Calibrated Weed Mapping

In the agricultural domain, semantic segmentation models are increasingly used to detect the presence of weeds, thereby enhancing the efficiency of field weeding operations. However, much of the existing research focuses primarily on maximizing predictive accuracy, often overlooking the calibration of model outputs—the alignment between predicted confidence scores and the actual likelihood of correctness. Poor calibration can lead to suboptimal decision-making, resulting in the inefficient use of resources, including excessive herbicide application and increased environmental impact. To address this limitation, we investigate the application of two post-hoc calibration techniques across multiple configurations of two state-of-the-art lightweight segmentation models. The results demonstrate that model calibration enhances the reliability of predictions and provides a viable and effective strategy for improving the practical utility of semantic segmentation in precision agriculture. Code is available at https://github.com/pasqualedem/CalibratedWeedMapping.