Apple Academic Press
Precision Agriculture for Sustainability: Use of Smart Sensors, Actuators, and Decision Support Systems
Key Metrics
- Narendra Khatri
- Apple Academic Press
- Hardcover
- 9781774913734
- -
- -
- Technology & Engineering > Agriculture - Agronomy - Crop Science
- English
Secure TransactionBook Description
This book provides a comprehensive exploration of the aspects of the current state-of-the-art digital technological intervention for precision agriculture for sustainable agricultural development. It delves into how modern technologies--i.e., global positioning systems (GPS), unmanned aerial vehicles (drones), image processing methods, artificial intelligence, machine learning, and deep learning--are being used in agriculture to make it more farmer-friendly and more economically profitable.
The volume discusses the use of smart sensors, actuators, and decision support systems for precision agriculture that provide intelligent data about crop health and for monitoring for yield prediction, soil quality, and nutrition requirement prediction, etc., using machine learning, deep learning, and artificial intelligence through a globally connected system via the Internet of Things (IoT).
The book begins with a section on AI in agriculture that looks at using satellite data for vegetation studies, AI-based solutions to increase farmer income, satellite images for yield prediction using machine learning algorithms, and more. The second section presents robotic-based innovations in agriculture, including agricultural field robots, along with cobots (computer-controlled robotic devices designed to people) used in and outside farms and greenhouses, methods for continual robotic monitoring of crops, robot-based weed identification and control systems, and more.
The section on intelligent computing in agriculture looks at soft computing methodologies and frameworks for yield forecasting for crop production, machine learning techniques to classify and identify plant diseases, machine learning algorithms to analyze all factors affecting crop yield and the climatic effect on produce, deep convolutional neural networks (DCNNs) for recognizing nutrient deficiencies, etc. The last section explores IoT in agriculture and provides an overview of the research that has gone into making smart precision agriculture a reality, IoT applications for smart garden plantation condition monitoring, smart agriculture that makes use of cloud computing and IoT, and much more.
The book covers artificial intelligence in agriculture, robotic-based innovations in agriculture, intelligent computing in agriculture, and the Internet of Things in agriculture, providing a rich resource on this exciting and developing area.
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