A5100434309- Remote Sensing in Agriculture
- Leaf Properties and Growth Measurement
- Climate change impacts on agriculture
- Rice Cultivation and Yield Improvement
- Crop Yield and Soil Fertility
- Plant Water Relations and Carbon Dynamics
- Remote Sensing and LiDAR Applications
- Remote Sensing and Land Use
- Greenhouse Technology and Climate Control
- Smart Agriculture and AI
- Land Use and Ecosystem Services
- Spectroscopy and Chemometric Analyses
- Soil and Unsaturated Flow
- Plant responses to elevated CO2
- Soil Carbon and Nitrogen Dynamics
- Soil Moisture and Remote Sensing
- Environmental and Agricultural Sciences
- Hydrology and Watershed Management Studies
- Soil Geostatistics and Mapping
- Irrigation Practices and Water Management
- Groundwater flow and contamination studies
- Civil and Geotechnical Engineering Research
- Wheat and Barley Genetics and Pathology
- Dam Engineering and Safety
- Geomechanics and Mining Engineering
Nanjing Agricultural University
2016-2025
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
2015-2025
Peking University People's Hospital
2023-2025
Wuhan University
2016-2025
Yangzhou Polytechnic Institute
2022-2025
National Engineering Research Center for Information Technology in Agriculture
2016-2024
Ministry of Agriculture and Rural Affairs
2018-2024
Affiliated Hospital of Jiangsu University
2013-2024
Nantong University
2022-2024
Université Lille Nord de France
2024
Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact global temperature increase on production these crops is therefore critical to maintaining food supply, but different studies have yielded results. Here, we investigated impacts yields four by compiling extensive published results from analytical methods: grid-based local point-based models, statistical regressions, field-warming experiments. Results methods consistently showed negative crop...
Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble simulate global yield changing climate. Potential benefits elevated atmospheric CO2 by 2050 on are likely be negated impacts from rising temperature changes rainfall, but with considerable disparities between regions. Grain yields expected lower more variable most low-rainfall regions,...
Abstract Predicting rice ( Oryza sativa ) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used yield prediction, but uncertainties associated remain largely unquantified. We evaluated 13 against multi‐year experimental data at four sites diverse climatic conditions Asia and examined whether different modeling approaches on major physiological processes attribute to the of prediction...
Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring growth is instructive for the diagnosis prediction grain yield. Unmanned aerial vehicle (UAV)-based remote sensing widely used in precision agriculture due to its unique advantages flexibility resolution. This study was carried out on wheat trials treated with different nitrogen levels seeding densities three regions Jiangsu Province 2018–2019. Canopy spectral images...
Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances unmanned aerial vehicle (UAV) sensor technologies, UAV-based remote sensing emerging as promising solution for monitoring crop LAI with great flexibility applicability. This study aimed determine the feasibility combining color texture information derived from digital images estimating rice (Oryza sativa L.). Rice field trials were conducted at two sites using...
Aboveground biomass (AGB) is a widely used agronomic parameter for characterizing crop growth status and predicting grain yield. The rapid accurate estimation of AGB in non-destructive way useful making informed decisions on precision management. Previous studies have investigated vegetation indices (VIs) canopy height metrics derived from Unmanned Aerial Vehicle (UAV) data to estimate the various crops. However, input variables were either one type or different sensors board UAVs. Whether...
Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS N status at individual stages their combination field data collected from a two-year experiment. The experiments were conducted 2015 2016,...
Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause accuracy to decrease for This paper proposes an YOLOv5 (You Look Only Once)-based method detect accurately in solve error miss caused by occlusion conditions. The proposed introduces data cleaning augmentation improve generalization ability network. network is rebuilt adding a microscale...
The Soil Plant Analysis Development (SPAD) chlorophyll meter is one of the most commonly used diagnostic tools to measure crop nitrogen status. However, measurement method could significantly affect accuracy final estimation. Thus, this research was undertaken develop a new methodology optimize SPAD measurements in rice (Oryza sativa L.). A flatbed color scanner map dynamic distribution and irregular leaf shapes. Calculus algorithm adopted estimate potential positions for along blade. Data...
Nondestructive monitoring and diagnosis of plant N status is necessary for precision management. The present study was conducted to determine if canopy reflectance could be used evaluate leaf in rice ( Oryza sativa L.). Ground‐based spectral concentration accumulation leaves were measured over the entire growing season under various treatments fertilization, irrigation, population. Analyses made on relationships seasonal reflectance, ratio indices, normalized difference indices different...