| ISBN13: | 9781032832807 |
| ISBN10: | 10328328011 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 176 oldal |
| Méret: | 234x156 mm |
| Nyelv: | angol |
| Illusztrációk: | 46 Illustrations, black & white; 11 Halftones, black & white; 35 Line drawings, black & white; 27 Tables, black & white |
| 700 |
A mezőgazdaság általános kérdései
Növénytermesztés
Villamosmérnöki tudományok, híradástechnika, műszeripar
Energetika, energiaipar
A számítástudomány elmélete, a számítástechnika általában
Szoftverfejlesztés
Mesterséges intelligencia
Környezetmérnöki tudományok
Agráripar
A mezőgazdaság általános kérdései (karitatív célú kampány)
Növénytermesztés (karitatív célú kampány)
Villamosmérnöki tudományok, híradástechnika, műszeripar (karitatív célú kampány)
Energetika, energiaipar (karitatív célú kampány)
A számítástudomány elmélete, a számítástechnika általában (karitatív célú kampány)
Szoftverfejlesztés (karitatív célú kampány)
Mesterséges intelligencia (karitatív célú kampány)
Környezetmérnöki tudományok (karitatív célú kampány)
Agráripar (karitatív célú kampány)
Smart Agriculture
GBP 74.99
Kattintson ide a feliratkozáshoz
This book is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios.
This book, Smart Agriculture: Harnessing Machine Learning for Crop Management, is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios. It also examines the role of remote monitoring and precision agriculture, highlighting how technologies such as remote sensing and recurrent neural networks can optimize farming practices.
This book:
? Emphasizes sustainable agricultural practices and data-driven decision-making for eco-friendly farming.
? Highlights the importance of using environmentally friendly practices, and how machine learning can play a pivotal role in achieving sustainability goals.
? Discusses topics such as crop optimization, disease detection, pest control, resource management, precision agriculture, and sustainability.
? Covers predictive analytics for weather forecasting, Internet of Things applications for precision agriculture, and the role of sensors in data collection.
? Illustrates optimizing resource allocation, irrigation with artificial intelligence, and machine learning for soil health assessment.
Whether you are a researcher, a student, an agricultural professional, or a technology enthusiast, this book offers valuable insights into the transformative power of machine learning in agriculture. It invites readers to explore the potential of machine learning to transform farming practices, improve food security, and promote environmental sustainability.
1. Reviewing Detection of Plant Disease by making use of Machine Learning Mechanism. 2. Future Prospects and Challenges of Digital Transformation in Agriculture and Dairy Industries Mechanisms. 3. Innovative IoT-Driven Solutions for Real-Time Crop Health Surveillance and Precision Agriculture. 4. Optimizing Resource Allocation in Precision Agriculture through the Application of K-Means Clustering. 5. Upholding Ethical Standards in Modern Agriculture: An Examination of Privacy-Preserving Machine Learning Techniques. 6. Exploring the Effectiveness of Decision Trees for Comprehensive Detection of Crop Diseases in Agricultural Environments. 7. Integrating Deep Learning and Image Recognition in Smart Farming. 8. Exploring the Effectiveness of Decision Trees for Comprehensive Detection of Crop Diseases in Agricultural Environments. 9. Enhancing Crop Yield Prediction Accuracy through the Application of Gradient Descent Optimization Algorithms. 10. Machine Learning Models for Early Detection of Pest Infestation in Crops: A Comparative Study.
