Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art

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Abstract

Plant disease detection represents a tremendous challenge for research and practical applications. Visual assessment by human raters is time-consuming, expensive, and error prone. Disease rating and plant protection need new and innovative techniques to address forthcoming challenges and trends in agricultural production that require more precision than ever before. Within this context, hyperspectral sensors and imaging techniques—intrinsically tied to efficient data analysis approaches—have shown an enormous potential to provide new insights into plant-pathogen interactions and for the detection of plant diseases. This article provides an overview of hyperspectral sensors and imaging technologies for assessing compatible and incompatible plant-pathogen interactions. Within the progress of digital technologies, the vision, which is increasingly discussed in the society and industry, includes smart and intuitive solutions for assessing plant features in plant phenotyping or for making decisions on plant protection measures in the context of precision agriculture.

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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

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Author and article information

Journal

Title: Annual Review of Phytopathology

Abbreviated Title: Annu. Rev. Phytopathol.

Publisher: Annual Reviews

ISSN (Print): 0066-4286

ISSN (Electronic): 1545-2107

Publication date Created: August 25 2018

Publication date (Print): August 25 2018

Volume: 56

Issue: 1

Pages: 535-558

Affiliations

[1 ]Institute of Sugar Beet Research (IfZ), 37079 Göttingen, Germany;

[2 ]Institute of Crop Science and Resource Conservation (INRES)–Plant Diseases and Plant Protection, University of Bonn, 53115 Bonn, Germany

[3 ]Greenhouse Horticulture, Wageningen University and Research, 6708PB Wageningen, Netherlands

[4 ]Institute of Agricultural Sciences, ETH Zürich, 8092 Zürich, Switzerland

Article

DOI: 10.1146/annurev-phyto-080417-050100

PubMed ID: 30149790

SO-VID: c941db3b-7901-4559-9adb-c547273cc1c5

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