A5002318429- Smart Agriculture and AI
- Advanced Neural Network Applications
- Plant Virus Research Studies
- Adversarial Robustness in Machine Learning
- Remote Sensing in Agriculture
- Photosynthetic Processes and Mechanisms
- Natural Language Processing Techniques
- Animal Virus Infections Studies
- Remote Sensing and LiDAR Applications
- Plant Pathogenic Bacteria Studies
- CCD and CMOS Imaging Sensors
- Industrial Vision Systems and Defect Detection
- Leaf Properties and Growth Measurement
- Plant Disease Management Techniques
- Mitochondrial Function and Pathology
- Remote Sensing and Land Use
- Anomaly Detection Techniques and Applications
- Spectroscopy and Chemometric Analyses
- Virology and Viral Diseases
- Advanced Measurement and Detection Methods
- Bacillus and Francisella bacterial research
- Translation Studies and Practices
- Neural Networks and Applications
- Microbial infections and disease research
- Viral gastroenteritis research and epidemiology
Guizhou University
2010-2025
Tsinghua University
2023-2024
Jilin Academy of Agricultural Sciences
2024
Xihua University
2024
Guangdong University of Technology
2022-2023
Guizhou Education University
2023
Changchun University of Science and Technology
2023
Shanghai Institute of Materia Medica
2021
Chinese Academy of Sciences
2021
Entry Exit Inspection and Quarantine Bureau
2015
Abstract The evolution of coronaviruses, such as SARS-CoV-2, makes broad-spectrum coronavirus preventional or therapeutical strategies highly sought after. Here we report a human angiotensin-converting enzyme 2 (ACE2)-targeting monoclonal antibody, 3E8, blocked the S1-subunits and pseudo-typed virus constructs from multiple coronaviruses including SARS-CoV-2 mutant variants (SARS-CoV-2-D614G, B.1.1.7, B.1.351, B.1.617.1, P.1), SARS-CoV HCoV-NL63, without markedly affecting physiological...
Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant is critical to agricultural production. Artificial intelligence technologies gradually replace traditional disease diagnosis methods due their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved detection for precision agriculture. In the meantime, most of existing usually adopt pre-trained model support diseased...
Plant disease diagnosis in time can inhibit the spread of and prevent a large-scale drop production, which benefits food production. Object detection-based plant methods have attracted widespread attention due to their accuracy classifying locating diseases. However, existing are still limited single crop diagnosis. More importantly, model has large number parameters, is not conducive deploying it agricultural mobile devices. Nonetheless, reducing parameters tends cause decrease accuracy. To...
Abstract Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most loss, which usually requires sufficient image information. Hence, being pursued as an independent discipline enable development high-throughput phenotyping disease. However, we often face challenges in sharing large-scale data due incompatibilities formats descriptions provided by different...
Wheat is the most widely grown crop in world, and its yield closely related to global food security. The number of ears important for wheat breeding estimation. Therefore, automated ear counting techniques are essential high-yield varieties increasing grain yield. However, all existing methods require position-level annotation training, implying that a large amount labor required annotation, limiting application development deep learning technology agricultural field. To address this...
Agriculture is the foundation of global food security and quality life, with staple crops such as rice, wheat, maize meeting dietary needs majority world's population. These are vulnerable to diseases that can cause severe yield losses; for instance, wheat rust disease results in annual losses exceeding \$2.9 billion. Accurate caption phenotypic characteristics plant play a crucial supportive role diagnosis, which essential ensure security. Existing methods agriculture fail adequately...
Object detection has become a crucial technology in intelligent vision systems, enabling automatic of target objects. While most detectors perform well on open datasets, they often struggle with small-scale This is due to the traditional top-down feature fusion methods that weaken semantic and location information small objects, leading poor classification performance. To address this issue, we propose novel pyramid network, adaptive learnable network (ALFPN). Our approach features an...
At present, deep neural networks have been widely used in various fields, but their vulnerability requires attention. The adversarial attack aims to mislead the model by generating imperceptible perturbations on source model, and although white-box attacks achieved good success rates, existing samples exhibit weak migration black-box case, especially some adversarially trained defense models. Previous work for gradient-based optimization either optimizes image before iteration or gradient...
Plant sensors are commonly used in agricultural production, landscaping, and other fields to monitor plant growth environmental parameters. As an important basic parameter monitoring, leaf inclination angle (LIA) not only influences light absorption pesticide loss but also contributes genetic analysis phenotypic data collection. The measurements of LIA provide a basis for crop research as well management, such water loss, absorption, illumination radiation. On the one hand, existing...
Plant diseases are a critical driver of the global food crisis. The integration advanced artificial intelligence technologies can substantially enhance plant disease diagnostics. However, current methods for early and complex detection remain challenging. Employing multimodal technologies, akin to medical diagnostics that combine diverse data types, may offer more effective solution. Presently, reliance on single-modal predominates in research, which limits scope detailed diagnosis....
Continued population growth and limited land availability will facilitate the utilization of plant regulators (PGRs) in sustainable agriculture to enhance crop yields. The PGRs industry has progressed significantly from 2003 2022, resulting a surge research activities field PGRs. However, existing studies lack exploration trends, as well challenges opportunities for innovation PGR development. Here, we analyze dynamic trends within by examining key factors such market, patent applications,...
Efficient dense reconstruction of objects or scenes has substantial practical implications, which can be applied to different 3D tasks (for example, robotics and autonomous driving). However, because the expensive hardware required overall complexity all-around scenarios, efficient using lightweight multi-view stereo methods received much attention from researchers. The technological challenge is maintaining low memory usage while rapidly reliably acquiring depth maps. Most current (MVS)...
The dense captioning task aims at detecting multiple salient regions of an image and describing them separately in natural language. Although significant advancements the field have been made, there are still some limitations to existing methods recent years. On one hand, most lack strong target detection capabilities struggle cover all relevant content when dealing with target-intensive images. other current transformer-based powerful but neglect acquisition utilization contextual...
Few-shot object detection (FSOD) aims to detect novel targets with only a few instances of the associated samples. Although combinations distillation techniques and meta-learning paradigms have been acknowledged as primary strategies for FSOD tasks, existing methods exhibit inherent biases sensitivity class variability. A critical hurdle is difficulty in ensuring appropriate knowledge learned from teacher model during fine-tuning stage. Furthermore, coarse procedures risk misalignment...
The adversarial attack is crucial to improving the robustness of deep learning models; they help improve interpretability and also increase security models in real-world applications. However, existing algorithms mainly focus on image classification tasks, lack research targeting object detection. Adversarial attacks against are global-based with no intrinsic features image. In other words, generate perturbations that cover whole image, each added perturbation quantitative undifferentiated....
Few-shot object detection (FSOD), a formidable task centered around developing inclusive models with annotated constrained samples, has attracted increasing interest in recent years. This discipline addresses unbalanced data distributions, which are particularly relevant to authentic scenarios. Although FSOD efforts have achieved considerable success terms of localization, recognition remains obstacle. stems from the fact that typical evolve general frameworks predicated on extensive...
The accurate prediction of traffic flow is paramount for the advancement intelligent transportation systems. Despite this, current models only account either temporal or spatial features in isolation, without considering their interaction, impeding model’s ability to express itself. In light we propose graph differential equations network (GDENet), an approach that can effectively mine spatiotemporal correlation. Specifically, a feature integrator (STFI), which alleviates error caused by...
Abstract To address the poor accuracy issue with tiny target recognition by UAVs, this study provides an improved YOLOv5 detection method attention mechanism. Firstly, CBAM is integrated into Backbone to suppress irrelevant features and enhance network’s space channels. This can help network learn more discriminative representations of objects in image. Then, introduction Biformer Neck removes redundant information on algorithm structure, endows dynamic query-aware sparsity, enhances its...
The post-translational import of nuclear-encoded chloroplast preproteins is critical for biogenesis, and the Toc159 family proteins receptor this process. Our previous work identified analyzed Toc GTPase in tomato; however, tomato-specific transport substrate still unknown, which limits study function TOC tomato. In study, we expand number preprotein substrates slToc159 members using slToc159-1 slToc159-2 as bait via a split-ubiquitin yeast two-hybrid membrane system. Forty-one specific were...