Using machine learning to identify and diagnose crop disease

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Crop diseases can cause major yield losses and the ability to detect and identify them in their early stages is important for disease control. Machine learning methods, in particular deep learning, have shown promise in classifying multiple diseases across many different crop types. In this chapter we give an introduction to how deep learning for image analysis and classification works and explain the requirements for collecting a dataset of plant disease images for use with deep learning networks. We discuss the results and successes of various previous studies and highlight pitfalls with individual methods. It is clear that deep learning is capable of handling complex disease classification problems where one disease is present. There is plenty of room for growth to work with the presence of multiple diseases in a single image or to quantify the amount of disease present.

Table of contents
1 Introduction
2 A quick introduction to deep learning
3 Preparation of data for deep learning experiments
4 Crop disease classification
5 Different visualisation techniques
6 Hyperspectral imaging for early disease detection
7 Case study: identification and classification of diseases on wheat
8 Conclusion and future trends
9 Where to look for more information
10 References
Original languageEnglish
Title of host publicationAdvances in sensor technology for sustainable crop production
EditorsCraig Lobsey, Asim Biswas
PublisherBurleigh Dodds Science Publishing
Number of pages22
ISBN (Electronic)9781786769800, 9781786769794
ISBN (Print)9781786769770, 9781801465045
Publication statusPublished - 21 Feb 2023
Externally publishedYes

Publication series

NameBurleigh Dodds Series in Agricultural Science
PublisherBurleigh Dodds Science Publishing Limited
ISSN (Print)2059-6936
ISSN (Electronic)2059-6944

Cite this