VOLUME 19 NUMBER 1 (January to June 2026)

A multispectral imaging framework for early-stage modelling and precision estimation of rice plant density using MSAVI-derived fractional vegetation cover

Accurate early-stage estimation of rice plant density is essential for precision crop management. However, current remote sensing methods face limitations in spatial resolution, revisit frequency, and sensitivity under sparse canopy conditions, highlighting the need for scalable, high-resolution UAV-based approaches. This study presents a UAV-based multispectral imaging framework for early-stage rice plant density estimation, proposing a scalable and cost-efficient solution for precision agriculture. Fractional vegetation cover derived from the Modified Soil Adjusted Vegetation Index (MSAVI) was used as the primary predictor variable in linear regression modelling. UAV imagery was acquired across varying flight altitudes (15–30 m) and crop growth stages (14–32 DAS). Five-fold cross-validation results shows that accuracy improved with crop development, with notable gains between 14 and 20 DAS. During the early vegetative stage, RMSE ranged from 39-41 plants/m2 and MAPE averaged ~30%, reflecting moderate predictive accuracy caused by sparse canopy cover and strong soil interference. As the crop progressed to early tillering, prediction error declined, with RMSE improving to approximately 30 plants/m2 and MAPE decreasing to about 29%. This improvement was attributed to denser canopy structure and stronger spectral separation between vegetation and background soil. Further analysis identified 18–25 DAS as the optimal developmental window for reliable plant density estimation, wherein models achieved high coefficients of determination (R² = 0.9139–0.9395) and the lowest RMSE (34 plants/m2). No significant differences were observed among flight altitudes, suggesting higher-altitude flights can maintain accuracy while improving operational efficiency and coverage.