Vineyard Yield Estimation Based on the Analysis of High Resolution Images Obtained with Artificial Illumination at Night

Sensors

Davinia Font²  Marcel Tresanchez²  Dani Martínez²  Javier Moreno²  Eduard Clotet²  Jordi Palacín¹ 

[1] Department of Computer Science and Industrial Engineering, Universitat de Lleida, Jaume II, 69, 25001 Lleida, Spain;

关键词: precision agriculture;    yield estimation;    segmentation techniques;    color features;   

DOI  :  10.3390/s150408284

来源: mdpi

【 摘 要 】

This paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear color model segmentation and histogram segmentation, in order to obtain the best estimation of the area of the clusters of grapes in this illumination conditions. The color spaces tested were the original RGB and the Hue-Saturation-Value (HSV). The best segmentation method in the case of a non-occluded reddish table-grape variety was the threshold segmentation applied to the H layer, with an estimation error in the area of 13.55%, improved up to 10.01% by morphological filtering. Secondly, after segmentation, two procedures for yield estimation based on a previous calibration procedure have been proposed: (1) the number of pixels corresponding to a cluster of grapes is computed and converted directly into a yield estimate; and (2) the area of a cluster of grapes is converted into a volume by means of a solid of revolution, and this volume is converted into a yield estimate; the yield errors obtained were 16% and −17%, respectively.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.