Phenotyping with light interception
This project was funded by National Natural Science Foundation of China from 2018 to 2021.
- A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images
- Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops
- Integrating Crop Growth Models with Remote Sensing for Predicting Biomass Yield of Sorghum
- ModelingGlobal Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods
- Extraction of phenotypic traits using unmanned aerial vehicle for field experiments in the agronomy and crop breeding