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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Publication Details

Journal
Smart Agricultural Technology
Published
2024-09-08
DOI
https://doi.org/10.1016/j.atech.2024.100570
Citations
3
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
1.38

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article

Assessing plant pigmentation impacts: A novel approach integrating UAV and multispectral data to analyze atrazine metabolite effects from soil contamination

A. Hamilton, Simon Gibson-Poole, Patchimaporn Udomkun, Thirasant Boonupara et al.
3 citations
Smart Agricultural Technology
Remote Sensing in Agriculture
1.38
article

Assessing plant pigmentation impacts: A novel approach integrating UAV and multispectral data to analyze atrazine metabolite effects from soil contamination

A. Hamilton, Simon Gibson-Poole, Patchimaporn Udomkun, Thirasant Boonupara, Puangrat Kaewlom
article en
3 citations

Abstract

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.

Smart Agricultural TechnologyVol. 9
Chiang Mai University
Zero hunger
Openalex Percentile: Top 18%
Remote Sensing in Agriculture
1.38
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