Machine Learning for Image Analysis: Leaf Disease Segmentation

Monica Furaste Danilevicz, Philipp Emanuel Bayer

Research output: Chapter in Book/Conference paperChapterpeer-review

1 Citation (Scopus)

Abstract

Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, generating massive amounts of data, which require an automated analysis to extract meaningful information. Deep learning is a branch of machine learning that has revolutionized many fields of research. Deep learning models are able to extract autonomously the underlying features within the dataset, providing a multi-level representation of the data. Our intention is to show the feasibility and effectiveness of using deep learning and low-cost technology for automated phenotyping. In this methods chapter, we describe how to train a deep neural network to segment leaf images and extract the pixels related to the disease.

Original languageEnglish
Title of host publicationPlant Bioinformatics
Subtitle of host publicationMethods and Protocols
EditorsDavid Edwards
Pages429-449
Number of pages21
Edition3
ISBN (Electronic)978-1-0716-2067-0
DOIs
Publication statusPublished - 2022

Publication series

NameMethods in Molecular Biology
Volume2443
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

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