Share
Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture
Chang, Jiyul ; Fuentes, Sigfredo (Author)
·
Mdpi AG
· Hardcover
Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture - Chang, Jiyul ; Fuentes, Sigfredo
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My Wishlists
Origin: U.S.A.
(Import costs included in the price)
It will be shipped from our warehouse between
Friday, July 19 and
Tuesday, August 06.
You will receive it anywhere in United Kingdom between 1 and 3 business days after shipment.
Synopsis "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 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.
