Title |
Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress
|
---|---|
Published in |
Plant Methods, October 2017
|
DOI | 10.1186/s13007-017-0233-z |
Pubmed ID | |
Authors |
Amy Lowe, Nicola Harrison, Andrew P French |
Abstract |
This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into 'healthy and diseased plant classification' with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 29% |
Germany | 1 | 14% |
Canada | 1 | 14% |
Turkey | 1 | 14% |
Colombia | 1 | 14% |
Unknown | 1 | 14% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 57% |
Members of the public | 2 | 29% |
Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 749 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 139 | 19% |
Student > Master | 106 | 14% |
Researcher | 98 | 13% |
Student > Bachelor | 57 | 8% |
Student > Doctoral Student | 33 | 4% |
Other | 96 | 13% |
Unknown | 220 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 196 | 26% |
Engineering | 91 | 12% |
Computer Science | 74 | 10% |
Environmental Science | 34 | 5% |
Earth and Planetary Sciences | 20 | 3% |
Other | 74 | 10% |
Unknown | 260 | 35% |