Automated assessment of plant diseases and traits by sensors: how can digital technologies support smart farming and plant breeding?

Description

Plant diseases pose a significant threat to agriculture. Precise and appropriately timed detection and identification of plant diseases is crucial for disease management, and for the selection of resistant and tolerant varieties. The detection of plant diseases by the human eye is dependent on the experience of the expert and on external influences such as environmental conditions. New sensor systems, artificial intelligence, and robotics – summarised under the term “digital technologies" – can help make complex assessments more efficient. There has been immense progress in the field of digital plant disease detection in the last two decades, through interdisciplinary research and technological advances. Understanding plant-pathogen interactions and visualising the underlying biochemical and biophysical processes with optical sensors enables plant disease detection and characterization. These innovative digital tools contribute to an objective and automated assessment of crop traits and are helping shape the future of smart farming and plant phenotyping.?

Table of contents

1 Introduction 2 Digital plant disease detection 3 Complexity of host–pathogen interactions 4 Complexity in a crop stand 5 Case study: application of deep learning to foliar plant diseases 6 Summary 7 Future trends in research 8 Where to look for further information 9 Acknowledgement 10 References