Current projections indicate that the world population will increase from 6.9 to 9.7 billion by 2050. Increases in the world population and living standards result in increased food demand. Under the situation of climate change and limited resources for agriculture, maintaining crop yields is becoming a challenge. Site-specific information of plant features/traits and stress is required for informed management of agricultural resources (e.g. water and nitrogen) whilst maintaining or increasing yield. The timely and precise estimation of plant traits and stress can help us to achieve these goals.
Plant trait and stress information can be detected by measurements in situ (e.g. soil moisture, leaf chlorophyll content and stomatal conductance). However, point measurements in situ are generally expensive, time consuming, and not suitable for large fields with heterogeneous conditions. The emerging unmanned aerial vehicle (UAV) remote sensing technology provides a non-contact way to monitor plants with high temporal-spatial resolution. Therefore, the use of UAV remote sensing together with big data analytics, image processing, and data modeling technologies can facilitate mapping and monitoring plant growth and development across seasons at both high spatial and temporal resolutions not previously accessible.
In this Research Topic, we are seeking original scientific contributions on characterizing plant traits and stress based on UAV remote sensing data. Contributions will cover, but are not limited to, the following topics:
- Development, integration, and testing of new and emerging sensors and UAV technologies for plant features and stress estimation;
- Plant and soil images acquired by UAV pre-processing technology for radiometric, spectral and spatial calibration, and mosaicking;
- Fusion of remote sensing data by UAV, satellite, ground vehicle, and other related data for estimating plant features and stress;
- Machine learning and AI approaches for the interpretation and analysis of UAV-based retrievals
- Plant mapping, plant features extraction, and plant stress estimation from UAV and other remote sensing data;
- UAV-based prescription map development for site-specific plant management;
- Development of plant management decision support tools based on UAV and/or other remote sensing data.
- UAV-based multispectral, hyperspectral, thermal, Lidar, and/or stereo sensing of plant traits and stress.
Current projections indicate that the world population will increase from 6.9 to 9.7 billion by 2050. Increases in the world population and living standards result in increased food demand. Under the situation of climate change and limited resources for agriculture, maintaining crop yields is becoming a challenge. Site-specific information of plant features/traits and stress is required for informed management of agricultural resources (e.g. water and nitrogen) whilst maintaining or increasing yield. The timely and precise estimation of plant traits and stress can help us to achieve these goals.
Plant trait and stress information can be detected by measurements in situ (e.g. soil moisture, leaf chlorophyll content and stomatal conductance). However, point measurements in situ are generally expensive, time consuming, and not suitable for large fields with heterogeneous conditions. The emerging unmanned aerial vehicle (UAV) remote sensing technology provides a non-contact way to monitor plants with high temporal-spatial resolution. Therefore, the use of UAV remote sensing together with big data analytics, image processing, and data modeling technologies can facilitate mapping and monitoring plant growth and development across seasons at both high spatial and temporal resolutions not previously accessible.
In this Research Topic, we are seeking original scientific contributions on characterizing plant traits and stress based on UAV remote sensing data. Contributions will cover, but are not limited to, the following topics:
- Development, integration, and testing of new and emerging sensors and UAV technologies for plant features and stress estimation;
- Plant and soil images acquired by UAV pre-processing technology for radiometric, spectral and spatial calibration, and mosaicking;
- Fusion of remote sensing data by UAV, satellite, ground vehicle, and other related data for estimating plant features and stress;
- Machine learning and AI approaches for the interpretation and analysis of UAV-based retrievals
- Plant mapping, plant features extraction, and plant stress estimation from UAV and other remote sensing data;
- UAV-based prescription map development for site-specific plant management;
- Development of plant management decision support tools based on UAV and/or other remote sensing data.
- UAV-based multispectral, hyperspectral, thermal, Lidar, and/or stereo sensing of plant traits and stress.