A5043393347- Smart Agriculture and AI
- Remote Sensing in Agriculture
- Leaf Properties and Growth Measurement
- Spectroscopy and Chemometric Analyses
- Video Coding and Compression Technologies
- Greenhouse Technology and Climate Control
- Advanced Vision and Imaging
- Advanced Image Processing Techniques
- Remote Sensing and Land Use
- Image and Video Quality Assessment
- Blood groups and transfusion
- Horticultural and Viticultural Research
- Land Use and Ecosystem Services
- Video Surveillance and Tracking Methods
- Remote Sensing and LiDAR Applications
- Platelet Disorders and Treatments
- Plant Disease Management Techniques
- Plant Water Relations and Carbon Dynamics
- Urban Heat Island Mitigation
- Strategic Planning and Analysis
- Digital Imaging for Blood Diseases
- Complement system in diseases
- Laser and Thermal Forming Techniques
- Food Supply Chain Traceability
- Mechanics and Biomechanics Studies
China Agricultural University
2011-2024
First Affiliated Hospital of Anhui Medical University
2024
Anhui Medical University
2024
Hebei University of Engineering
2024
Institute of Environment and Sustainable Development in Agriculture
2017-2023
Chinese Academy of Agricultural Sciences
2017-2023
Ministry of Agriculture and Rural Affairs
2023
Beijing Children’s Hospital
2022-2023
Capital Medical University
2017-2022
Beijing Chao-Yang Hospital
2022
Growth-related traits, such as aboveground biomass and leaf area, are critical indicators to characterize the growth of greenhouse lettuce. Currently, nondestructive methods for estimating growth-related traits subject limitations in that susceptible noise heavily rely on manually designed features. In this study, a method monitoring lettuce was proposed by using digital images convolutional neural network (CNN). Taking input, CNN model trained learn relationship between corresponding i.e.,...
Field-scale crop yield prediction is critical to site-specific field management, which has been facilitated by recent studies fusing unmanned aerial vehicles (UAVs) based multimodal data. However, these equivalently stacked data and underused canopy spatial information. In this study, imagery fusion (MIF) attention was proposed dynamically fuse UAV-based RGB, hyperspectral near-infrared (HNIR), thermal imagery. Based on the MIF attention, a novel model termed MultimodalNet for field-scale of...
Automatic and accurate estimation of disease severity is critical for management yield loss prediction. Conventional performed using images with simple backgrounds, which limited in practical applications. Thus, there an urgent need to develop a method estimating the plants based on leaf captured field conditions, very challenging since intensity sunlight constantly changing, image background complicated.This study developed image-based optimized neural network. A hybrid attention transfer...
China is one the largest maize (Zea mays L.) producer worldwide. Considering water deficit as of most important limiting factors for crop yield stability, remote sensing technology has been successfully used to monitor relations in soil–plant–atmosphere system through canopy and leaf reflectance, contributing better management under precision agriculture practices quantification dynamic traits. This research was aimed evaluate relation between content (LWC) ground-based unoccupied aerial...
Computer vision provides a real-time, non-destructive, and indirect way of horticultural crop yield estimation. Deep learning helps improve estimation accuracy. However, the accuracy current models based on RGB (red, green, blue) images does not meet standard soft sensor. Through enriching more data improving model structure convolutional neural networks (CNNs), this paper increased coefficient determination (R2) by 0.0284 decreased normalized root mean squared error (NRMSE) 0.0575. After...
Diabetes is a common disease and its early symptoms are not very noticeable, so an efficient method of prediction will help patients make self-diagnosis. However, the conventional to identify diabetes blood glucose test by doctors medical resource limited. Therefore, most cannot get diagnosis immediately. Since obvious relationship between complex, self-diagnosis results based on patients' own experience accurate. The process Machine Learning train computational algorithm for big dataset. It...
Winter wheat is a major food source for the inhabitants of North China. However, its yield affected by drought stress during growing period. Hence, it necessary to develop drought-resistant winter varieties. For breeding researchers, measurement, crucial indication, costly, labor-intensive, and time-consuming. Therefore, in order breed variety short time, field plot scale crop estimation essential. Unmanned aerial vehicles (UAVs) have developed into reliable method gathering canopy...
In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on power grid, Vehicle-to-Grid (V2G) technology has been developed. This seeks synchronize vehicles with improving stability of their connections and fostering positive energy exchanges between them. The key component for implementing V2G is bidirectional AC/DC converter. study concentrates non-isolated converter, providing a detailed analysis its two-stage operation creating mathematical model. A...
Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images. In order achieve robust segmentation, a method for processing greenhouse vegetable foliar images was proposed in this paper. The based on decision tree which constructed by two-step coarse-to-fine procedure. Firstly, coarse built CART (Classification Regression Tree) algorithm with feature subset. subset consisted color features that selected Pearson's Rank correlations....