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Sensors 2017, 17(12), 2738; https://doi.org/10.3390/s17122738

On-Tree Mango Fruit Size Estimation Using RGB-D Images

1
Centre for Intelligent Systems, Central Queensland University, Rockhampton, Queensland 4701, Australia
2
Institute for Future Farming Systems, Central Queensland University, Rockhampton, Queensland 4701, Australia
*
Author to whom correspondence should be addressed.
Received: 17 October 2017 / Revised: 15 November 2017 / Accepted: 25 November 2017 / Published: 28 November 2017
(This article belongs to the Special Issue Sensors in Agriculture and Forestry)
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Abstract

In-field mango fruit sizing is useful for estimation of fruit maturation and size distribution, informing the decision to harvest, harvest resourcing (e.g., tray insert sizes), and marketing. In-field machine vision imaging has been used for fruit count, but assessment of fruit size from images also requires estimation of camera-to-fruit distance. Low cost examples of three technologies for assessment of camera to fruit distance were assessed: a RGB-D (depth) camera, a stereo vision camera and a Time of Flight (ToF) laser rangefinder. The RGB-D camera was recommended on cost and performance, although it functioned poorly in direct sunlight. The RGB-D camera was calibrated, and depth information matched to the RGB image. To detect fruit, a cascade detection with histogram of oriented gradients (HOG) feature was used, then Otsu’s method, followed by color thresholding was applied in the CIE L*a*b* color space to remove background objects (leaves, branches etc.). A one-dimensional (1D) filter was developed to remove the fruit pedicles, and an ellipse fitting method employed to identify well-separated fruit. Finally, fruit lineal dimensions were calculated using the RGB-D depth information, fruit image size and the thin lens formula. A Root Mean Square Error (RMSE) = 4.9 and 4.3 mm was achieved for estimated fruit length and width, respectively, relative to manual measurement, for which repeated human measures were characterized by a standard deviation of 1.2 mm. In conclusion, the RGB-D method for rapid in-field mango fruit size estimation is practical in terms of cost and ease of use, but cannot be used in direct intense sunshine. We believe this work represents the first practical implementation of machine vision fruit sizing in field, with practicality gauged in terms of cost and simplicity of operation. View Full-Text
Keywords: allometry; fruit size; RGB-D camera; machine vision; precision fruiticulture; time of flight allometry; fruit size; RGB-D camera; machine vision; precision fruiticulture; time of flight
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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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Wang, Z.; Walsh, K.B.; Verma, B. On-Tree Mango Fruit Size Estimation Using RGB-D Images. Sensors 2017, 17, 2738.

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