A5067332063- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Spectroscopy and Chemometric Analyses
- Smart Agriculture and AI
- Remote Sensing and Land Use
- Land Use and Ecosystem Services
- Leaf Properties and Growth Measurement
- Potato Plant Research
- Soil Geostatistics and Mapping
- Soil Moisture and Remote Sensing
- Remote-Sensing Image Classification
- Species Distribution and Climate Change
- Climate change and permafrost
- Water Quality Monitoring and Analysis
- Plant Pathogens and Fungal Diseases
- Rangeland and Wildlife Management
- Precipitation Measurement and Analysis
- Environmental Changes in China
- Industrial Vision Systems and Defect Detection
- Advanced Measurement and Detection Methods
- Plant Disease Management Techniques
- Soil Carbon and Nitrogen Dynamics
- Soil and Land Suitability Analysis
- Bioenergy crop production and management
- Food Supply Chain Traceability
Henan Agricultural University
2020-2025
North University of China
2024
Henan Polytechnic University
2016-2024
National Engineering Research Center for Information Technology in Agriculture
2016-2023
Nanjing University
2017-2023
Cloud Computing Center
2023
Northeast Agricultural University
2023
Ministry of Agriculture and Rural Affairs
2019-2022
Beijing Normal University
2020
State Key Laboratory of Remote Sensing Science
2020
Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted an unmanned aerial vehicle (UAV). The UHD were used to calculate the height reflectance winter wheat canopies from panchromatic images. constructed several single-parameter models spectral parameters, such as specific bands,...
Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Index) eight statistical regression techniques: artificial neural network (ANN), multivariable linear (MLR), decision-tree (DT), boosted binary tree (BBRT), partial least squares (PLSR), random...
Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for assessment of crop growth, therefore agricultural management. Although improvements have been made in monitoring growth parameters using ground- satellite-based sensors, application these technologies is limited by imaging difficulties, complex data processing, low spatial resolution. Therefore, this study evaluated use hyperspectral indices, red-edge parameters, their combination to estimate map...
Obtaining crop above-ground biomass (AGB) information quickly and accurately is beneficial to farmland production management the optimization of planting patterns. Many studies have confirmed that, due canopy spectral saturation, AGB underestimated in multi-growth period crops when using only optical vegetation indices. To solve this problem, study obtains textures height directly from ultrahigh-ground-resolution (GDS) red-green-blue (RGB) images estimate potato three key growth periods....
Timely and accurate estimates of crop parameters are crucial for agriculture management. Unmanned aerial vehicles (UAVs) carrying sophisticated cameras very pertinent this work because they can obtain remote-sensing images with higher temporal, spatial, ground resolution than satellites. In study, we evaluated (i) the performance using a near-surface spectroscopy (350~2500 nm, 3 nm at 700 8.5 1400 6.5 2100 nm), UAV-mounted snapshot hyperspectral sensor (450~950 8 532 nm) high-definition...
Wheat is one of the most important staple crops globally. Timely mapping and monitoring wheat harvests are essential for efficiently scheduling large-scale harvesters, ensuring timely completion harvest, maintaining grain quality. Traditional manual survey methods obtaining harvest information neither highly accurate nor cost-effective do not meet needs agricultural management departments. This study introduces two novel indices detection: optical-band brightness index (OBHI) visible-band...
The number of wheat ears in the field is very important data for predicting crop growth and estimating yield as such receiving ever-increasing research attention. To obtain data, we propose a novel algorithm that uses computer vision to accurately recognize digital image. First, red-green-blue images acquired by manned ground vehicle are selected based on light intensity ensure this method robust with respect intensity. Next, cut target can be identified remaining parts. simple linear...