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Advances in image-based identification and analysis of crop insect pests

Code: 9781801468336
Publication date: 24/04/2023
Extent: 18 pages

Contributions by: Daniel Guyer, Michigan State University, USA; and Charles Whitfield, NIAB, UK

Chapter synopsis:

This chapter reviews progress in developing automated image-based systems for identifying crop insect pests. It identifies the challenges in distinguishing insect pests in field conditions and ways they can be addressed. The chapter outlines key steps in image-based identification (image capture, processing, segmentation, feature extraction and classification) and the growing use of artificial intelligence to increase accuracy and reliability. It also provides examples of commercially-available systems and assesses future developments. The chapter is aimed at practitioners and scientists new to the topic and as a useful reference on the pros and cons of different monitoring strategies for those already in the field.



DOI: 10.19103/AS.2022.0113.08
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Table of contents
  • 1 Introduction
  • 2 Challenges and solutions in automated image-based insect identification
  • 3 Understanding machine vision image-based insect identification
  • 4 Automated and semi-automated image-based insect identification technologies
  • 5 Commercially available systems
  • 6 Conclusion
  • 7 References

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