What is he best known for?
The fields of study he is best known for:
- Ecology
- Agriculture
- Artificial intelligence
Mirco Boschetti mainly focuses on Remote sensing, Agronomy, Satellite imagery, Vegetation and Biomass.
His work deals with themes such as Image resolution, Crop, Cropping, Canopy and Normalized Difference Vegetation Index, which intersect with Remote sensing.
In general Agronomy, his work in Leaf area index and Nitrogen fertilizer is often linked to Destructive sampling and Field data linking many areas of study.
In his study, which falls under the umbrella issue of Satellite imagery, Fuzzy set is strongly linked to Synthetic aperture radar.
His Vegetation research is multidisciplinary, incorporating perspectives in Multispectral pattern recognition, Sorghum, Coefficient of determination and Chlorophyll fluorescence.
His Biomass research is multidisciplinary, relying on both Precision agriculture and Hyperspectral imaging.
His most cited work include:
- Advanced methods of plant disease detection. A review (249 citations)
- Multi-year monitoring of rice crop phenology through time series analysis of MODIS images (127 citations)
- Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry (108 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Remote sensing, Vegetation, Crop, Leaf area index and Remote sensing.
His Remote sensing research includes elements of Growing season, Normalized Difference Vegetation Index and Scale.
His studies deal with areas such as Biomass, Land cover, Hydrology, Backscatter and Thematic map as well as Vegetation.
His study in Biomass is interdisciplinary in nature, drawing from both Canopy and Rangeland.
His Crop study combines topics in areas such as Cropping, Agriculture, Crop yield and Sowing.
He combines subjects such as Mean squared error, Nitrogen fertilizer, Field experiment and Paddy field with his study of Leaf area index.
He most often published in these fields:
- Remote sensing (43.33%)
- Vegetation (20.00%)
- Crop (17.33%)
What were the highlights of his more recent work (between 2018-2021)?
- Remote sensing (43.33%)
- Crop (17.33%)
- Agricultural engineering (6.67%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Remote sensing, Crop, Agricultural engineering, Agriculture and Crop yield.
His studies link Scale with Remote sensing.
His Crop study combines topics from a wide range of disciplines, such as Growing season, Yield, Seasonality and Stage.
His Agriculture research includes themes of Biomass, Ecosystem services, Bioenergy and Sustainable development.
He interconnects Statistics and Grain quality in the investigation of issues within Crop yield.
In his research on the topic of Normalized Difference Vegetation Index, Vegetation is strongly related with RGB color model.
Between 2018 and 2021, his most popular works were:
- Remote sensing-based crop lodging assessment: Current status and perspectives (22 citations)
- A high-resolution, integrated system for rice yield forecasting at district level (21 citations)
- Multi-scale evaluation of drone-based multispectral surface reflectance and vegetation indices in operational conditions (16 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Agriculture
- Artificial intelligence
Mirco Boschetti focuses on Crop, Remote sensing, Scale, Multispectral image and Leaf area index.
His biological study spans a wide range of topics, including Agriculture, Cultivar, Phenology and Wet season.
His research in Remote sensing is mostly concerned with Remote sensing.
His Scale study incorporates themes from Normalized Difference Vegetation Index, Drone and Calibration.
His work carried out in the field of Multispectral image brings together such families of science as Paddy field, Fuzzy logic and Pattern recognition.
His Leaf area index research is multidisciplinary, incorporating elements of Sowing and Yield.
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.
