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Sensors 2016, 16(12), 2098; doi:10.3390/s16122098

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components

1
Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China
2
College of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
3
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Manuela Vieira
Received: 26 September 2016 / Revised: 12 November 2016 / Accepted: 28 November 2016 / Published: 10 December 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [14652 KB, uploaded 10 December 2016]   |  

Abstract

The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination. View Full-Text
Keywords: grape detection; AdaBoost classifier; color components; harvesting robot grape detection; AdaBoost classifier; color components; harvesting robot
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Luo, L.; Tang, Y.; Zou, X.; Wang, C.; Zhang, P.; Feng, W. Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components. Sensors 2016, 16, 2098.

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