MATEC Web of Conferences
Volume 31, 20152015 7th International Conference on Mechanical and Electronics Engineering (ICMEE 2015)
|Number of page(s)||5|
|Section||Image processing and application|
|Published online||23 November 2015|
Potato Size and Shape Detection Using Machine Vision
School of Information Science and Technology, Hunan Agricultural University, Changsha, 410128, China
a Corresponding author: email@example.com
To reduce the error and faster classification by mechanizing in classifying the potato shape and size through machine vision using the extraction of characters procedure to identify the size, and using the shape detection procedure to identify the shape. Test results in potato size detection revealed 40/191 = 0.210mm/pixel as length scale or calibration factor (40/M) where 40 is the table tennis ball size (40mm) and 191 as image pixels table tennis (M); measurement results revealed that between the algorithm results and the manual measurements, the absolute error was <3mm, while the relative error rate was <4%; and the measurement results based on the ellipse axis length can accurately calculate the actual long axis and short axis of potato. Potato shape detection revealed the analysis of 228 images composed of 114 positive and 114 negatives side, only 2 have been incorrectly classified, mainly because the Extracted ratio (R) of the potato image of those two positive and negative images are near 0.67, respectively 0.671887, 0.661063, 0.667604, and 0.67193. The comparison to establish a calibration system method using both basic rectangle and ellipse R ratio methods to detect the potato size and shape, revealed that the basic rectangle method has better effect in the case of fixed place. Moreover, the ellipse axis method was observed to be more stable with an error rate of 7%. Therefore it is recommended that the ellipse axis method should be used to detect the shape of potato for differentiation into round, long cylindrical, and oval shapes, with the accuracy level of 98.8%.
© Owned by the authors, published by EDP Sciences, 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.