Deep Learning based Automatic Approach using Hybrid Global and Local Activated Features towards Largescale Multiclass Pest Monitoring Monitoring pest in agriculture has been a highpriority issue all over the world. Computer vision techniques are widely utilized in practical crop pest prevention applications due to the rapid development of artificial intelligence technology. However, current deep learning image analytic approaches achieve low accuracy and poor robustness in agriculture pest monitoring task. This paper targets at this challenge by proposing a novel twostage deep learning based automatic pest monitoring system with hybrid global and local activated feature. In this approach, a Global activated Feature Pyramid Network (GaFPN) is firstly proposed for extracting highly representative features of pests over both depth and spatial position activation levels. Then, an improved Local activated Region Proposal Network (LaRPN) augmenting contextual and attentional information is represented for precisely locating pest objects. Finally, we design a fully connected neural network to estimate the severity of input image under the detected pests. The experimental results on our 88.6K images dataset (with 16 types of common pests) show that our approach outweighs the stateoftheart methods in industrial circumstances.

Fusing multiscale contextaware information representation for automatic infield pest detection and recognition Abstract   Automatic infield pest detection and recognition using mobile vision technique is a hot topic in modern intelligent agriculture, but suffers from serious challenges including complexity of wild environment, detection of tiny size pest and classification of multiple classes of pests. While recent deep learning based mobile vision techniques have shown some success in overcoming above issues, one key problem is that towards largescale multiple species of pest data, imbalanced classes significantly reduce their detection and recognition accuracy. In this paper, we propose a novel twostages mobile vision based cascading pest detection approach (DeepPest) towards largescale multiple species of pest data. This approach firstly extracts multiscale contextual information of the images as prior knowledge to build up a contextaware attention network for initial classification of pest images into crop categories. Then, a multiprojection pest detection model (MDM) is proposed and trained by croprelated pest images. The role of MDM can combine pest contextual information from lowlevel convolutional layers with these in highlevel convolutional layers for generating the superresolved feature. Finally, we utilize the attention mechanism and data augmentation to improve the effectiveness of infield pest detection. We evaluate our method on our newly established largescale dataset InField Pest in Food Crop (IPFC) and sufficient experimental results show that DeepPest proposed in this paper outperforms stateoftheart object detection methods in detecting infield pest.