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Artificial Intelligence for Precision Agriculture [Kõva köide]

Edited by , Edited by , Edited by (IBM Pvt.Ltd., India)
  • Formaat: Hardback, 307 pages, kõrgus x laius: 234x156 mm, kaal: 453 g, 114 Line drawings, black and white; 114 Illustrations, black and white
  • Ilmumisaeg: 19-Dec-2024
  • Kirjastus: Auerbach
  • ISBN-10: 1032462345
  • ISBN-13: 9781032462349
Teised raamatud teemal:
Artificial Intelligence for Precision AgricultureSuurem pilt
  • Formaat: Hardback, 307 pages, kõrgus x laius: 234x156 mm, kaal: 453 g, 114 Line drawings, black and white; 114 Illustrations, black and white
  • Ilmumisaeg: 19-Dec-2024
  • Kirjastus: Auerbach
  • ISBN-10: 1032462345
  • ISBN-13: 9781032462349
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032462349.html
Märksõnad:

Precision agriculture is a next-generation farming management concept that optimizes resource use, productivity, quality, profitability, and sustainability by observing and responding to crop variability. Precision agriculture employs such digital technologies as IoT, AI, 5G communication, cybersecurity, edge computing, cloud-native principles, and blockchain to ensure crops and soil receive exactly what they need for optimal health and productivity.

Artificial Intelligence for Precision Agriculture the latest developments in precision agriculture, detailing how AI contributes to its goals. The book discusses how precision agriculture solutions use IoT devices, data storage, AI analytics, connectivity, and cloud infrastructures to analyze such factors as soil type, terrain, weather, plant growth, and yield data. Also examined are edge technologies—sensors, microchips, beacons, RFID tags, robots, drones, and actuators— that collect field data and transmit it to cloud-based AI platforms for analysis. The book shows how AI-driven insights guide actions in the field, such as crop rotation, optimal planting and harvesting times, and soil management, as well as help farmers apply the right amounts of water, fertilizers, and pesticides, reducing waste and environmental impact. Applications covered in the book include:

  • Drone-based high-resolution field mapping,
  • Tracking crops,
  • Crop yield assessments,
  • Data collection for irrigation, fertilization, and crop management,
  • Advanced weather monitoring
  • Equipment management.

With chapters on AI model development, plant disease detection and remediation, sustainable farming techniques, data integration, AI-enabled data analytics, and knowledge visualization, this book is a comprehensive guide to technologies and applications in precision agriculture.



The book articulates how AI can be channeled to make a perceptible impact on agriculture through digital transformation. Use cases illustrate how AI can simplify and speed this agricultural transformation. Focusing on the distinct characteristics of AI, the book explains how to intelligently automate agriculture processes and practices.

1. Machine Learning Algorithms and Applications
2. Introduction to Deep Learning
3. Role of AI in the Field of Precision Agriculture
4. How AI Contributes to Precision Agriculture
5. AI Model Generation Platforms
6. AI Deployment Models in Precision Agriculture
7. AI Model Deployment: Enhanced Rice Plant Disease Detection and Classification as well as Improved Spatial Pyramid with Enhanced YOLO v.7
8. Sustainable Farming through AI-Enabled Precision Agriculture
9. The Significance of Data Integration and Ingestion in Enhancing Agricultural Operations: A Comprehensive Exploration
10. Revolutionizing Crop Yield Prediction in Agriculture: Harnessing Machine Learning and Environment Factors for Effective Solutions in the Face of Climate Change
11. AI in Optimization of Agricultural Output in Precision Agriculture
12. Data Analytics Methods for Precision Agriculture
13. Data Analytics Methods: A Hands-on Approach for Precision Agriculture
14. Smart Sensing Technologies and Data Analytics for Precision Agriculture
15. Knowledge Visualization Tools Relating to Agriculture

Pethuru Raj Chellah is the chief architect at Edge AI Division, Reliance Jio Platforms Ltd. (JPL), Bangalore, India.

N.Gayathri is an assistant professor in the Department of Computer Science and Engineering at GITAM University, Bengaluru, India.

G. Jaspher Willsie Kathrine, is an associate professor, and programme oordinator - CSE at the Department of Computing Science and Engineering, Kayruna Institute of Technology and Sciences, Coimbatore, India.