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Full Description
Smart Agriculture: Concepts, Strategies, and Case Studies showcases improvements in agricultural technology, bringing together work from leading experts in the field. It explores techniques and disciplines essential for the widespread adoption of precision agriculture, including artificial intelligence (AI), machine learning, the Internet of Things (IoT), Agriculture 5.0, smart spraying systems, and decision support tools that are transforming modern farming practices.
Designed to introduce readers to the vast potential of data-driven agriculture, this book presents information on smart agriculture through practical, real-world applications. It features a diverse range of case studies across various crops and agricultural operations, offering actionable insights into innovative solutions. Highlights include variable-rate orchard spraying for high-density canopies like olives and oranges, image analysis for evaluating apple and grape properties, digital tools for optimizing irrigation, geostatistical modelling of Xylella fastidiosa, UAV-assisted fertilization of olive trees, automated wheat growth monitoring, smart agriculture techniques for peach production, greenhouse data management, digital twins for strawberry cultivation, deep learning for obstacle detection in robotic rice harvesters, and IoT middleware solutions for arable crops and livestock.
A book in the NextGen Agriculture: Novel Concepts & Innovative Strategies series, this comprehensive resource is ideal for professional engineers, researchers in agricultural engineering, computer science, machine vision, and remote sensing, as well as technology developers. Graduate students specializing in digital agriculture and precision farming will also find this book invaluable for understanding the transformative power of modern agricultural technologies.
Contents
Chapter 1. Variable-rate spraying for sustainable citrus and grape production
Verónica Saiz-Rubio, Francisco Rovira-Más, and Carla Román-Rochina
Chapter 2. Image analysis techniques for fruit assessment in apples and grapes
Julie Tang, Annie Xu Wang, and Mark Whitty
Chapter 3. Chapter 3 Digital tools in the context of Agriculture 4.0 for integrated management of orchard crops
Stanley Best, José Maldonado, Melany Navarro, and Valentina De la Barrera
Chapter 4. Geostatistical modelling of multispectral data for monitoring Xylella fastidiosa in olive groves.
Antonella Belmonte, Gabriele Buttafuoco, and Annamaria Castrignanò
Chapter 5. UAV remote sensing for fertilization rating in olive groves.
Eliseo Roma and Pietro Catania
Chapter 6. Smart Agriculture for Peach Orchard Management
Irene Borra-Serrano, José Manuel Peña, Joe M. Maja, Juan Carlos Melgar, Kelly Nascimento-Silva, and Ana de Castro
Chapter 7. Decision-support systems for greenhouse horticulture.
Lucio Colizzi, Emanuela Guerriero, Tommaso Adamo, and Nunzia Lomonte
Chapter 8. Smart Sensing and Digital twin technology for yield estimation and phenomics prediction in strawberries
Daeun Choi, Xu Wang, and Omeed Mirbod
Chapter 9. Remote Sensing for Automatic Disease Monitoring in Wheat
Orly Enrique Apolo-Apolo, Luis Sánchez-Fernández, Simon Appeltans, Gregorio Egea, and Manuel Perez-Ruiz
Chapter 10. Deep learning object detection for robotic harvesters in rice
Jiajun Zhu, Yang Li, and Michihisa Iida
Chapter 11. IoT Middleware solutions for arable crops and livestock farming
Eleni Symeonaki, Konstantinos G. Arvanitis, Chrysanthos Maraveas, Dimitrios Loukatos, and Spyros Fountas
