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1 | 1 | #' Rice Dataset Commeo and Osmancik |
2 | 2 | #' |
3 | | -#' A total of 3810 rice grain's images were taken for the two species (Cammeo and Osmancik), |
4 | | -#' processed and feature inferences were made. Seven morphological features were obtained for each grain of rice. |
| 3 | +#' @description |
| 4 | +#' A total of 3810 images of rice grains were taken for two varieties (Cammeo |
| 5 | +#' and Osmancik). The images are then processed and feature were extracted in a |
| 6 | +#' table. Seven morphological features were obtained for each grain of rice. |
5 | 7 | #' |
6 | 8 | #' @format A data frame with 8 variables and 3810 observations: |
7 | 9 | #' \describe{ |
8 | | -#' \item{\code{area}}{The number of pixels within the boundaries of the rice grain.} |
| 10 | +#' \item{\code{area}}{The number of pixels within the boundaries of the |
| 11 | +#' rice grain.} |
9 | 12 | #' \item{\code{perimeter}}{The perimeter of the rice grain.} |
10 | | -#' \item{\code{major_axis_length}}{The longest line that can be drawn on the rice grain.} |
11 | | -#' \item{\code{minor_axis_length}}{The shortest line that can be drawn on the rice grain.} |
12 | | -#' \item{\code{eccentricity}}{It measures how round the ellipse, which has the same moments as the rice grain, is.} |
13 | | -#' \item{\code{convex_area}}{The the pixel count of the smallest convex shell of the region formed by the rice grain.} |
14 | | -#' \item{\code{extent}}{the ratio of the region formed by the rice grain to the bounding box pixels.} |
15 | | -#' \item{\code{class}}{A **factor** with two levels: `"Cammeo"`, and `"Osmancik"`.} |
| 13 | +#' \item{\code{major_axis_length}}{The longest line that can be drawn on the |
| 14 | +#' rice grain.} |
| 15 | +#' \item{\code{minor_axis_length}}{The shortest line that can be drawn on the |
| 16 | +#' rice grain.} |
| 17 | +#' \item{\code{eccentricity}}{It measures how round the ellipse, which has |
| 18 | +#' the same moments as the rice grain.} |
| 19 | +#' \item{\code{convex_area}}{The pixel count of the smallest convex hull of |
| 20 | +#' the region formed by the rice grain.} |
| 21 | +#' \item{\code{extent}}{the ratio of the region formed by the rice grain to |
| 22 | +#' the bounding box pixels.} |
| 23 | +#' \item{\code{class}}{A **factor** with two levels: `"Cammeo"`, and |
| 24 | +#' `"Osmancik"`.} |
16 | 25 | #' } |
17 | | -#' @source {Cinar, I. and Koklu, M. (2019). Classification of Rice Varieties Using Artificial Intelligence Methods. International Journal of Intelligent Systems and Applications in Engineering, vol.7, no.3 (Sep. 2019), pp.188-194. doi:10.18201/ijisae.2019355381} |
| 26 | +#' @source {Cinar, I. and Koklu, M. (2019). Classification of Rice Varieties |
| 27 | +#' Using Artificial Intelligence Methods. International Journal of Intelligent |
| 28 | +#' Systems and Applications in Engineering, vol.7, no.3 (Sep. 2019), |
| 29 | +#' pp.188-194. doi:10.18201/ijisae.2019355381} |
18 | 30 | "rice" |
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