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Detection process of single ripe tomato on plant combining Faster R-CNN and intuitionistic fuzzy set.
Abstract: Fast and accurate detection of ripe tomatoes on plant, which replaces manual labor with a robotic vision-based harvesting system, is a challenging task. Tomatoes in adjac... View more
Fast and accurate detection of ripe tomatoes on plant, which replaces manual labor with a robotic vision-based harvesting system, is a challenging task. Tomatoes in adjacent positions are easily mistaken as a single tomato by image recognition methods. In this study, a ripe tomato detection method that combines deep learning with edge contour detection is proposed. Our approach efficiently separates target tomatoes from overlapping tomatoes to detect individual fruits. This approach yields several improvements. First, deep learning requires less time and extracts deeper features than traditional methods for assessing candidate ripe tomato regions. Second, we use Gaussian density function of H and S in the HSV color space to help segment tomato regions from the background, followed by erosion and dilation on the tomato body to separate adjacent tomatoes and remove peripheral subpixels from all detected ripe tomatoes. Third, an adaptive threshold intuitionistic fuzzy set (IFS) method was developed to identify the tomato's edge, and it performs well in detecting blurred edges in overlapping regions. To improve the efficiency and stability of edge detection under natural conditions, we adopted an illumination adjustment algorithm for the tomato image before edge detection. As samples, we collected images showing tomatoes that were separated, adjacent, overlapped, and even shaded by leaves. The widths and heights of these tomato samples were calculated and analyzed to evaluate the detection performance of the proposed method. The root mean square error (RMSE) results for tomato width and height using the proposed method are 2.996 pixels and 3.306 pixels, respectively. The mean relative error percent (MRE%) values for horizontal and vertical center position shift are 0.261% and 1.179%, respectively. These results demonstrate that the proposed method improves tomato detection accuracy and that it can be further applied in the harvesting process of agricultural robots.
Published in: IEEE Access ( Volume: 7 )
Date of Publication: 24 October 2019
Detection process of single ripe tomato on plant combining Faster R-CNN and intuitionistic fuzzy set.
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Can J Vet Res
v.74(4); 2010 Oct
PMC2949343
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Can J Vet Res. 2010 Oct; 74(4): 299–304.
Equine Performance Testing Clinic, Department of Veterinary Clinical Sciences and Cornell University Hospital for Animals, Cornell University College of Veterinary Medicine, Ithaca, New York 14850, USA
Address all correspondence to Dr. Thomas Witte; telephone: +44 1707 666818; fax: +44 1707 666304; e-mail: ku.ca.cvr@ettiwt
Dr. Witte’s current address is The Royal Veterinary College, University of London, Hawkshead Lane, Hatfield, Hertfordshire AL9 7TA, United Kingdom.
Received 2009 Aug 5; Accepted 2009 Dec 2.
Copyright and/or publishing rights held by the Canadian Veterinary Medical Association
1. Kidd JA, Slone DE. Treatment of laryngeal hemiplegia in horses by prosthetic laryngoplasty, ventriculectomy and vocal cordectomy. Vet Rec. 2002; 150 :481–484. [ PubMed ] [ Google Scholar ]
2. Hawkins JF, Tulleners EP, Ross MW, E
Присунул красотке из России
Галерея 3052753
Замечательная девка у которой огромный хуй хвастается перед камерой