Rapid Identification and Prediction of Cadmium-Lead Cross-Stress of Different Stress Levels in Rice Canopy Based on Visible and Near-Infrared Spectroscopy
| Autor: | Siying Wang, Yiyun Chen, Teng Fei, Yizhuo Li, Yingjing Huang, Jun Li, Shuangyin Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Canopy
inorganic chemicals Coefficient of determination 010504 meteorology & atmospheric sciences heavy metal diagnosis chemistry.chemical_element Soil science cross-stress prediction of heavy metals 01 natural sciences Stress level Stress (mechanics) greenhouse experiment rice lcsh:Science 0105 earth and related environmental sciences Pollutant Cadmium fungi Visible and near infrared spectroscopy 04 agricultural and veterinary sciences Reflectivity chemistry 040103 agronomy & agriculture 0401 agriculture forestry and fisheries General Earth and Planetary Sciences Environmental science lcsh:Q |
| Zdroj: | Remote Sensing; Volume 12; Issue 3; Pages: 469 Remote Sensing, Vol 12, Iss 3, p 469 (2020) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs12030469 |
| Popis: | Accurate detection of cadmium (Cd) and lead (Pb)-induced cross-stress on crops is essential for agricultural, ecological environment, and food security. The feasibility to diagnose and predict Cd−Pb cross-stress in agricultural soil was explored by measuring the visible and near-infrared reflectance of rice leaves. In this study, two models were developed—namely a diagnostic model and a prediction model. The diagnostic model was established based on visible and near-infrared reflectance spectroscopy (VNIRS) datasets with Support Vector Machine (SVM), followed by leave-one-out cross-validation (LOOCV). A partial least-squares (PLS) regression, as the prediction model was employed to predict the foliar concentration of Cd and Pb contents. To accurately calibrate the two models, a rigorous greenhouse experiment was designed and implemented, with 4 levels of treatments on each of the Cd and Pb stress on rice. Results show that with the appropriate pre-processing, the diagnostic model can identify 79% of Cd and 85% of Pb stress of any levels. The significant bands that have been used mainly distributed between 681−776 nm and 1224−1349 nm for Cd stress and 712−784 nm for Pb stress. The prediction model can estimate Cd with coefficient of determination of 0.7, but failed to predict Pb accurately. The results illustrated the feasibility to diagnose Cd stress accurately by measuring the visible and near-infrared reflectance of rice canopy in a cross-contamination soil environment. This study serves as one step forward to heavy metal pollutant detection in a farmland environment. |
| Databáze: | OpenAIRE |
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