期刊
BIOSYSTEMS ENGINEERING
卷 175, 期 -, 页码 156-167出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2018.09.004
关键词
Fruit detection and maturity identification; DSIFT; Locality-constrained linear coding; Faster R-CNN
资金
- Science and Technology Planning Project of Guangdong Province [2015A020209148, 2015A020224038, 2014A020208108, 2016A020210087]
- Guangzhou Municipal Science and Technology Planning Project [201605030013, 201604016122]
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. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.
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