A5048723149- Greenhouse Technology and Climate Control
- Leaf Properties and Growth Measurement
- Smart Agriculture and AI
- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Remote Sensing and Land Use
- Genetic Mapping and Diversity in Plants and Animals
- Computer Graphics and Visualization Techniques
- Crop Yield and Soil Fertility
- Simulation and Modeling Applications
- Plant Water Relations and Carbon Dynamics
- 3D Shape Modeling and Analysis
- Spectroscopy and Chemometric Analyses
- Horticultural and Viticultural Research
- Genetics and Plant Breeding
- Genetic and phenotypic traits in livestock
- Advanced Computational Techniques and Applications
- Soil Mechanics and Vehicle Dynamics
- Genomics and Phylogenetic Studies
- Industrial Technology and Control Systems
- Alkaloids: synthesis and pharmacology
- Advanced Numerical Analysis Techniques
- Tree Root and Stability Studies
- Image Processing and 3D Reconstruction
- Biochemical and Structural Characterization
National Engineering Research Center for Information Technology in Agriculture
2013-2025
Chongqing Bureau of Geology and Minerals Exploration
2025
Beijing Academy of Agricultural and Forestry Sciences
2018-2023
Fanjingshan National Nature Reserve
2023
Beijing Haidian Hospital
2021
Digital Science (United States)
2019
Center for Information Technology
2008-2014
Accurate and high-throughput determination of plant morphological traits is essential for phenotyping studies. Nowadays, there are many approaches to acquire high-quality three-dimensional (3D) point clouds plants. However, it difficult estimate parameters accurately the whole growth stages maize plants using these 3D clouds. In this paper, an accurate skeleton extraction approach was proposed bridge gap between cloud estimation The algorithm first uses clustering color difference denoising...
Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization shoot architecture for breeding are useful to analyze morphological differences response environments crop cultivation. Accordingly, high-throughput grown field conditions urgently needed, MVS-Pheno, a portable low-cost platform plants, was developed. The is composed four major components: semiautomatic multiview stereo (MVS) image...
High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches been applied to acquire the traits. It is quite confusing for researchers determine appropriate way their application. In this study, three representative three-dimensional (3D) approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, digitizing, were evaluated maize plant multi growth stages. Phenotyping...
Plant phenotyping is essential in plant breeding and management. High-throughput data acquisition automatic phenotypes extraction are common concerns phenotyping. Despite the development of platforms realization high-throughput three-dimensional (3D) tall plants, such as maize, handling small-size plants with complex structural features remains a challenge. This study developed miniaturized shoot platform MVS-Pheno V2 focusing on low shoots. The an improvement V1 was based multi-view stereo...
The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for breeding management. However, it is difficult to align point cloud data extract accurate phenotypic traits populations. In this study, high-throughput, raw maize were collected using a rail-based platform with light detection ranging (LiDAR) an RGB (red, green, blue) camera. orthorectified images LiDAR clouds aligned via direct linear...
The morphology and structure of wheat plants are intricate, containing numerous tillers, rich details, significant cross-obscuration. Methods effectively reconstructing three-dimensional (3D) models that reflects the varietal architectural differences using measured data is challenging in plant phenomics functional–structural models. This paper proposes a 3D reconstruction technique for integrates point cloud virtual design optimization. approach extracted single stem number, growth...
Stalk lodging is an impediment to improving profitability and production efficiency in maize. Lodging resistance, a comprehensive indicator appraise genotypes, requires both characterization of mechanical properties laboratory investigation percentage field. However, situ maize resistance still remains poor. The aim this study was develop indicator, named cumulative index (CLI), based on percentages at different wind speeds for evaluating cultivars, evaluate the accuracy reliability...
Summary High‐throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual vascular bundles tedious and time‐consuming, which lags behind the development functional genomics maize. More robust automated techniques traits at high‐throughput are urgently needed large crop populations. In this study, we developed a standard process stem micro‐CT data acquisition automatic CT image pipeline to obtain bundle stems including...
Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure morphology. In studies, segmentation of individual plants to organs directly determines accuracy organ-level phenotype estimation reliability reconstruction. However, highly accurate, automatic, robust approaches are unavailable. Thus, high-throughput many shoots challenging. Although deep learning can feasibly solve this issue, software tools annotation construct training dataset...
Maize ear leaves have important roles in photosynthesis, nutrient partitioning and hormone regulation. The morphological structural variations observed maize are numerous contribute significantly to the yield. Nevertheless, research on fine-scale morphology of is less, particularly quantitative methods characterize two-dimensional (2D) space absent. This makes it challenging accurately identify 2D leaf shape their cultivars. Therefore, this study presents semantic feature extraction atlas...