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Accurately assessing the heavy-metal contamination in crops is crucial to food security. This study provides a method to distinguish heavy-metal stress levels in rice using the variations of two physiological functions as discrimination indices, which are obtained by assimilation of remotely sensed data with a crop growth model. Two stress indices, which correspond to daily total $\text{CO}_{2}$ assimilation...
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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