| Title |
Digital image processing techniques for detecting, quantifying and classifying plant diseases
|
|---|---|
| Published in |
SpringerPlus, December 2013
|
| DOI | 10.1186/2193-1801-2-660 |
| Pubmed ID | |
| Authors |
Jayme Garcia Arnal Barbedo |
| Abstract |
This paper presents a survey on methods that use digital image processing techniques to detect, quantify and classify plant diseases from digital images in the visible spectrum. Although disease symptoms can manifest in any part of the plant, only methods that explore visible symptoms in leaves and stems were considered. This was done for two main reasons: to limit the length of the paper and because methods dealing with roots, seeds and fruits have some peculiarities that would warrant a specific survey. The selected proposals are divided into three classes according to their objective: detection, severity quantification, and classification. Each of those classes, in turn, are subdivided according to the main technical solution used in the algorithm. This paper is expected to be useful to researchers working both on vegetable pathology and pattern recognition, providing a comprehensive and accessible overview of this important field of research. |
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 | 2 | 67% |
| Kuwait | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 583 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Belgium | 2 | <1% |
| India | 2 | <1% |
| Malaysia | 1 | <1% |
| Indonesia | 1 | <1% |
| Ecuador | 1 | <1% |
| Bangladesh | 1 | <1% |
| Chile | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 573 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 105 | 18% |
| Student > Ph. D. Student | 75 | 13% |
| Student > Bachelor | 65 | 11% |
| Researcher | 54 | 9% |
| Student > Doctoral Student | 31 | 5% |
| Other | 94 | 16% |
| Unknown | 159 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 147 | 25% |
| Engineering | 105 | 18% |
| Agricultural and Biological Sciences | 93 | 16% |
| Environmental Science | 12 | 2% |
| Biochemistry, Genetics and Molecular Biology | 11 | 2% |
| Other | 41 | 7% |
| Unknown | 174 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 August 2020.
All research outputs
#2,578,370
of 22,733,113 outputs
Outputs from SpringerPlus
#153
of 1,853 outputs
Outputs of similar age
#31,162
of 306,585 outputs
Outputs of similar age from SpringerPlus
#7
of 82 outputs
Altmetric has tracked 22,733,113 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,853 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 306,585 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 82 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.