Detecting plant species in the field with deep learning and drone technology.
Aerial drones are providing a new source of high-resolution imagery for mapping of plant species of interest, amongst other applications. On-board detection algorithms could open the door to allow for applications in which drones can intelligently interact with their environment. However, the majority of plant detection studies have focused on detection in post-flight processed orthomosaics. Greater research into developing detection algorithms robust to real-world variations in environmental conditions is necessary, such that they are suitable for deployment in the field under variable conditions. We outline the steps necessary to develop such a system, show by example how real-world considerations can be addressed during model training and briefly illustrate the performance of our best performing model in the field when integrated with an aerial drone. Our results show that introducing variations in brightness as an additional augmentation strategy during training is beneficial when dealing with real-life data. We achieved a 27% improvement in the F1-score obtained on the unseen test set when using this approach. Further improvements to the model performance were obtained through the use of weight map-based loss, accounting for uncertainty in the annotation masks due to the indistinct nature of the edges of the target plants using weighting. This resulted in a 15% improvement in precision for the best configuration of hyper-parameters, yielding a final model with an F1-score of 83% and accuracy of 96%. Finally, results computed on the fly show that such a system is deployable in the field. This study shows that it is possible for a commercially available drone, integrated with a deep learning model, to detect invasive plants in the field and demonstrates methodology which could be applied to developing similar systems for other plant species of interest. The ability to perform detection on the fly is necessary for future applications in which intelligent interaction between a drone and its environment is required.