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Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China
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Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Sensors 2016, 16(2), 173; https://doi.org/10.3390/s16020173
Received: 1 December 2015 / Revised: 16 January 2016 / Accepted: 26 January 2016 / Published: 29 January 2016
(This article belongs to the Section Physical Sensors)
Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. View Full-Text
Keywords: tomato recognition; robotic harvesting; low cost; image fusion; multiple feature images tomato recognition; robotic harvesting; low cost; image fusion; multiple feature images
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MDPI and ACS Style

Zhao, Y.; Gong, L.; Huang, Y.; Liu, C. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion. Sensors 2016, 16, 173. https://doi.org/10.3390/s16020173

AMA Style

Zhao Y, Gong L, Huang Y, Liu C. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion. Sensors. 2016; 16(2):173. https://doi.org/10.3390/s16020173

Chicago/Turabian Style

Zhao, Yuanshen; Gong, Liang; Huang, Yixiang; Liu, Chengliang. 2016. "Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion" Sensors 16, no. 2: 173. https://doi.org/10.3390/s16020173

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