Phenological stage and vegetation index for predicting corn yield under rainfed environments
نویسندگان
چکیده
Uncrewed aerial systems (UASs) provide high temporal and spatial resolution information for crop health monitoring informed management decisions to improve yields. However, traditional in-season yield prediction methodologies are often inconsistent inaccurate due variations in soil types environmental factors. This study aimed identify the best phenological stage vegetation index (VI) estimating corn under rainfed conditions. Multispectral images were collected over three years (2020-2022) during growing season fifty VIs analyzed. In three-year period, thirty-one exhibited significant correlations (r ≥ 0.7) with yield. Sixteen significantly correlated at least two years, five had a correlation all years. A strong was achieved by combining red, red edge, near infrared-based indices. Further, combined random forest an alyses between led identification of consistent highest predictive power prediction. Among them, leaf chlorophyll index, Medium Resolution Imaging Spectrometer (MERIS) terrestrial modified normalized difference 705 most predictors when recorded around reproductive (R1). demonstrated dynamic nature canopy reflectance importance considering growth stages, conditions accurate
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ژورنال
عنوان ژورنال: Frontiers in Plant Science
سال: 2023
ISSN: ['1664-462X']
DOI: https://doi.org/10.3389/fpls.2023.1168732