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Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture

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 resea...

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Bibliographic Details
Main Author: Chang, Jiyul
Other Authors: Fuentes, Sigfredo
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
Machine Learning
Nanosatellite
Xgb
Random Forest
Nitrogen Nutrition Diagnosis
Monsoon Crops
Unmanned Aerial Vehicle
Wheat Powdery Mildew
N/a
High Throughput Phenotyping
Biomass Estimation
Wheat Lodging
Hyperspectral
High-throughput Phenotyping
Variable Rate Application
Deep Learning
Solanum Tuberosum
Random Forests
Spectral Index
Oats
Lda
Pca
Satellite Imagery
Fractional Order Differential
Relieff
Leaf Chlorophyll Concentration
Pls
Yield Prediction
Digital Surface Model (dsm)
Optical Sensor
Remote Sensing Monitoring
Pesticide Application
Hyperspectral Imaging
Dimensionality Reduction
Meloidogyne
Vineyard
Satellite
Leaf Area Index
Randomforest
Uav
Optimal Retrieval Model
Crop Water Content
Support Vector Machine
Lightweight
Remote Sensing
Forage Dry Matter Yield
Winter Wheat
Soil Salinity Sensitive Parameter
Multispectral
Continuous Wavelet Transform
Brazilian Pasture
Normalized Difference Vegetation Index (ndvi)
Wheat
Optimal Subset Regression
Information Fusion
Medicine And Nursing / Bicssc
Uav/drone
Nitrogen Indicator
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary: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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (226 p.)
ISBN:9783036566153
9783036566146
books978-3-0365-6615-3

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