Dr. Jianliang Wang, Yangzhou University,china
Jianliang Wang, is a male Ph.D. student at the College of Agriculture, Yangzhou University, People’s Republic of China, where he specializes in smart agriculture and crop phenotype monitoring using advanced remote sensing techniques. His primary research interests include Remote Sensing, Sensor Technology, Machine Learning, Deep Learning, Image Processing, and Crop Modeling, with a particular focus on wheat. Wang has published eight papers in scientific journals, four of which he authored as the first author, contributing significantly to the field of agricultural technology. His recent works include research on assessing wheat seedling freezing injury using unmanned ground vehicles and hyperspectral imaging, as well as estimating rice leaf nitrogen content through machine learning approaches. Wang is dedicated to advancing agricultural practices through innovative technologies that enhance crop monitoring and management.
ORCID
Suitability for Best Researcher Award:
Jianliang Wang’s innovative research addresses pressing challenges in agriculture through the application of cutting-edge technology. His ability to publish significant research as a first author, coupled with his focus on practical solutions for crop management and sustainability, exemplifies his qualifications for the Best Researcher Award. His work not only contributes to academic knowledge but also has the potential to impact agricultural practices positively. Therefore, he is highly suitable for recognition as a leading researcher in the field.
Education:
- Ph.D. in Agriculture
Institution: Yangzhou University, Yangzhou, CN
Expected Graduation: 2024
Primary Research Interests:
- Remote Sensing 📡
- Sensor Technology 🔍
- Machine Learning 🤖
- Wheat Phenotyping 🌾
- Deep Learning 🧠
- Image Processing 🖼️
- Crop Modeling 🌱
Work Experience:
Achievements: 🏆
- Publications:
- Published 8 SCI papers, with 4 as the first author in reputable journals, contributing to advancements in smart agriculture and remote sensing.
Publication Top Notes:
Achieving wheat seedling freezing injury assessment during the seedling stage using Unmanned Ground Vehicle (UGV) and hyperspectral imaging technology
Estimating Rice Leaf Nitrogen Content and Field Distribution Using Machine Learning with Diverse Hyperspectral Features
Research on the estimation of wheat AGB at the entire growth stage based on improved convolutional features
Time series global sensitivity analysis of genetic parameters of CERES-maize model under water stresses at different growth stages