Publication Type:Journal Article
Keywords:Asclepias syriaca, autonomous plant detection, faster region‐based convolutional neural network, Milkweed
Milkweed (Asclepias spp.) are host plants of monarch butterflies (Danaus plexippus). It is important to detect milkweed plant locations to assess the status and trends of monarch habitat in support of monarch conservation programs. In this paper, we describe autonomous detection of milkweed plants using cameras mounted to vehicles. For detection, we used both aggregated channel features (ACF) for running the detectors on embedded computing platforms with central processing unit and faster region‐based convolutional neural network (Faster R‐CNN) with a ResNet architecture‐based detector that is suitable for graphics processing unit optimized processing. The ACF‐based model produced 0.89 mean average precision (mAP) on the training dataset and 0.29 mAP on the test dataset, whereas the ResNet‐based Faster R‐CNN model provided 0.98 mAP on training and 0.44 mAP on the test dataset. The detections were used to calculate approximate densities of milkweed plants in geo‐referenced locations based on global positioning system point correspondences of recorded images. Probability‐of‐count distributions are compared for the actual milkweed plant locations near roadsides. This is one of the first examples of using automated milkweed plant detection and density mapping using a vehicle‐mounted camera.