Aerial CIR remote sensing for weed density mapping in a soybean field

Accurate weed maps are essential for the success of site-specific herbicide application using map-based variable-rate sprayers. In this study, remotely sensed images acquired using an airborne digital color infrared (CIR) sensor were used for mapping and modeling the spatial distribution of weed infestation density within a soybean field. The effect of spatial positioning error associated with data on resolution requirements and mapping accuracy was also studied. Vegetative indices developed from the three-band CIR image showed strong correlation with spatial weed density. The best correlation was observed at the spatial resolutions of 4.5 m/pixel to 5.3 m/pixel, which was lower than the actual data resolutions. Higher modeling accuracies observed at lower resolutions were caused by the positioning error associated with both aerial imaging data and ground-truth data. At this resolution, the weed density models developed using an artificial neural network resulted in R² values of 0.87 and 0.83. This model mapped the spatial distribution of weed density with an R² value of 0.58 for a field not used in modeling.