Estimation of water potential in corn plants using machine learning techniques with UAV imagery and evaluating the effect of flight height

The use of unmanned aerial vehicles (UAVs) in precision agriculture has proven to be a useful tool for crop monitoring. The use of this technology in irrigation water management represents a significant improvement opportunity compared to the tools commonly used. This study aimed to estimate the water content in corn plants using images captured by a drone, evaluating the effect that the flight height has on the accuracy of the estimation of this indicator. For this purpose, the water potential (WP) was measured in corn plant leaves, which allows inferring the presence of water stress and indicating the need for irrigation in the plant. Aerial images of the crop were captured under three treatments based on irrigation levels (40%, 70%, and 100% water applied compensating for evapotranspiration) to induce gradients of moisture content in the plants. Seven drone flights were carried out at different dates at 30, 50 and 70 m high. The water potential of the leaves was correlated with radiometrically calibrated multispectral images (R, G, B, red-edge, and near-infrared). Three models were developed: a multiple linear regression (LM), neural networks (NN), and a random forest (RF). The LM and NN models showed similar error metrics, with the RF model showing the best results, with an average root mean square error (RMSE) and coefficient of determination (R²) of 2.45 and 0.94, respectively. The flight height, which affected the resolution of the images, was not significant in the estimation of WP in this height range.