| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A phytopathometry glossary for the twenty-first century: towards consistency and precision in intra- and inter-disciplinary dialogues
|
Bock, Clive H.
|
|
|
47 |
1 |
p. 14-24
|
artikel
|
| 2 |
Artificial intelligence for spectral classification to identify the basal stem rot disease in oil palm using dielectric spectroscopy measurements
|
Khaled, Alfadhl Yahya
|
|
|
47 |
1 |
p. 140-151
|
artikel
|
| 3 |
A special issue on phytopathometry — visual assessment, remote sensing, and artificial intelligence in the twenty-first century
|
Bock, Clive H.
|
|
|
47 |
1 |
p. 1-4
|
artikel
|
| 4 |
Deep learning applied to plant pathology: the problem of data representativeness
|
Barbedo, Jayme G. A.
|
|
|
47 |
1 |
p. 85-94
|
artikel
|
| 5 |
Forrest W. Nutter, Jr.: a career in phytopathometry
|
Madden, Laurence V.
|
|
|
47 |
1 |
p. 5-13
|
artikel
|
| 6 |
How much do standard area diagrams improve accuracy of visual estimates of the percentage area diseased? A systematic review and meta-analysis
|
Del Ponte, Emerson M.
|
|
|
47 |
1 |
p. 43-57
|
artikel
|
| 7 |
Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools
|
León-Rueda, William A.
|
|
|
47 |
1 |
p. 152-167
|
artikel
|
| 8 |
Insights for improving bacterial blight management in coffee field using spatial big data and machine learning
|
de Carvalho Alves, Marcelo
|
|
|
47 |
1 |
p. 118-139
|
artikel
|
| 9 |
Measuring plant disease severity in R: introducing and evaluating the pliman package
|
Olivoto, Tiago
|
|
|
47 |
1 |
p. 95-104
|
artikel
|
| 10 |
Plant disease severity estimated visually: a century of research, best practices, and opportunities for improving methods and practices to maximize accuracy
|
Bock, Clive H.
|
|
|
47 |
1 |
p. 25-42
|
artikel
|
| 11 |
RGB-based phenotyping of foliar disease severity under controlled conditions
|
Alves, Kaique S.
|
|
|
47 |
1 |
p. 105-117
|
artikel
|
| 12 |
Sensing and imaging of plant disease through the lens of science mapping
|
Ruwona, Justice
|
|
|
47 |
1 |
p. 74-84
|
artikel
|
| 13 |
Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology
|
Chiang, Kuo-Szu
|
|
|
47 |
1 |
p. 58-73
|
artikel
|
| 14 |
Using computer vision to identify seed-borne fungi and other targets associated with common bean seeds based on red–green–blue spectral data
|
Pozza, Edson Ampélio
|
|
|
47 |
1 |
p. 168-185
|
artikel
|
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