Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the still-image detector for detection, and finally synthesized the frames into video. In the still-image detector, we used faster-RCNN as the framework. We used image-training models to detect relatively blurry videos. Additionally, a set of video-based evaluation metrics based on a machine learning classifier was proposed, which reflected the quality of video detection effectively in the experiments. Experiments showed that our system with the custom backbone was more suitable for detection of the untrained rice videos than VGG16, ResNet-50, ResNet-101 backbone system and YOLOv3 with our experimental environment.

The recent explosion of large volume of standard dataset of annotated images has offered promising opportunities for deep learning techniques in effective and efficient object detection applications. However, due to a huge difference of quality between these standardized dataset and practical raw data, it is still a critical problem on how to maximize utilization of deep learning techniques in practical agriculture applications. Here, we introduce a domain-specific benchmark dataset, called AgriPest, in tiny wild pest recognition and detection, providing the researchers and communities with a standard large-scale dataset of practically wild pest images and annotations, as well as evaluation procedures. During the past seven years, AgriPest captures 49.7K images of four crops containing 14 species of pests by our designed image collection equipment in the field environment. All of the images are manually annotated by agricultural experts with up to 264.7K bounding boxes of locating pests. This paper also offers a detailed analysis of AgriPest where the validation set is split into four types of scenes that are common in practical pest monitoring applications. We explore and evaluate the performance of state-of-the-art deep learning techniques over AgriPest. We believe that the scale, accuracy, and diversity of AgriPest can offer great opportunities to researchers in computer vision as well as pest monitoring applications.

In agriculture, pest always causes the major damage in fields and results in significant crop yield losses. Currently, manual pest classification and counting are very time-consuming and many subjective factors can affect the population counting accuracy. In addition, the existing pest localization and recognition methods based on Convolutional Neural Network (CNN) are not satisfactory for practical pest prevention in fields because of pests’ different scales and attitudes. In order to address these problems, an effective data augmentation strategy for CNN-based method is proposed in this paper. In training phase, we adopt data augmentation through rotating images by various degrees followed by cropping into different grids. In this way, we could obtain a large number of extra multi-scale examples that could be adopted to train a multi-scale pest detection model. In terms of test phase, we utilize the test time augmentation (TTA) strategy that separately inferences input images with various resolutions using the trained multi-scale model. Finally, we fuse these detection results from different image scales by non-maximum suppression (NMS) for the final result. Experimental results on wheat sawfly, wheat aphid, wheat mite and rice planthopper in our domain specific dataset, show that our proposed data augmentation strategy achieves the pest detection performance of 81.4 mean Average Precision (mAP), which improves 11.63%, 7.93%, 4.73% compared to three state-of-the-art approaches.

Specialized control of pests and diseases have been a high-priority issue for agriculture industry in many countries. On account of automation and cost-effectiveness, image analytic based pest recognition systems are widely utilized in practical crops prevention applications. But due to powerless handcrafted features, current image analytic approaches achieve low accuracy and poor robustness in practical large-scale multi-class pest detection and recognition. To tackle this problem, this paper proposes a novel deep learning based automatic approach using hybrid and local activated features for pest monitoring solution. In the presented method, we exploit the global information from feature maps to build our Global activated Feature Pyramid Network (GaFPN) to extract pests' highly discriminative features across various scales over both depth and position levels. It makes changes of depth or spatial sensitive features in pest images more visible during downsampling. Next, an improved pest localization module named Local activated Region Proposal Network (LaRPN) is proposed to find the precise pest objects' positions by augmenting contextualized and attentional information for feature completion and enhancement in local level. The approach is evaluated on our 7-year large-scale pest dataset containing 88.6K images (16 types of pests) with 582.1K manually labelled pest objects. The experimental results show that our solution performs over 75.03% mAP in industrial circumstances, which outweighs two other state-of-the-art methods: Faster R-CNN with mAP up to 70% and FPN mAP up to 72%. Our code and dataset will be made publicly available.

The goal of pan-sharpening is to produce a high-spatial-resolution multi-spectral (HRMS) image from a low-spatial-resolution multi-spectral (LRMS) counterpart by super-resolving the LRMS one under the guidance of a texture-rich panchromatic (PAN) image. Existing research has concentrated on using spatial information to generate HRMS images, but has neglected to investigate the frequency domain, which severely restricts the performance improvement. In this work, we propose a novel pan-sharpening approach, named Multi-Scale Dual-Domain Guidance Network (MSDDN) by fully exploring and exploiting the distinguished information in both the spatial and frequency domains. Specifically, the network is inborn with multi-scale U-shape manner and composed by two core parts: a spatial guidance sub-network for fusing local spatial information and a frequency guidance sub-network for fusing global frequency domain information and encouraging dual-domain complementary learning. In this way, the model can capture multi-scale dual-domain information to help it generate high-quality pan-sharpening results. Employing the proposed model on different datasets, the quantitative and qualitative results demonstrate that our method performs appreciatively against other state-of-the-art approaches and comprises a strong generalization ability for real-world scenes. The source code is available at https://github.com/alexhe101/MSDDN.