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Produktbild: Multimodal Artificial Intelligence in Precision Agriculture

Multimodal Artificial Intelligence in Precision Agriculture Practices, Challenges, and Applications

Aus der Reihe Multimedia and Multimodal Intelligence

252,99 €

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Beschreibung

The text presents various methods for applying machine learning and deep learning techniques to heterogeneous agricultural data and incorporating advanced Internet of Things-enabled technologies for web and mobile based applications for crop lifecycle tracking.

Beschreibung

The text presents various methods for applying machine learning and deep learning techniques to heterogeneous agricultural data and incorporating advanced Internet of Things-enabled technologies for web and mobile based applications for crop lifecycle tracking.

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

05.05.2026
Abbildungen

schwarz-weiss Illustrationen, Raster,schwarz-weiss, Zeichnungen, schwarz-weiss, Tabellen, schwarz-weiss
Herausgeber Abhilasha Sharma + weitere

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

05.05.2026
Abbildungen

schwarz-weiss Illustrationen, Raster,schwarz-weiss, Zeichnungen, schwarz-weiss, Tabellen, schwarz-weiss
Herausgeber
Verlag Taylor and Francis
Seitenzahl

340
Maße (L/B/H)

24/16,1/2,3 cm
Gewicht

625 g
Sprache

Englisch
ISBN

978-1-04-106827-3
Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Multimodal Artificial Intelligence in Precision Agriculture

  • 1. Introduction. 2. Use of Data Fusion Techniques for Optimizing Agricultural Practices. 3. Use of Multimedia Technologies/ Multimodal Intelligence for Crop Monitoring and Management. 4. Implementation of Machine Learning and Deep Learning Techniques for Disease Detection and Classification. 5. Analysis of Soil Properties using Internet of Things Sensors and Multimodal Data Analytics. 6. Employing Integrated Data to Study the Impact of Climate Change on Agriculture. 7. Using Multimodal Data to Monitor Environmental Conditions and their Effects on Crop Production. 8. Use of Historical Data and Multimedia Inputs to Model and Forecast Crop Yields. 9. Utilization of Real-time Environmental Data for Crop Harvest Forecasting and Prediction. 10. Utilizing Sensor Data for Tracking and Tracing Agricultural Products from Farm to Market. 11. Using Multimedia and Audio Sensor Data for Livestock Health Monitoring. 12. Developing Mobile and Web Applications to Provide Farmers Recommendations for Efficient Farming Practices. 13. Future Trends in Multimedia and Multimodal Intelligence for Smart Farming. 14. The Importance of Integrating Federated Learning in Contemporary Farming Practices. 15. Precision Agriculture Utilizing Emerging Edge, Cloud and Network Computing. 16. Challenges and Future Trends w.r.t. Integration of Edge and Cloud Computing in Precision Agriculture. 17. Conclusion.