What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Botany
His primary scientific interests are in Remote sensing, Canopy, Vegetation, Hyperspectral imaging and Winter wheat.
His studies in Remote sensing integrate themes in fields like Mean squared error, Correlation coefficient and Normalized Difference Vegetation Index.
His studies deal with areas such as Chlorophyll, Multispectral image, Leaf area index and Nitrogen as well as Canopy.
His research in Vegetation intersects with topics in Lake ecosystem, Growing season and Stage.
His Hyperspectral imaging research incorporates elements of In situ, Agronomy and Rust.
His study in Winter wheat is interdisciplinary in nature, drawing from both Bidirectional reflectance distribution function, Canopy reflectance and Crop.
His most cited work include:
- The genome of the pear (Pyrus bretschneideri Rehd.) (534 citations)
- Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation (388 citations)
- Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging (206 citations)
What are the main themes of his work throughout his whole career to date?
Wenjiang Huang mostly deals with Remote sensing, Hyperspectral imaging, Canopy, Winter wheat and Vegetation.
His Remote sensing research is multidisciplinary, incorporating perspectives in Mean squared error, Reflectivity, Leaf area index and Normalized Difference Vegetation Index.
Wenjiang Huang combines subjects such as Inversion and Atmospheric sciences with his study of Leaf area index.
Wenjiang Huang works mostly in the field of Hyperspectral imaging, limiting it down to topics relating to Statistics and, in certain cases, Powdery mildew, as a part of the same area of interest.
His work deals with themes such as Soil science, Chlorophyll, Water content and Nitrogen, which intersect with Canopy.
The Winter wheat study combines topics in areas such as Canopy reflectance and Vegetation Index.
He most often published in these fields:
- Remote sensing (51.59%)
- Hyperspectral imaging (33.76%)
- Canopy (23.25%)
What were the highlights of his more recent work (between 2017-2021)?
- Hyperspectral imaging (33.76%)
- Remote sensing (51.59%)
- Artificial intelligence (7.32%)
In recent papers he was focusing on the following fields of study:
Wenjiang Huang focuses on Hyperspectral imaging, Remote sensing, Artificial intelligence, Vegetation and Pattern recognition.
His work carried out in the field of Hyperspectral imaging brings together such families of science as Agronomy, Reflectivity, Rust, Mean squared error and Wavelet.
The various areas that Wenjiang Huang examines in his Remote sensing study include Winter wheat, Normalized Difference Vegetation Index and Satellite data.
He studied Winter wheat and Leaf area index that intersect with Vegetation Index.
Wenjiang Huang has researched Normalized Difference Vegetation Index in several fields, including In situ and Moderate-resolution imaging spectroradiometer.
His work in Vegetation addresses subjects such as Canopy, which are connected to disciplines such as Spectral bands and Horticulture.
Between 2017 and 2021, his most popular works were:
- Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval (48 citations)
- Monitoring Plant Diseases and Pests through Remote Sensing Technology: A Review (43 citations)
- A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2 (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Agriculture
His primary areas of investigation include Hyperspectral imaging, Remote sensing, Artificial intelligence, Random forest and Rust.
Wenjiang Huang interconnects Linear discriminant analysis and Wavelet in the investigation of issues within Hyperspectral imaging.
His biological study spans a wide range of topics, including Image resolution, Winter wheat and Mean squared error.
His Random forest study combines topics from a wide range of disciplines, such as Crop cycle and Crop.
His Rust study integrates concerns from other disciplines, such as Canopy, Identification, Texture, Vegetation and Feature extraction.
His Canopy study combines topics in areas such as Horticulture, Photochemical Reflectance Index, Disease detection, Electronic engineering and Anthesis.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
