Multimodal Artificial Intelligence in Precision Agriculture

Multimodal Artificial Intelligence in Precision Agriculture portes grátis

Multimodal Artificial Intelligence in Precision Agriculture

Practices, Challenges, and Applications

Rathi, Vishwas; Biswas, Anupam; Sharma, Abhilasha; Singh, Anil; Rana, Omer

Taylor & Francis Ltd

05/2026

336

Dura

Inglês

9781041068273

Pré-lançamento - envio 15 a 20 dias após a sua edição

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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.
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smart farming technology;IoT sensor networks;machine learning agriculture;data fusion methods;livestock monitoring systems;environmental data analytics;federated learning in agriculture