Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Chapter 1: Sensors in agriculture - p. iv 6. Chapter 2: Artificial intelligence in agriculture - p. 15 7. Chapter 3: Utilization of multisensors and data fusion in precision agriculture - p. 101 8. Chapter 4: Tutorial I: Weed detection - p. 172 9. Chapter 5: Tutorial II: Disease detection with fusion techniques - p. 197 10. Chapter 6: Tutorial III: Disease and nutrient stress detection - p. 221 11. Chapter 7: Tutorial IV: Leaf disease recognition - p. 239 12. Chapter 8: Tutorial V: Yield prediction - p. 258 13. Chapter 9: Tutorial VI: Postharvest phenotyping - p. 273 14. Chapter 10: General overview of the proposed data mining and fusion techniques in agriculture - p. 285 15. Index - p. 315
Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms.
1. Cover image - p. iv 2. Title page - p. iv 3. Table of Contents - p. iv 4. Copyright - p. iv 5. Chapter 1: Sensors in agriculture - p. iv 6. Chapter 2: Artificial intelligence in agriculture - p. 15 7. Chapter 3: Utilization of multisensors and data fusion in precision agriculture - p. 101 8. Chapter 4: Tutorial I: Weed detection - p. 172 9. Chapter 5: Tutorial II: Disease detection with fusion techniques - p. 197 10. Chapter 6: Tutorial III: Disease and nutrient stress detection - p. 221 11. Chapter 7: Tutorial IV: Leaf disease recognition - p. 239 12. Chapter 8: Tutorial V: Yield prediction - p. 258 13. Chapter 9: Tutorial VI: Postharvest phenotyping - p. 273 14. Chapter 10: General overview of the proposed data mining and fusion techniques in agriculture - p. 285 15. Index - p. 315
