Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

Abstract

Background: Convolvulus sepium (hedge bindweed) detection in sugar beet felds remains a challenging problem due to variation in appearance of plants, illumination changes, foliage occlusions, and diferent growth stages under field conditions. Current approaches for weed and crop recognition, segmentation and detection rely predominantly on conventional machine-learning techniques that require a large set of hand-crafted features for modelling. These might fail to generalize over diferent felds and environments.Results: Here, we present an approach that develops a deep convolutional neural network (CNN) based on the tiny YOLOv3 architecture for C. sepium and sugar beet detection. We generated 2271 synthetic images, before combining these images with 452 feld images to train the developed model. YOLO anchor box sizes were calculated from the training dataset using a k-means clustering approach. The resulting model was tested on 100 feld images, showing that the combination of synthetic and original feld images to train the developed model could improve the mean average precision (mAP) metric from 0.751 to 0.829 compared to using collected feld images alone. We also compared the performance of the developed model with the YOLOv3 and Tiny YOLO models. The developed model achieved a better trade-of between accuracy and speed. Specifcally, the average precisions ([email protected]) of C. sepium and sugar beet were 0.761 and 0.897 respectively with 6.48 ms inference time per image (800×1200) on a NVIDIA Titan X GPU environment.Conclusion: The developed model has the potential to be deployed on an embedded mobile platform like the Jetson TX for online weed detection and management due to its high-speed inference. It is recommendable to use synthetic images and empirical feld images together in training stage to improve the performance of models.Background: Convolvulus sepium (hedge bindweed) detection in sugar beet felds remains a challenging problem due to variation in appearance of plants, illumination changes, foliage occlusions, and diferent growth stages under field conditions. Current approaches for weed and crop recognition, segmentation and detection rely predominantly on conventional machine-learning techniques that require a large set of hand-crafted features for modelling. These might fail to generalize over diferent felds and environments.Results: Here, we present an approach that develops a deep convolutional neural network (CNN) based on the tiny YOLOv3 architecture for C. sepium and sugar beet detection. We generated 2271 synthetic images, before combining these images with 452 feld images to train the developed model. YOLO anchor box sizes were calculated from the training dataset using a k-means clustering approach. The resulting model was tested on 100 feld images, showing that the combination of synthetic and original feld images to train the developed model could improve the mean average precision (mAP) metric from 0.751 to 0.829 compared to using collected feld images alone. We also compared the performance of the developed model with the YOLOv3 and Tiny YOLO models. The developed model achieved a better trade-of between accuracy and speed. Specifcally, the average precisions ([email protected]) of C. sepium and sugar beet were 0.761 and 0.897 respectively with 6.48 ms inference time per image (800×1200) on a NVIDIA Titan X GPU environment.Conclusion: The developed model has the potential to be deployed on an embedded mobile platform like the Jetson TX for online weed detection and management due to its high-speed inference. It is recommendable to use synthetic images and empirical feld images together in training stage to improve the performance of models.A