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Heavy metal stress in crops is characterized by stability in space and time, which differs from other stressors that are typically more transient (e.g., drought, pests/diseases, and mismanagement). The objective of this study is to assess regional heavy metal stress in rice by integrating physiological function variability with spatio-temporal stability based on multi-temporal thermal infrared (TIR)...
Heavy metal contamination in crops is a worldwide problem that requires accurate and timely monitoring. This study is aimed at improving the accuracy of monitoring heavy metal stress levels in rice utilizing remote sensing data. An assimilation framework based on remote sensing and improved crop growth model was developed to continuously monitor heavy metal stress levels over the entire period of...
In plants, chlorophyll content (CC) is an important indicator of photosynthetic activity, stress, and nutritional status. In this study, hyperspectral indices were used to create a neural network model to detect subtle variations in leaf chlorophyll content of rice under heavy metal stress. We selected four experiment rice farmlands with different levels of heavy metal contamination. The four sites...
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