Information and Communication Technologies for Agriculture?Theme II: Data
Series: Springer Optimization and Its Applications; 183;
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Product details:
- Edition number 1st ed. 2022
- Publisher Springer International Publishing
- Date of Publication 18 March 2022
- Number of Volumes 1 pieces, Book
- ISBN 9783030841478
- Binding Hardback
- No. of pages288 pages
- Size 235x155 mm
- Weight 623 g
- Language English
- Illustrations XIV, 288 p. 113 illus., 89 illus. in color. Illustrations, black & white 212
Categories
Long description:
- Big data management from heterogenous sources
- Data mining within large data sets
- Data fusion and visualization
- IoT based management systems
- Data Knowledge Management for converting data into valuable information
- Metadata and data standards for expanding knowledge through different data platforms
- AI - based image processing for agricultural systems
- Data - based agricultural business
- Machine learning application in agricultural products value chain
Table of Contents:
Section 1 Data Technologies: You Got Data.... Now What: Building the Right Solution for the Problem (Jackman).- Data fusion and its applications in Agriculture (Moshou).- Machine learning technology and its current implementation in agriculture (Anagnostis).- Section 2 Applications: Application possibilities of IoT based management systems in agriculture (To?th).- Plant species detection using image processing and deep learning: A mobile-based application (Mangina).- Computer vision-based detection and tracking in the olive sorting pipeline (Gogos).- Integrating spatial with qualitative data to monitor land use intensity: evidence from arable land ? animal husbandry systems (Vasilakos).- Air drill seeder distributor head evaluation: a comparison between laboratory tests and Computational Fluid Dynamics simulations (R. Scola).- Section 3 Value Chain: Data - based agricultural business continuity management policies (Podaras).- Soybean price trend forecast using deep learning techniques based on prices and text sentiments (F. Silva).- Use of unsupervised machine learning for agricultural supply chain data labeling (F. Silva).
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