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Phenological stage and vegetation index for predicting corn yield under rainfed environments
Amrit Shrestha1, Raju Bheemanahalli2, Ardeshir Adeli3
1Department of Agricultural & Biological Engineering, Mississippi State University, Mississippi State, MS, United States.
Frontiers in Plant Science
|August 7, 2023
Summary
Accurate corn yield prediction relies on identifying optimal vegetation indices (VIs) and growth stages. Leaf chlorophyll and MERIS terrestrial chlorophyll indices around the reproductive stage (R1) show the most promise for improving crop management.
Area of Science:
- Agricultural remote sensing
- Precision agriculture
- Crop physiology
Background:
- Uncrewed aerial systems (UASs) offer high-resolution data for crop monitoring, but traditional yield prediction methods lack consistency due to environmental variability.
- Accurate in-season yield estimation is crucial for optimizing agricultural management decisions and improving crop yields.
Purpose of the Study:
- To determine the optimal phenological stage and vegetation index (VI) for accurate corn yield estimation under rainfed conditions.
- To identify the most consistent and powerful VIs for predicting corn yield using multispectral imagery.
Main Methods:
- Collected multispectral imagery over three growing seasons (2020-2022) for corn crops.
- Analyzed over fifty vegetation indices (VIs) for their correlation with final corn yield.
- Employed correlation analyses and random forest models to identify key predictive VIs and growth stages.
Main Results:
- Thirty-one VIs showed significant yield correlations (r ≥ 0.7) over the three-year study period.
- Five VIs, particularly those based on red, red edge, and near-infrared reflectance, maintained significant correlations across all three years.
- Leaf chlorophyll index, MERIS terrestrial chlorophyll index, and modified normalized difference at 705 were the most consistent predictors when measured at the reproductive stage (R1).
Conclusions:
- The study highlights the dynamic nature of canopy reflectance and its strong link to corn yield.
- Integrating specific vegetation indices with the reproductive growth stage (R1) significantly enhances corn yield prediction accuracy.
- These findings support the use of UAS-derived spectral data for more reliable in-season crop yield forecasting.
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