Xiaojun Liu
Orcid: 0000-0001-7593-085XAffiliations:
- Nanjing Agricultural University, National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, China
According to our database1,
Xiaojun Liu
authored at least 18 papers
between 2008 and 2023.
Collaborative distances:
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Bibliography
2023
Optimizing rice in-season nitrogen topdressing by coupling experimental and modeling data with machine learning algorithms.
Comput. Electron. Agric., June, 2023
2022
Improving wheat yield prediction integrating proximal sensing and weather data with machine learning.
Comput. Electron. Agric., 2022
Advances in the estimations and applications of critical nitrogen dilution curve and nitrogen nutrition index of major cereal crops. A review.
Comput. Electron. Agric., 2022
2021
Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat.
Sensors, 2021
Combining texture, color, and vegetation indices from fixed-wing UAS imagery to estimate wheat growth parameters using multivariate regression methods.
Comput. Electron. Agric., 2021
2020
A Comparative Assessment of Measures of Leaf Nitrogen in Rice Using Two Leaf-Clip Meters.
Sensors, 2020
Using an Active Sensor to Develop New Critical Nitrogen Dilution Curve for Winter Wheat.
Sensors, 2020
Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China.
Remote. Sens., 2020
Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat.
Remote. Sens., 2020
Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle.
Remote. Sens., 2020
2019
Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat.
Sensors, 2019
Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages.
Remote. Sens., 2019
Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles.
Remote. Sens., 2019
Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation.
Remote. Sens., 2019
2018
Comparison RGB Digital Camera with Active Canopy Sensor Based on UAV for Rice Nitrogen Status Monitoring.
Proceedings of the 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics), 2018
2017
Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China).
Sensors, 2017
2010
Design and Realization of a VRGIS-Based Digital Agricultural Region Management System.
Proceedings of the Computer and Computing Technologies in Agriculture IV, 2010
2008
Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice.
Int. J. Appl. Earth Obs. Geoinformation, 2008