Canopy-Level Rice Yield and Yield Component Estimation Using NIR-Based Vegetation Indices

Accurately predicting rice yield and its components is crucial for optimizing agricultural practices and ensuring food security. Traditional methods of assessing crop status wwcan be time-consuming and labor-intensive. This study investigated the use of drone-based multispectral imagery and machine learning to improve the prediction of rice yield and yield components. Time-series VIs were collected from 152 rice samples across various nitrogen treatments, transplanting times, and rice varieties in 2023 and 2024, using an UAV at approximately 3-day intervals. A four-parameter log-normal model was applied to analyze the VI curves, effectively quantifying the maximum value, spread, and baseline of each index, revealing the dynamic influence of nitrogen and transplanting timing on crop growth. Machine learning regression models were then used to predict yield and yield components using the log-normal parameters and individual VIs as input. Results showed that the maximum ( a ) and variance ( c ) parameters of the log-normal model, derived from the VI curves, were strongly correlated with yield, grain number, and panicle number, emphasizing the importance of mid-to-late growth stages. Among the tested VIs, NDRE, LCI, and NDVI demonstrated the highest accuracy in predicting yield and key yield components. This study demonstrates that integrating log-normal modeling of time-series multispectral data with machine learning provides a powerful and efficient approach for precision agriculture, enabling more accurate and timely assessments of rice yield and its contributing factors.