Estimation of potato canopy leaf water content in various growth stages using UAV hyperspectral remote sensing and machine learning
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View abstract on PubMed
Summary
This summary is machine-generated.This study uses UAV hyperspectral remote sensing to accurately monitor potato leaf water content (LWC) for efficient irrigation, crucial for food security amid water shortages. Optimal models were developed for different growth stages, enabling precise water management.
Area Of Science
- Agricultural Science
- Remote Sensing Technology
- Plant Physiology
Background
- Water scarcity necessitates efficient agricultural irrigation for food security.
- Accurate monitoring of crop water status is vital for sustainable agriculture.
- UAV hyperspectral remote sensing shows promise for large-scale crop water content monitoring.
Purpose Of The Study
- To develop and validate models for estimating potato leaf water content (LWC) using UAV hyperspectral data.
- To identify optimal spectral bands and modeling techniques for LWC estimation across different potato growth stages.
- To provide insights for precise potato irrigation strategies.
Main Methods
- Collected hyperspectral and LWC data for potatoes (Solanum tuberosum) during tuber formation, growth, and starch accumulation.
- Applied mathematical transformations (MSC, SNV) to hyperspectral data.
- Selected feature spectral bands using CARS and RF.
- Developed LWC estimation models using PLSR, SVR, and BP with full and feature bands.
Main Results
- MSC and SNV transformations improved spectral data-LWC correlation.
- Optimal LWC estimation models varied by growth stage: MSC-CARS-SVR (tuber formation), SNV-CARS-PLSR (tuber growth), MSC-RF-PLSR (starch accumulation).
- All optimal models demonstrated excellent predictive performance (RPD > 2).
Conclusions
- UAV hyperspectral remote sensing, combined with optimized data processing and modeling, provides accurate LWC estimation for potatoes.
- The developed models enable precise monitoring of potato water status throughout key growth phases.
- This technology supports data-driven decisions for water-saving irrigation and sustainable potato production.
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