Intelligent Data Mining and Fusion Systems in Agriculture - Chapter 1: Sensors in agriculture

Elsevier, Intelligent Data Mining and Fusion Systems in Agriculture, 2020, Pages 1-15
Xanthoula Eirini Pantazi, Dimitrios Moshou, and Dionysis Bochtis

According to the current trends of increased sustainability concerns in production systems, there is a high need for the targeted audience to become aware of the connection between decision making in agricultural operations and the decision support features that are offered by advanced computational intelligence algorithms combined with sensor fusion from a variety of sensors that are capable of providing a better view for crop condition and simultaneously lay the foundation for efficient crop management in agriculture. The main objective of the current book is to present methods of Computational Intelligence and Data Fusion with application in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. The presented methods are related to the combination of sensors with Artificial Intelligence architectures in Precision Agriculture. The Artificial Intelligence algorithms included Bio-inspired Hierarchical Neural Maps and Novelty Detection algorithms capable of detecting sudden changes in different conditions.