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TRUVACE RECORD VERSION
record: TRV-2026-1188
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-26T06:53:35.125266Z
status: published
lens: g_space
sector: lifestyle
headline: Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring
dek: Real-time and accurate monitoring of heavy metal concentrations in rice is essential for ensuring food safety and supporting the safe utilization of contaminated agricultural land. Hyperspectral technology offers advantages such as rapid, nondestructive, cost-effective, and environmentally friendly monitoring. This study developed multiple models for estimating Pb content in rice leaves, compared their performance, and identified stable modeling pathways for continuous regional monitoring using hyperspectral rem…
gain_title: In contrast, the combination of Whale Optimization Algorithm (WOA) and PLS achieved the highest validation estimation accuracy (R 2 = 0.7452).
problem_title: (none)
trace_subject: (none)
gain_reading: In contrast, the combination of Whale Optimization Algorithm (WOA) and PLS achieved the highest validation estimation accuracy (R 2 = 0.7452).
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: Real-time and accurate monitoring of heavy metal concentrations in rice is essential for ensuring food safety and supporting the safe utilization of contaminated agricultural land. Hyperspectral technology offers advantages such as rapid, nondestructive, cost-effective, and environmentally friendly monitoring.

The results indicated that the combination of feature band selection algorithms and modeling methods substantially affected model performance. In contrast, the combination of Whale Optimization Algorithm (WOA) and PLS achieved the highest validation estimation accuracy (R 2 = 0.7452).
limitation: 
tag: Evidence-backed gain
key_points: Real-time and accurate monitoring of heavy metal concentrations in rice is essential for ensuring food safety and supporting the safe utilization of contaminated agricultural land. | Hyperspectral technology offers advantages such as rapid, nondestructive, cost-effective, and environmentally friendly monitoring. | This study developed multiple models for estimating Pb content in rice leaves, compared their performance, and identified stable modeling pathways for continuous regional monitoring using hyperspectral remote sensing data.
rundown: Real-time and accurate monitoring of heavy metal concentrations in rice is essential for ensuring food safety and supporting the safe utilization of contaminated agricultural land. Hyperspectral technology offers advantages such as rapid, nondestructive, cost-effective, and environmentally friendly monitoring.

This study developed multiple models for estimating Pb content in rice leaves, compared their performance, and identified stable modeling pathways for continuous regional monitoring using hyperspectral remote sensing data. A meta-analysis was conducted to assess how different spectral preprocessing methods and modeling strategies affect model performance.
sources:
- peer_reviewed | Environmental Monitoring and Assessment | https://doi.org/10.1007/s10661-026-15959-x | 2026-09-23
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