A5083396853- Remote Sensing in Agriculture
- Spectroscopy and Chemometric Analyses
- Remote Sensing and Land Use
- Advanced Chemical Sensor Technologies
- Leaf Properties and Growth Measurement
- Tea Polyphenols and Effects
- Remote Sensing and LiDAR Applications
- Soil Geostatistics and Mapping
- Plant Water Relations and Carbon Dynamics
- Land Use and Ecosystem Services
- Fermentation and Sensory Analysis
- Meat and Animal Product Quality
- Air Quality and Health Impacts
- Nutritional Studies and Diet
- Smart Agriculture and AI
- Water Quality Monitoring and Analysis
- Spectroscopy Techniques in Biomedical and Chemical Research
- Spatial and Panel Data Analysis
- Forest ecology and management
National Engineering Research Center for Information Technology in Agriculture
2019-2025
Ministry of Agriculture and Rural Affairs
2019-2024
Rural Development Institute
2021-2023
Beijing Automation Control Equipment Institute
2020
Shanxi Agricultural University
2019
The Leaf Area Index (LAI) is an essential parameter that affects the exchange of energy and materials between vegetative canopy surrounding environment. Estimating LAI using machine learning models with remote sensing data has become a prevalent method for large-scale estimation. However, existing have exhibited various flaws, hindering accurate estimation LAI. Thus, new Dendrocalamus giganteus was proposed, which integrates ICESat-2/ATLAS, Sentinel-1/-2 data, refines through application...
The use of remote sensing to monitor nitrogen (N) in crops is important for obtaining both economic benefit and ecological value because it helps improve the efficiency fertilization reduces environmental burden. In this study, we model total leaf N concentration (TLNC) winter wheat constructed from hyperspectral data by considering vertical distribution (VND). field acquired during 2013–2014 growing season were used construct validate model. results show that: (1) law LNC was distinct,...
Hitherto, the intelligent detection of black tea fermentation quality is still a thought-provoking problem because one-side sample information and poor model performance. This study proposed novel method for prediction major chemical components including total catechins, soluble sugar caffeine using hyperspectral imaging technology electrical properties. The multielement fusion were used to establish quantitative models. performance was better than that single information. Subsequently,...
The spectrophotometer method is costly, time-consuming, laborious, and destructive to the plant. Samples will be lost during transportation process, can only obtain sample point data. This poses a challenge estimation of chlorophyll content at regional level. In this study, in order improve accuracy, new collaborative inversion using Landsat 8 Global Ecosystem Dynamics Investigation (GEDI) proposed. Specifically, data set combined with preprocessed two remote-sensing (RS) factors construct...
Recognizing and identifying tea plant (Camellia sinensis) cultivar plays a significant role in planting germplasm resource management, particularly for oolong tea. There is wide range of high-quality with diverse varieties plants that are suitable production. The conventional method confirming cultivars involves visual assessment. Machine learning computer vision-based automatic classification methods offer efficient non-invasive alternatives rapid categorization. Despite advancements...
Estimation of forest biomass at regional scale based on GEDI spaceborne LiDAR data is great significance for quality assessment and carbon cycle. To solve the problem discontinuous footprints, this study mapped different echo indexes in footprints to surface by inverse distance weighted interpolation method, verified influence number results. Random algorithm was chosen estimate spruce-fir combined with parameters provided 138 sample plots Shangri-La. The results show that: (1) By extracting...
The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued both producers and consumers. Rapid, nondestructive, low-cost monitoring methods for quality could improve economic benefits associated with tea. This research explored possibility leaf from multi-spectral images. Threshold segmentation manual sampling were used to eliminate image background, after which spectral features constructed. Based on this, texture images canopy extracted. Three machine learning...
Bamboo forests, as some of the integral components forest ecosystems, have emerged focal points in forestry research due to their rapid growth and substantial carbon sequestration capacities. In this paper, satellite-borne lidar data from GEDI ICESat-2/ATLAS are utilized main information sources, with Landsat 9 DEM covariates, combined 51 pieces ground-measured data. Using random regression (RFR), boosted tree (BRT), k-nearest neighbor (KNN), Cubist, extreme gradient boosting (XGBoost),...
Chlorophyll and nitrogen contents were used as leaf physiological parameters. Based on multispectral images from multiple detection angles the stoichiometric data of tea (Camellia sinensis) leaves in different positions plants, spatial differences parameters explored, full channel difference vegetation index was established to effectively remove soil shadow noise. Support vector machine, random forest (RF), partial least square, back-propagation algorithms canopy scales then train parameter...
A critical nitrogen (N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> ) concentration, defined as the minimum (N) concentration required for maximum plant growth, could be used an intermediate variable between remote sensing data and recommendation N fertilizer. In this study, dilution curve was established based on aboveground biomass (AGB) from at jointing, booting, anthesis filling stages. The quantitative correlations normalized...
The Leaf Area Index (LAI) plays a crucial role in assessing the health of forest ecosystems. This study utilized ICESat-2/ATLAS as primary information source, integrating 51 measured sample datasets, and employed Sequential Gaussian Conditional Simulation (SGCS) method to derive surface grid for area. backscattering coefficient texture feature factor from Sentinel-1, well spectral band vegetation index factors Sentinel-2, were integrated. random (RF), gradient-boosted regression tree (GBRT)...
Chlorophyll content is a vital indicator for evaluating vegetation health and estimating productivity. This study addresses the issue of Global Ecosystem Dynamics Investigation (GEDI) data discreteness explores its potential in chlorophyll content. used empirical Bayesian Kriging regression prediction (EBKRP) method to obtain continuous distribution GEDI spot parameters an unknown space. Initially, 52 measured sample were employed screen modeling with Pearson RF methods. Next, optimization...