Precision agriculture is used to improve site-specific agricultural decision-making based on data collection and analysis, formulation of site-specific management recommendations, and implementation of management practices to correct for factors that can limit crop growth, yield, and quality. Various approaches for the remote sensing of soil fertility, water stress, diseases and infestations, and crop growth and condition have been developed and applied for precision agricultural purposes. With developments in remote sensing technologies, the spatial and spectral resolution and return frequencies available from both satellite and other remote collection platforms have improved to the point that the promise of precision agriculture can increasingly be realized. Unmanned aerial vehicles (UAV) in particular are providing newer and deeper insights, leveraging their high resolution, sensor-carrying flexibility and dynamic acquisition schedule. This range of remote sensing platforms has been used to estimate comprehensive information related to crop health and dynamics, providing rapid retrievals of leaf area index, canopy cover, chlorophyll, nitrogen, canopy/leaf water content, canopy/leaf temperature, biomass, and yield, amongst many other variables of interest. In combination, they allow for the expansion from local to regional scales and beyond. There has never been a greater opportunity for remote sensing data to enable precision agricultural insights that can be used to better monitor, manage and respond to in-field changes that might impact crop growth, health and yield.
This Research Topic seeks to compile the latest innovative research results in the field of remote sensing-based developments and applications driving precision agricultural insights, with a specific focus on studies estimating the crop condition, growth status and yield estimation using satellite, UAV or proximal sensing platforms. The following provides a general (but not exhaustive) overview of the topics that might be relevant for this Research Topic:
• Estimation of crop health, condition and yield (or yield losses)
• Precision management, including fertilizer, pesticide, irrigation, etc.
• Detecting crop pests and diseases
• Combination of multisource/multi-sensor data to improve the retrieval of crop parameters
• Estimation of crop yield by integration of remote sensing data and crop models
• Estimating and monitoring crop status and stress
• Soil physical and chemical properties mapping
• Machine learning approaches for enhanced precision agriculture
• UAV/Satellite platforms development and application in precision agriculture
• Algorithms development and validation
• Image processing and data-fusion technology
Precision agriculture is used to improve site-specific agricultural decision-making based on data collection and analysis, formulation of site-specific management recommendations, and implementation of management practices to correct for factors that can limit crop growth, yield, and quality. Various approaches for the remote sensing of soil fertility, water stress, diseases and infestations, and crop growth and condition have been developed and applied for precision agricultural purposes. With developments in remote sensing technologies, the spatial and spectral resolution and return frequencies available from both satellite and other remote collection platforms have improved to the point that the promise of precision agriculture can increasingly be realized. Unmanned aerial vehicles (UAV) in particular are providing newer and deeper insights, leveraging their high resolution, sensor-carrying flexibility and dynamic acquisition schedule. This range of remote sensing platforms has been used to estimate comprehensive information related to crop health and dynamics, providing rapid retrievals of leaf area index, canopy cover, chlorophyll, nitrogen, canopy/leaf water content, canopy/leaf temperature, biomass, and yield, amongst many other variables of interest. In combination, they allow for the expansion from local to regional scales and beyond. There has never been a greater opportunity for remote sensing data to enable precision agricultural insights that can be used to better monitor, manage and respond to in-field changes that might impact crop growth, health and yield.
This Research Topic seeks to compile the latest innovative research results in the field of remote sensing-based developments and applications driving precision agricultural insights, with a specific focus on studies estimating the crop condition, growth status and yield estimation using satellite, UAV or proximal sensing platforms. The following provides a general (but not exhaustive) overview of the topics that might be relevant for this Research Topic:
• Estimation of crop health, condition and yield (or yield losses)
• Precision management, including fertilizer, pesticide, irrigation, etc.
• Detecting crop pests and diseases
• Combination of multisource/multi-sensor data to improve the retrieval of crop parameters
• Estimation of crop yield by integration of remote sensing data and crop models
• Estimating and monitoring crop status and stress
• Soil physical and chemical properties mapping
• Machine learning approaches for enhanced precision agriculture
• UAV/Satellite platforms development and application in precision agriculture
• Algorithms development and validation
• Image processing and data-fusion technology
