This volume reviews key advances in precision agriculture technology and applications. Chapters summarise developments in monitoring techniques, including proximal crop and soil sensors and remote sensing technologies. The book then goes on to discuss how this information is processed to identify management zones and input targets. Chapters also assesses advances in delivery mechanisms such as variable rate application and targeted spray technologies. The final part of the book surveys the wide range of applications of precision agriculture, from controlled traffic farming to site-specific nutrient and water management. With its distinguished editor and international team of subject experts, this will be a standard reference for crop scientists and agronomists as well as all those concerned with improving the efficiency and sustainability of agriculture.

This collection features four peer-reviewed reviews on proximal sensors in agriculture. The first chapter addresses the use of proximal sensors to evaluate crop health and performance throughout the growing season. The chapter reviews the evolution of crop sensors, as well as the issues and limitations facing further development, including the need to develop sensors equipped with the ability to detect stresses other than nitrogen. The second chapter reviews recent advances in using proximal sensors to detect crop health status in horticultural crops. The chapter considers the application of sensors to detect micro-environmental parameters linked to pathogen lifecycles which can then be utilised to predict disease risk. The third chapter reviews advances in using proximal spectroscopic sensors to assess soil health. It assesses principles and technologies, key properties measured, advantages and disadvantages together with applications in improving soil management. The final chapter discusses advances in the use of proximal sensor fusion and multi-sensor platforms for improved crop management. The chapter considers the combination of remote sensing from satellites and weather station data as the basis for crop growth models and explores the benefits of utilising a selection of tools to investigate yield prediction.