Hello guest
Your basket is empty
We provide two pathways to the content. Thematic (chapters that address certain themes, e.g. cultivation, regardless of crop or animal type) and Product (chapters that relate to a specific type of crop or animal). Choose the most applicable route to find the right collection for you. 
 
Can’t find what you are looking for? Contact us and let us help you build a custom-made collection. 
You are in: All categories > A-Z Chapters > O
Use the Contact form to discuss the best purchasing method for you... Start building your collection today!

Online decision support systems, remote sensing and artificial intelligence applications for wheat pest management

Code: 9781801468589
Daniel J. Leybourne, Leibniz University Hannover, Germany and RSK ADAS Ltd, UK; Mark Ramsden and Sacha White, RSK ADAS Ltd, UK; Rujing Wang, He Huang and Chengjun Xe, Institute of Intelligent Machines, Chinese Academy of Sciences, China; and Po Yang, University of Sheffield, UK

Chapter synopsis:

Infestation with herbivorous insects and other invertebrates (“pests”) can be extremely damaging to wheat production, potentially resulting in up to 80% yield loss. Reducing the damage caused by these pests is a central component of crop protection practices. Decision Support Systems (DSS) are interactive systems (usually software based) that help users identify and solve problems and make decisions as part of an IPM strategy. DSS play an important role in pest management, especially in relation to treatment application. DSS can be used as an umbrella term for any software-based support system that helps farmers make management and production decisions. The chapter will focus on providing an overview of DSS targeting the primary wheat pests covered in this book, we will cover both forecasting and prediction DSS available for wheat pest management as well as remote sensing and AI tools available for pest detection.



DOI: 10.19103/AS.2022.0114.21
£25.00
Table of contents
  • 1 Introduction
  • 2 Pest forecasting, observation and support tools for wheat pest management
  • 3 Remote sensing and artificial intelligence image analysis to inform pest identification and promote decision-making
  • 4 The uptake of digital support systems amongst farmers
  • 5 Conclusion and future trends in research
  • 6 Where to look for further information
  • 7 Funding
  • 8 References

Also in O