素吸收反射指数(MCARI)对类胡萝卜素的拟合效果最好,而 ARI 在描述花青素的实际测量值方面表现出色(R² = 0.90)。通过选择有效的单一变量而建立的最佳 VI 模型与实际色素测量值的拟合度最高(R² > 0.83)。这些发现强调了无人机衍生多光谱图像在评估 DEA 水平和改善环境监测方面的作用,有助于更好地规划农业和环境修复,从而增强生态系统的健康和恢复能力。
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Assessing plant pigmentation impacts: A novel approach integrating UAV and multispectral data to analyze atrazine metabolite effects from soil contamination

The objective of this study was to evaluate the levels of desethylatrazine (DEA), a hydrophilic metabolite of atrazine, and its impact on plant health. This was achieved by utilizing multispectral imagery captured by Unmanned Aerial Vehicles (UAVs) in combination with ground-measured data to assess photosynthetic pigment levels in Green Cos lettuce following atrazine application in agricultural soil. Strong correlations were found between DEA levels and chlorophyll a, chlorophyll b, and anthocyanin levels in lettuce (R² > 0.70), while the correlation with carotenoid levels was weaker (R² = 0.55). This disruption to the pigments could interfere with photosynthesis, potentially hindering the plant's growth and development, and ultimately leading to a reduction in yield. The Anthocyanin Reflectance Index (ARI) demonstrated a robust positive correlation with DEA, whereas the Normalized Difference Red Edge (NDRE), Leaf Chlorophyll Index (LCI), and Normalized Difference Vegetation Index (NDVI) displayed pronounced negative correlations. Incorporating ARI, LCI, and NDRE, with or without NDVI, provided the most accurate prediction of DEA levels, with an R² exceeding 0.96. NDRE emerged as the most efficient index for forecasting chlorophyll a and chlorophyll b levels. Modified Chlorophyll Absorption in Reflectance Index (MCARI) demonstrated the best fit for carotenoids, while ARI performed exceptionally well in describing actual measurements of anthocyanins (R² = 0.90). The best-performing VI models, developed from the selection of effective single variables, exhibited the best fit to actual pigment measurements (R² > 0.83). These findings underscore the role of UAV-derived multispectral imagery in assessing DEA levels and improving environmental monitoring, aiding in better planning for agriculture and environmental remediation to enhance ecosystem health and resilience.

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