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Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture
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When adopting remote sensing techniques in precision agriculture, there are two main areas to consider: data acquisition and data analysis methodologies. Imagery and remote sensor data collected using different platforms provide a variety of information volumes and formats. For example, recent research in precision agriculture has used multispectral images from different platforms, such as satellites, airborne, and, most recently, drones. These images have been used for various analyses, from the detection of pests and diseases, growth, and water status of crops to yield estimations. However, accurately detecting specific biotic or abiotic stresses requires a narrow range of spectral information to be analyzed for each application. In data analysis, the volume and complexity of data formats obtained using the latest technologies in remote sensing (e.g., a cube of data for hyperspectral imagery) demands complex data processing systems and data analysis using multiple inputs to estimate specific categorical or numerical targets. New and emerging methodologies within artificial intelligence, such as machine learning and deep learning, have enabled us to deal with these increasing data volumes and the analysis complexity.
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Keywords
- biomass estimation
- Brazilian pasture
- continuous wavelet transform
- crop water content
- deep learning
- digital surface model (DSM)
- Dimensionality Reduction
- forage dry matter yield
- fractional order differential
- high throughput phenotyping
- high-throughput phenotyping
- hyperspectral
- hyperspectral imaging
- information fusion
- LDA
- leaf area index
- leaf chlorophyll concentration
- lightweight
- Machine learning
- medicine
- Meloidogyne
- monsoon crops
- multispectral
- n/a
- nanosatellite
- nitrogen indicator
- nitrogen nutrition diagnosis
- normalized difference vegetation index (NDVI)
- oats
- optical sensor
- optimal retrieval model
- optimal subset regression
- PCA
- pesticide application
- PLS
- random forest
- random forests
- RandomForest
- ReliefF
- Remote sensing
- remote sensing monitoring
- satellite
- Satellite Imagery
- soil salinity sensitive parameter
- Solanum tuberosum
- spectral index
- support vector machine
- UAV
- UAV/drone
- unmanned aerial vehicle
- variable rate application
- vineyard
- wheat
- wheat lodging
- wheat powdery mildew
- winter wheat
- XGB
- yield prediction
Links
DOI: 10.3390/books978-3-0365-6615-3Editions