| Title |
Machine learning prediction of coffee rust severity on leaves using spectroradiometer data
|
|---|---|
| Published in |
Tropical Plant Pathology, September 2017
|
| DOI | 10.1007/s40858-017-0187-8 |
| Authors |
Abel Chemura, Onisimo Mutanga, Mbulisi Sibanda, Pardon Chidoko |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 33% |
| Brazil | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 111 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 14% |
| Student > Master | 13 | 12% |
| Student > Bachelor | 12 | 11% |
| Student > Ph. D. Student | 9 | 8% |
| Other | 6 | 5% |
| Other | 21 | 19% |
| Unknown | 34 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 17% |
| Engineering | 17 | 15% |
| Computer Science | 14 | 13% |
| Environmental Science | 5 | 5% |
| Earth and Planetary Sciences | 3 | 3% |
| Other | 8 | 7% |
| Unknown | 45 | 41% |