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The plant vegetation index (VI) is often analyzed using machine learning techniques. Garcia-Ruiz and colleagues utilized drone imagery to study a citrus orchard, analyzing and researching six indices for both healthy and citrus Huanglongbing (HLB)-infected trees: the normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), difference vegetation index (DVI), vegetation index number (VIN), and ratio vegetation index (RVI). By calculating these indices, they established models to relate vegetation characteristics, analyzing the relationship between crop growth height and leaf area. This allowed them to estimate above-ground biomass and to test and validate five models estimating fresh and dry above-ground biomass using cross-validation methods.

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Basics of Airborne Remote Sensing in Precision Agriculture

  • 章节

  • 发表日期：

    2025年

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  • Yubin Lan

摘要

The plant vegetation index (VI) is often analyzed using machine learning techniques. Garcia-Ruiz and colleagues utilized drone imagery to study a citrus orchard, analyzing and researching six indices for both healthy and citrus Huanglongbing (HLB)-infected trees: the normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), difference vegetation index (DVI), vegetation index number (VIN), and ratio vegetation index (RVI). By calculating these indices, they established models to relate vegetation characteristics, analyzing the relationship between crop growth height and leaf area. This allowed them to estimate above-ground biomass and to test and validate five models estimating fresh and dry above-ground biomass using cross-validation methods.