
Artificial Intelligence for Precision Agriculture
- 344 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Artificial Intelligence for Precision Agriculture
About this book
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 digital technologies such as the Internet of Things (IoT), artificial intelligence (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 explores 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 factors such as soil type, terrain, weather, plant growth, and yield data. It also examines edge technologiesāsensors, microchips, beacons, RFID tags, robots, drones, and actuatorsāthat 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, and 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.
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Information
Table of contents
- Cover Page
- Half-Title Page
- Title Page
- Copyright Page
- Contents
- Preface
- Editors
- Contributors
- 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 Besides Improved Spatial Pyramid with Enhanced YOLOv7
- 8 Sustainable Farming Through AI-Enabled Precision Agriculture
- 9 The Significance of Data Integration and Ingestion in Enhancing Agricultural Operations: A Comprehensive Exploration
- 10 Revolutionising Crop Yield Prediction in Agriculture: Harnessing Machine Learning and Environment Factors for Effective Solutions in the Face of Climate Change
- 11 AI in Optimisation 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