Buch, Englisch, 500 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 875 g
Reihe: River Publishers Series in Mathematical, Statistical and Computational Modelling for Engineering
Tools and Technologies
Buch, Englisch, 500 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 875 g
Reihe: River Publishers Series in Mathematical, Statistical and Computational Modelling for Engineering
ISBN: 978-87-7004-100-3
Verlag: River Publishers
The research in this book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers' choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models are utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies.
Technical topics discussed in the book include:
- Precision agriculture
- Machine learning
- Wireless sensor networks
- IoT
- Deep learning
Zielgruppe
Postgraduate and Professional Practice & Development
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
- Naturwissenschaften Physik Mechanik Energie
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
- Naturwissenschaften Chemie Chemie Allgemein Chemometrik, Chemoinformatik
Weitere Infos & Material
1. Use of CNNs and their Frameworks for the Detection of Fungal Herb Disease 2. Technologies based on the IoT and Artificial and Natural Intelligence for Sustainable Agriculture 3. IoT for Smart Farming Technology: Practices, Methods and Future 4. Integrating Artificial Intelligence into Pest Management 5. Practices of Deep Learning in Farming: What Deep Learning Can Do in Intelligent Agriculture 6. Building a Solar-powered Greenhouse Having SMS and a Web Information Framework 7. Agriculture using Digital Technologies 8. Agriculture Digitization: Perspectives on the Networked World 9. Cucumber in PH Disease Monitoring Using an IoT-Based Mobile App 10. New Technologies for Sustainable Agriculture 11. Agriculture Automation 12. Food 4.0: A Survey 13. Crop Monitoring in Real Time in Agriculture 14. Smart Farming Utilizing Wireless Sensor Network and Internet of Things 15. Intelligent Agriculture Using Autonomous UAVs 16. Agriculture using Smart Sensors 17. Technologies that Work Together for Precision Agriculture 18. Utilizing Smart Farming Methods to Reduce Water Scarcity 19. Real-time Irrigation Optimization for Horticulture Crops Using WSN, APSim, and Communication Models 20. Greenhouse Gas Discharges from Farming Modeled Mathematically for Various End Users