Convolutional neural network for estimating soil gravimetric moisture using UAV multispectral bands
DOI:
https://doi.org/10.36825/RITI.13.32.004Keywords:
Convolutional Neural Networks, Multispectral UAV, Gravimetric Moisture, Precision Agriculture, Water ManagementAbstract
Accurate estimation of soil gravimetric moisture is essential for the efficient management of water resources in precision agriculture, particularly in semi-arid regions. This study proposes a convolutional neural network (CNN) to estimate soil gravimetric moisture using multispectral data acquired by unmanned aerial vehicles (UAVs). The research was conducted on a clay-loam soil plot in Guasave, Sinaloa, Mexico, during different post-irrigation drying phases. A DJI Mavic 3 Multispectral drone equipped with sensors in the Green (560 nm), Red (650 nm), Red-Edge (730 nm), and NIR (860 nm) bands was employed, capturing imagery with a spatial resolution of approximately 4 cm/pixel. In situ gravimetric measurements were collected to validate the performance of the developed CNN model. The proposed CNN architecture consisted of three Conv1D layers with 32 filters, one MaxPooling layer, a flattening layer, and a fully connected dense layer, specifically designed to capture complex nonlinear relationships between spectral bands and soil moisture. The model achieved outstanding performance, with a mean squared error (MSE) of 0.0058, a coefficient of determination (R²) of 0.91, and a mean absolute error (MAE) of 0.046 on the test dataset. Predictions were highly correlated with actual measurements (r = 0.92, p < 0.001). Statistical analyses confirmed homoscedasticity and residual normality, indicating model robustness. Furthermore, the CNN-based approach outperformed traditional empirical indices, demonstrating high sensitivity to within-field microvariability.
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