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Detection of passion fruits and maturity classification using Red-Green-Blue Depth images
- Source :
- Biosystems Engineering. 175:156-167
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- A machine vision algorithm was developed to detect passion fruits and identify maturity of the detected fruits using natural outdoor RGB-D images. As different passion fruits on the same branch can be in different maturity stages, detection and maturity classification on a complex background are very important for yield mapping and development of intelligent mobile fruit-picking robots. In this study, a Kinect sensor was used for data acquisition, and maturity stages of the fruits were divided into five categories: young (Y), near-young (NY), near-mature (NM), mature (M) and after-mature (AM). The algorithm involved two stages. First, by colour and depth images, passion fruits were detected using faster region-based convolutional neural networks (Faster R-CNN), and colour-based detection was integrated with depth-based detection for improving detection performance. Second, for each detected fruit region, the dense scale invariant features transform (DSIFT) algorithm combined with locality-constrained linear coding (LLC) was used to extract and represent the features of fruit maturity from R, G, and B channels, respectively. In addition, the RGB-DSIFT-LLC features were input into a linear support vector machine (SVM) classifier for identifying the maturity of fruits. By conducting an experimental study on a special dataset, we verified that the proposed method achieves 92.71% detection accuracy and 91.52% maturity classification accuracy.
- Subjects :
- Artificial neural network
business.industry
Machine vision
Soil Science
Pattern recognition
04 agricultural and veterinary sciences
02 engineering and technology
Two stages
Convolutional neural network
Yield mapping
Support vector machine
Control and Systems Engineering
040103 agronomy & agriculture
0202 electrical engineering, electronic engineering, information engineering
0401 agriculture, forestry, and fisheries
Detection performance
RGB color model
020201 artificial intelligence & image processing
Artificial intelligence
business
Agronomy and Crop Science
Food Science
Mathematics
Subjects
Details
- ISSN :
- 15375110
- Volume :
- 175
- Database :
- OpenAIRE
- Journal :
- Biosystems Engineering
- Accession number :
- edsair.doi...........2a484745d5d0ef18445eaaf70ce27234
- Full Text :
- https://doi.org/10.1016/j.biosystemseng.2018.09.004