Efficiently deep learning for monitoring in the wild.

are an invasive weed which has caused serious harm to the biodiversity and stability of the ecosystem. It is very important to accurately and rapidly identifying and monitoring in the wild for managements taking the necessary strategies to control the to rapidly grow in the wild. However, current approaches mainly depend on manual identification, which result in high cost and low efficiency. Satellite and manned aircraft are feasible assisting approaches, but the quality of the images collected by them is not well since the ground sampling resolution is low and cloud exists. In this study, we present a novel identifying and monitoring framework and method for based on unmanned aerial vehicle (UAV) and artificial intelligence (AI). In the proposed framework, we low-costly collected the images with 8256 × 5504 pixels of the monitoring area by the UAV and the collected images are split into more small sub-images with 224 × 224 pixels for identifying model. For identifying , we also proposed a novel deep convolutional neural network which includes 12 layers. Finally, the can be efficiently monitored by painting the area containing . In our experiments, we collected 100 raw images and generated 288000 samples, and made comparison with LeNet, AlexNet, GoogleNet, VGG and ResNet for validating our framework and model. The experimental results show the proposed method is excellent, the accuracy is 93.00% and the time cost is 7.439 s. The proposed method can achieve to an efficient balance between high accuracy and low complexity. Our method is more suitable for the identification of in the wild than other methods.